Improved topic identification for similar document search on mobile devices

This paper presents a novel, two level classifier ensemble designed to support document topic identification in mobile device environments. The proposed system aims at supporting mobile device users who search for documents located in other mobile devices which have similar topic to the documents on the users own device. Conforming to the environment of mobile devices, the algorithms are designed for slower processor, smaller memory capacity and they maintain small data traffic between the devices in order to keep low the cost of communication. We propose a keyword list based topic comparison, enhanced with a two level classifier ensemble to accelerate the topic identification process. The new technique enables document topic comparison using few communication traffic and it requires few calculations

Topic comparison of remote documents using small communication traffic

This paper presents a new method for semantic search solutions designed for mobile device environments. The proposed system aims at helping users by searching for documents which have similar topics to the ones stored on the users own device. The search is performed in background continuously and the user is notified if documents worth for downloading were found. The methods proposed in this paper aim at solving this task while maintaining low communication traffic to make them applicable in the mobile device environment

Deep reinforcement learning : a study of the CartPole problem

One of the major challenges of artificial intelligence is to learn solving tasks which are considered to be challenging for even a human. Reinforcement learning is the most general learning framework among the three main type of learning methods: supervised, unsupervised and reinforcement learning. Most of the problems can easily fit into this framework. Experience shows that a lot of machine learning methods with non-linear function approximators suffers from instability regarding convergence. Reinforcement learning is more prone to diverge due to its ability to change the structure of its training data by modifying the way how it interacts with the environment. In this paper we investigate the divergence issue of DQN on the CartPole problem in terms of the algorithm’s parameters. Instead of the usual approach we do not focus on the successful trainings but instead we focus on the dark side where the algorithm fails on such an easy problem like CartPole. The motivation is to gain some further insight into the nature of the divergence issues on a specific problem

GrainAutLine: an Environment for Semi-Automatic Processing of Marble Thin Section Images

GrainAutLine is an interdisciplinary microscopy image analysis tool with domain specific smart functions to partially automate the processing of marble thin section images. It allows the user to create a clean grain boundary image which is a starting point of several archaeometric and geologic analyses. The semi-automatic tools minimize the need for carefully drawing the grain boundaries manually, even in cases where twin crystals prohibit the use of classic edge detection based boundary detection. Due to the semi-automatic approach, the user has full control over the process and can modify the automatic results before finalizing a specific step. This approach guarantees high quality results both in cases where the process is easy to automate, and also if it needs more help from the user. This paper presents the basic operation of the system and details about the provided tools as a case study for an interdisciplinary, semi-automatic image processing application

Spectroscopy-Based Partial Prediction of In Vitro Dissolution Profile Using Artificial Neural Networks

In pharmaceutical industry, dissolution testing is part of the target product quality that essentials are in the approval of new products. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. RAMAN and Near Infrared (NIR) spectroscopy are two complementary methods, that provide information on the physical and chemical properties of the tablets and can help in predicting their dissolution profiles. This work aims to use the information collected by these methods to support the decision of how much of the dissolution profile should be measured and which methods to use, so that by estimating the remaining part, the accuracy requirement of the industry is met. Artificial neural network models were created, in which parts of the measured dissolution profiles, along with the spectroscopy data and the measured compression curves were used as an input to estimate the remaining part of the dissolution profiles. It was found that by measuring the dissolution profiles for 30 minutes, the remaining part was estimated within the acceptance limits of the f2 similarity factor. Adding further spectroscopy methods along with the measured parts of the dissolution profile significantly increased the prediction accuracy

Real-Time Monitoring of Continuous Pharmaceutical Mixed Suspension Mixed Product Removal Crystallization Using Image Analysis

In this work, we developed an in-line image analysis system for the monitoring of the continuous crystallization of an active pharmaceutical ingredient. Acetylsalicylic acid was crystallized in a mixed suspension mixed product removal crystallizer, which was equipped with overflow tubing as an outlet. A steep glass plate was placed under the outlet onto which the slurry dripped on its surface. The glass plate spread and guided the droplets toward the product collection filter. A high-speed process camera was mounted above the glass plate to capture images of the crystals. Several light sources were tested in various positions to find the appropriate experimental setup for the optimal image quality. Samples were taken during continuous operation for off-line particle size analysis in order to compare to the crystal size distributions calculated from the images. The results were in good agreement, and the trends of the process could be followed well using the images. As a next step, image analysis was operated throughout the entire continuous crystallization experiment, and a huge quantity of information was collected from the process. The crystal size distribution of the product was calculated every 30 s, which provided a thorough and detailed insight into the crystallization process