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Found 23 papers, 7 papers with code
AMFPMC -- An improved method of detecting multiple types of drug-drug interactions using only known drug-drug interactions
Adverse drug interactions are largely preventable causes of medical accidents, which frequently result in physician and emergency room encounters.
Transfer learning for time series classification using synthetic data generation
In this paper, we propose an innovative Transfer learning for Time series classification method.
Boosting Anomaly Detection Using Unsupervised Diverse Test-Time Augmentation
Anomaly detection is a well-known task that involves the identification of abnormal events that occur relatively infrequently.
Evolving Context-Aware Recommender Systems With Users in Mind
An empirical analysis of our results validates that our proposed approach outperforms SOTA CARS models while improving transparency and explainability to the user.
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach
We find that individuals can be ranked for effective testing using a few simple features, and that ranking them using such models we can capture 65% (CI: 64. 7%-68. 3%) of the positive individuals using less than 20% of the testing capacity or 92. 1% (CI: 91. 1%-93. 2%) of positives individuals using 70% of the capacity, allowing reserving a significant portion of the tests for population studies.
Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate
Learning sophisticated models to understand and predict user behavior is essential for maximizing the CTR in recommendation systems.
Automatic Machine Learning Derived from Scholarly Big Data
One of the challenging aspects of applying machine learning is the need to identify the algorithms that will perform best for a given dataset.
Sequence Preserving Network Traffic Generation
We examined two methods for extracting sequences of activities: a Markov model and a neural language model.
Diversifying Database Activity Monitoring with Bandits
Database activity monitoring (DAM) systems are commonly used by organizations to protect the organizational data, knowledge and intellectual properties.
Deep Context-Aware Recommender System Utilizing Sequential Latent Context
Recent research has shown that modeling contextual information as a latent vector may address the sparsity and dimensionality challenges.
Assessing the Quality of Scientific Papers
In this paper, we propose a measure and method for assessing the overall quality of the scientific papers in a particular field of study.
A difficulty ranking approach to personalization in E-learning
EduRank constructs a difficulty ranking for each student by aggregating the rankings of similar students using different aspects of their performance on common questions.
New Item Consumption Prediction Using Deep Learning
Our experiments show that PISA outperforms a well-known deep learning baseline when new items are introduced.
Personal Dynamic Cost-Aware Sensing for Latent Context Detection
Results show that by applying a dynamic sampling policy, our method naturally balances information loss and energy consumption and outperforms the static approach.% We compared the performance of our method with another state of the art dynamic sampling method and demonstrate its consistent superiority in various measures.
Online Budgeted Learning for Classifier Induction
In online learning, the challenge is to find the optimum set of features to be acquired from each instance upon arrival from a data stream.
Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures
Finally, we create an ensemble-based classifier using AMF, AMFP, and existing link prediction methods and obtain an area under the receiver operating characteristic curve of 0. 814 and 0. 991 for the retrospective and the holdout analyses.
Attack Graph Obfuscation
Before executing an attack, adversaries usually explore the victim's network in an attempt to infer the network topology and identify vulnerabilities in the victim's servers and personal computers.
Explaining Anomalies Detected by Autoencoders Using SHAP
Anomaly detection algorithms are often thought to be limited because they don't facilitate the process of validating results performed by domain experts.
Implicit Dimension Identification in User-Generated Text with LSTM Networks
In the process of online storytelling, individual users create and consume highly diverse content that contains a great deal of implicit beliefs and not plainly expressed narrative.
Choosing the Right Word: Using Bidirectional LSTM Tagger for Writing Support Systems
We use a bidirectional Recurrent Neural Network (RNN) with LSTM for learning the proper word choice based on a word's sentential context.
Wikibook-Bot - Automatic Generation of a Wikipedia Book
A Wikipedia book (known as Wikibook) is a collection of Wikipedia articles on a particular theme that is organized as a book.
Sampling High Throughput Data for Anomaly Detection of Data-Base Activity
Data leakage and theft from databases is a dangerous threat to organizations.
Language Models with Pre-Trained (GloVe) Word Embeddings
In this work we implement a training of a Language Model (LM), using Recurrent Neural Network (RNN) and GloVe word embeddings, introduced by Pennigton et al. in [1].
