Design and Architecture of an Ontology-driven Dialogue System for HPV Vaccine Counseling

Speech and conversational technologies are increasingly being used by consumers, with the inevitability that one day they will be integrated in health care. Where this technology could be of service is in patient-provider communication, specifically for communicating the risks and benefits of vaccines. Human papillomavirus (HPV) vaccine, in particular, is a vaccine that inoculates individuals from certain HPV viruses responsible for adulthood cancers - cervical, head and neck cancers, etc. My research focuses on the architecture and development of speech-enabled conversational agent that relies on series of consumer-centric health ontologies and the technology that utilizes these ontologies. Ontologies are computable artifacts that encode and structure domain knowledge that can be utilized by machines to provide high level capabilities, such as reasoning and sharing information. I will focus the agent’s impact on the HPV vaccine domain to observe if users would respond favorably towards conversational agents and the possible impact of the agent on their beliefs of the HPV vaccine. The approach of this study involves a multi-tier structure. The first tier is the domain knowledge base, the second is the application interaction design tier, and the third is the feasibility assessment of the participants. The research in this study proposes the following questions: Can ontologies support the system architecture for a spoken conversational agent for HPV vaccine counseling? How would prospective users’ perception towards an agent and towards the HPV vaccine be impacted after using conversational agent for HPV vaccine education? The outcome of this study is a comprehensive assessment of a system architecture of a conversational agent for patient-centric HPV vaccine counseling. Each layer of the agent architecture is regulated through domain and application ontologies, and supported by the various ontology-driven software components that I developed to compose the agent architecture. Also discussed in this work, I present preliminary evidence of high usability of the agent and improvement of the users’ health beliefs toward the HPV vaccine. All in all, I introduce a comprehensive and feasible model for the design and development of an open-sourced, ontology-driven conversational agent for any health consumer domain, and corroborate the viability of a conversational agent as a health intervention tool

Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314

A low-cost closed-loop solar tracking system based on the sun position algorithm

Sun position and the optimum inclination of a solar panel to the sun vary over time throughout the day. A simple but accurate solar position measurement system is essential for maximizing the output power from a solar panel in order to increase the panel efficiency while minimizing the system cost. Solar position can be measured either by a sensor (active/passive) or through the sun position monitoring algorithm. Sensor-based sun position measuring systems fail to measure the solar position in a cloudy or intermittent day, and they require precise installation and periodic calibrations. In contrast, the sun position algorithms use mathematical formula or astronomical data to obtain the station of the sun at a particular geographical location and time. A standalone low-cost but high-precision dual-axis closed-loop sun-tracking system using the sun position algorithm was implemented in an 8-bit microcontroller platform. The Astronomical Almanac's (AA) algorithm was used for its simplicity, reliability, and fast computation capability of the solar position. Results revealed that incorporation of the sun position algorithm into a solar tracking system helps in outperforming the fixed system and optical tracking system by 13.9% and 2.1%, respectively. In summary, even for a small-scale solar tracking system, the algorithm-based closed-loop dual-axis tracking system can increase overall system efficiency. - 2019 Muhammad E. H. Chowdhury et al.The publication of this article was funded by the Qatar National Library. The authors would like to thank Qatar University for granting the student grant (QUST--CENG-SPR\2017-23) which made this work possible. We would like to thank the Mechanical Engineering Department, Qatar University, for their assistance in designing the mechanical system.Scopu

RamanNet: A generalized neural network architecture for Raman Spectrum Analysis

Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kind of materials. This sort of fingerprinting molecules has thus led to widespread application of Raman spectrum in various fields like medical dignostics, forensics, mineralogy, bacteriology and virology etc. Despite the recent rise in Raman spectra data volume, there has not been any significant effort in developing generalized machine learning methods for Raman spectra analysis. We examine, experiment and evaluate existing methods and conjecture that neither current sequential models nor traditional machine learning models are satisfactorily sufficient to analyze Raman spectra. Both has their perks and pitfalls, therefore we attempt to mix the best of both worlds and propose a novel network architecture RamanNet. RamanNet is immune to invariance property in CNN and at the same time better than traditional machine learning models for the inclusion of sparse connectivity. Our experiments on 4 public datasets demonstrate superior performance over the much complex state-of-the-art methods and thus RamanNet has the potential to become the defacto standard in Raman spectra data analysi

An Intelligent and Low-cost Eye-tracking System for Motorized Wheelchair Control

In the 34 developed and 156 developing countries, there are about 132 million disabled people who need a wheelchair constituting 1.86% of the world population. Moreover, there are millions of people suffering from diseases related to motor disabilities, which cause inability to produce controlled movement in any of the limbs or even head.The paper proposes a system to aid people with motor disabilities by restoring their ability to move effectively and effortlessly without having to rely on others utilizing an eye-controlled electric wheelchair. The system input was images of the users eye that were processed to estimate the gaze direction and the wheelchair was moved accordingly. To accomplish such a feat, four user-specific methods were developed, implemented and tested; all of which were based on a benchmark database created by the authors.The first three techniques were automatic, employ correlation and were variants of template matching, while the last one uses convolutional neural networks (CNNs). Different metrics to quantitatively evaluate the performance of each algorithm in terms of accuracy and latency were computed and overall comparison is presented. CNN exhibited the best performance (i.e. 99.3% classification accuracy), and thus it was the model of choice for the gaze estimator, which commands the wheelchair motion. The system was evaluated carefully on 8 subjects achieving 99% accuracy in changing illumination conditions outdoor and indoor. This required modifying a motorized wheelchair to adapt it to the predictions output by the gaze estimation algorithm. The wheelchair control can bypass any decision made by the gaze estimator and immediately halt its motion with the help of an array of proximity sensors, if the measured distance goes below a well-defined safety margin.Comment: Accepted for publication in Sensor, 19 Figure, 3 Table

Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media

This study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer–drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifedipine, Paracetamol, Sulfadiazine, Sulfadimidine, Sulfamerazine, and Sulfathiazole in 1,3-bis[2-pyrrolidone-1-yl] butane, Polyvinyl Acetate, Polyvinylpyrrolidone (PVP), PVP K12, PVP K15, PVP K17, PVP K25, PVP/VA, PVP/VA 335, PVP/VA 535, PVP/VA 635, PVP/VA 735, Soluplus analyzes from a modeling perspective. The least-squares support vector regression (LS-SVR) designs to approximate the solubility temperature of drugs in polymers from polymer and drug types and drug loading in polymers. The structure of this machine learning model is well-tuned by conducting trial and error on the kernel type (i.e., Gaussian, polynomial, and linear) and methods used for adjusting the LS-SVR coefficients (i.e., leave-one-out and 10-fold cross-validation scenarios). Results of the sensitivity analysis showed that the Gaussian kernel and 10-fold cross-validation is the best candidate for developing an LS-SVR for the given task. The built model yielded results consistent with 278 experimental samples reported in the literature. Indeed, the mean absolute relative deviation percent of 8.35 and 7.25 is achieved in the training and testing stages, respectively. The performance on the largest available dataset confirms its applicability. Such a reliable tool is essential for monitoring polymer–drug systems’ stability and deliverability, especially for poorly soluble drugs in polymers, which can be further validated by adopting it to an actual implementation in the future