Employee Churn Prediction using Logistic Regression and Support Vector Machine

It is a challenge for Human Resource (HR) team to retain their existing employees than to hire a new one. For any company, losing their valuable employees is a loss in terms of time, money, productivity, and trust, etc. This loss could be possibly minimized if HR could beforehand find out their potential employees who are planning to quit their job hence, we investigated solving the employee churn problem through the machine learning perspective. We have designed machine learning models using supervised and classification-based algorithms like Logistic Regression and Support Vector Machine (SVM). The models are trained with the IBM HR employee dataset retrieved from https://kaggle.com and later fine-tuned to boost the performance of the models. Metrics such as precision, recall, confusion matrix, AUC, ROC curve were used to compare the performance of the models. The Logistic Regression model recorded an accuracy of 0.67, Sensitivity of 0.65, Specificity of 0.70, Type I Error of 0.30, Type II Error of 0.35, and AUC score of 0.73 where as SVM achieved an accuracy of 0.93 with Sensitivity of 0.98, Specificity of 0.88, Type I Error of 0.12, Type II Error of 0.01 and AUC score of 0.96

Physical Education

Class 9 & 10 (Nepali Date: 2041); Language: Nepal

Dialogue act classification in human-to-human tutorial dialogues

We present in this paper preliminary results with dialogue act classification in human-to-human tutorial dialogues. Dialogue acts are ways to characterize the actions of tutors and students based on the language-as-action theory. This work serves our larger goal of identifying patterns of tutors’ actions, in the form of dialogue acts, that relate to learning. The preliminary results we obtained for dialogue act classification using a machine learning approach are promising

Bacteriological Profile and Drug Susceptibility in Mucosal type Chronic Suppurative Otitis Media

Background: In Chronic Suppurative Otitis Media, mucosal type, most common organisms are Pseudomonas aeruginosa and Proteus species (P. mirabilis and P. vulgaris). It is important to prescribe culture-directed antibiotics to prevent resistance. This study was conducted to determine the bacteriological profile and drug susceptibility in patient with chronic suppurative otitis media. Methods: This is a hospital-based descriptive study done at Gandaki Medical College, Pokhara, Nepal from July 2019 to June 2020. Under aseptic condition, the swab specimens were obtained from patients with history of ear discharge of >12 weeks duration and findings central perforation of the tympanic membrane. The sample was labeled and immediately transferred to the microbiology lab for culture/sensitivity test according to the guidelines of the Clinical and Laboratory Standards Institute. Results: Out of total 127 patients, 48 (37.8%) were male and 79 (62.2%) were female. One hundred and seven samples (84.3%) had positive culture while 20 samples (15.7%) had no growth. Staphylococcus aureus (43%), was the most common isolate followed by Pseudomonas aeruginosa (23.4%), Proteus mirabilis (9.3%), and Escherichia coli (8.4%). All the organisms isolated were 100% sensitive to imipenem followed by 96.2% sensitive to gentamicin and 95.3% to amikacin. Conclusions: Staphylococcus aureus (43%) was the most predominant isolate followed by Pseudomonas aeruginosa (23.4%), Proteus mirabilis (9.3%), and Escherichia coli (8.4%). Imipenem was the most sensitive antibiotic (100%) followed by gentamicin (96.2%), amikacin (95.3%), and ofloxacin (88.78%). Keywords: Antibiotic susceptibility; bacteriology; chronic suppurative otitis medi

Automated assessment of open-ended student answers in tutorial dialogues using Gaussian Mixture Models

Open-ended student answers often need to be assessed in context. However, there are not many previous works that consider context when automatically assessing student answers. Furthermore, student responses vary significantly in their explicit content and writing style which leads to a wide range of assessment scores for the same qualitative assessment category, e.g. correct answers vs. incorrect answers. In this paper, we propose an approach to assessing student answers that takes context into account and which handles variability using probabilistic Gaussian Mixture Models (GMMs). We developed the model using a recently released corpus called DT-Grade which was manually annotated, taking context into account, with four different levels of answer correctness. Our best GMM model outperforms the baseline model with a margin of 9% in terms of accuracy

Handling missing words by mapping across word vector representations

Vector based word representation models are often developed from very large corpora. However, we often encounter words in real world applications that are not available in a single vector model. In this paper, we present a novel Neural Network (NN) based approach for obtaining representations for words in a target model from another model, called the source model, where representations for the words are available, effectively pooling together their vocabularies. Our experiments show that the transformed vectors are well correlated with the native target model representations and that an extrinsic evaluation based on a word-to-word similarity task using the Simlex-999 dataset leads to results close to those obtained using native model representations

DTSim at SemEval-2016 Task 1: Semantic similarity model including multi-level alignment and vector-based compositional semantics

In this paper we describe our system (DT-Sim) submitted at SemEval-2016 Task 1: Semantic Textual Similarity (STS Core). We developed Support Vector Regression model with various features including the similarity scores calculated using alignment based methods and semantic composition based methods. The correlations between our system output and the human ratings were above 0.8 in three datasets

Automated labelling of dialogue modes in tutorial dialogues

We present in this paper a study whose goal was to automatically label higher level constructs, called dialogue modes, in tutorial dialogues. Each tutorial dialogue is regarded as a sequence of utterances articulated by either the learner or the tutor. The dialogue utterances can be grouped into dialogue modes which correspond to general conversational phases such as dialogue openings, e.g. when the conversational partners greet each other, or serve specific pedagogical purposes, e.g. a scaffolding students\u27 problem solving process. Detecting dialogue modes is important because they can be used as an instrument to understand what good tutors do at a higher level of abstraction, thus, enabling more general conclusions about good tutoring. We propose an approach to the dialogue mode labeling problem based on Conditional Random Fields, a powerful machine learning technique for sequence labeling which has net advantages over alternatives such as Hidden Markov Models. The downside of the Condition Random Fields approach is that it requires annotated data while the Hidden Markov Models approach is unsupervised. The performance of the approach on a large data set of 1,438 tutoring sessions yielded very good results compared to human generated tags

SemAligner: A method and tool for aligning chunks with semantic relation types and semantic similarity scores

This paper introduces a ruled-based method and software tool, called SemAligner, for aligning chunks across texts in a given pair of short English texts. The tool, based on the top performing method at the Interpretable Short Text Similarity shared task at SemEval 2015, where it was used with human annotated (gold) chunks, can now additionally process plain text-pairs using two powerful chunkers we developed, e.g. using Conditional Random Fields. Besides aligning chunks, the tool automatically assigns semantic relations to the aligned chunks (such as EQUI for equivalent and OPPO for opposite) and semantic similarity scores that measure the strength of the semantic relation between the aligned chunks. Experiments show that SemAligner performs competitively for system generated chunks and that these results are also comparable to results obtained on gold chunks. SemAligner has other capabilities such as handling various input formats and chunkers as well as extending lookup resources

DTSim at SemEval-2016 task 2: Interpreting similarity of texts based on automated chunking, chunk alignment and Semantic relation prediction

In this paper we describe our system (DTSim) submitted at SemEval-2016 Task 2: Inter-pretable Semantic Textual Similarity (iSTS). We participated in both gold chunks category (texts chunked by human experts and provided by the task organizers) and system chunks category (participants had to automatically chunk the input texts). We developed a Conditional Random Fields based chunker and applied rules blended with semantic similarity methods in order to predict chunk alignments, alignment types and similarity scores. Our system obtained F1 score up to 0.648 in predicting the chunk alignment types and scores together and was one of the top performing systems overall