High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

While diagnosing sleep disorders by physicians using electroencephalographic data is protracted and inaccurate, we report promising results from a novel, fast and reliable machine learning approach. Our approach only needs an electroencephalographic recording snippet of 10 minutes instead of eight hours to correctly classify the disorder with an accuracy of over 90 percent. The Rapid Eye Movement sleep behavior disorder can lead to secondary diseases like Parkinson or Dementia. Therefore, it is important to classify the disorder fast and with a high level of accuracy - which is now possible with our approach

Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings

While diagnosing schizophrenia by physicians based on patients' history and their overall mental health is inaccurate, we report on promising results using a novel, fast and reliable machine learning approach based on electroencephalography (EEG) recordings. We show that a fine granular division of EEG spectra in combination with the Random Forest classifier allows a distinction to be made between paranoid schizophrenic (ICD-10 F20.0) and non-schizophrenic persons with a very good balanced accuracy of 96.77 percent. We evaluate our approach on EEG data from an open neurological and psychiatric repository containing 499 one-minute recordings of n=28 participants (14 paranoid schizophrenic and 14 healthy controls). Since the fact that neither diagnostic tests nor biomarkers are available yet to diagnose paranoid schizophrenia, our approach paves the way to a quick and reliable diagnosis with a high accuracy. Furthermore, interesting insights about the most predictive subbands were gained by analyzing the electroencephalographic spectrum up to 100 Hz

High-performance detection of epilepsy in seizure-free EEG recordings: A novel machine learning approach using very specific epileptic EEG sub-bands

We applied machine learning to diagnose epilepsy based on the fine-graded spectral analysis of seizure-free (resting state) EEG recordings. Despite using unspecific agglomerated EEG spectra, our fine-graded spectral analysis specifically identified the two EEG resting state sub-bands differentiating healthy people from epileptics (1.5-2 Hz and 11-12.5 Hz). The rigorous evaluation of completely unseen data of 100 EEG recordings (50 belonging to epileptics and the other 50 to healthy people) shows that the approach works successfully, achieving an outstanding accuracy of 99 percent, which significantly outperforms the current benchmark of 70% to 95% by a panel of up to three experienced neurologists. Our epilepsy diagnosis classifier can be implemented in modern EEG analysis devices, especially in intensive care units where early diagnosis and appropriate treatment are decisive in life and death scenarios and where physicians’ error rates are particularly high. Our approach is accurate, robust, fast, and cost-efficient and substantially contributes to Information Systems research in healthcare. The approach is also of high practical and theoretical relevance

Cost behavior in e-commerce firms

We conduct empirical research on the flexibility of operating costs of e-commerce firms. With an international sample of firms from different European countries, we find that e-commerce firms have a different cost structure than traditional retail firms, with a lower share of labor costs and cost of goods sold, but a higher share of other operating costs. While we find no significant different behavior in cost of goods sold and labor costs between the two types of firms, e-commerce firms are more flexible in adjusting other operating costs than traditional retail firms when activity decreases. Results are robust to different models, estimations methods and samples. The higher flexibility of e-commerce firms relies on other operating costs, but e-commerce creates qualified jobs with higher wages than traditional retail, with no additional exposure to labor uncertainty for employees

Direct and indirect effect of last mile logistics performance on user intention of crowdsourced delivery services

La literatura sobre logística colaborativa" (CSL) y logística de última milla hasta ahora se ha centrado principalmente en la percepción de los consumidores como "co-creadores. Sin embargo, hay una brecha en la literatura sobre la percepción de los consumidores como destinatarios de esta logística. El propósito de esta investigación fue analizar el efecto directo del Rendimiento Logístico de Última Milla (LMLP), sobre la Intención de Usuario (UI) del usuario final de las plataformas de entrega colaborativas, e indirecto a través de la Confianza Percibida (PT) y la Expectativa de Desempeño (PE ). La metodología aplicada consta de 721 encuestas, recolectadas a través de un instrumento validado. Para el análisis se aplicó un Modelo de Ecuaciones Estructurales (SEM), por mínimos cuadrados parciales. El modelo seleccionado presentó Índices de Ajuste fuertes (CFI=0.976; TLI=0.970; RMSEA; = 0.044; SRMR=0.025). No hay efecto directo de LMLP y PT sobre UI (p = 0,175, 0,054), pero sí existen relaciones indirectas. La conclusión es que LMLP y PT son considerados por los usuarios finales de los servicios de entrega colaborativos como parte del desempeño de la empresa en su conjunto al momento de decidir utilizar estas plataformas. Para futuras investigaciones, se recomienda primero, investigar factores asociados a la cultura; segundo, estratificar los resultados para evaluar diferencias entre grupos de edad; tercero, estudiar factores internos que pueden afectar la intención de uso de estas plataformas, como la experiencia del usuario, la facilidad de uso, el control percibido, que no fueron considerados; cuarto, realizar una investigación que contemple las diferencias de marca.The literature on crowdsourced logistics" (CSL) and edge logistics so far has primarily focused on the perception of consumers as "co-creators of logistics". However, there is a breach in the literature about the perception of consumers as recipients of these logistics services. The purpose of this research was to analyze the direct effect of Last Mile Logistics Performance (LMLP), on the User Intention (UI) of the end user of crowdsourced delivery platforms, and indirect through Perceived Confidence (PT) and Performance Expectation (PE). The applied methodology comprises 721 surveys, gathered through a validated instrument. For the analysis, a Structural Equations Model (SEM) was applied, by partial least squares. The selected model had strong Fit Indexes (CFI=0.976; TLI=0.970; RMSEA; = 0.044; SRMR=0.025). There is no direct effect of LMLP and PT over UI (p = 0.175; 0.054). However, the standardized indirect effect of LMLP in IU, mediated by PT is, 0.699; while the standardized indirect effect of PT in IU, mediated by PE is 0.664. The conclusion is that LMLP and PT are seemed by the final users of crowdsourced delivery services as part of the performance of the business as a whole at the moment of deciding to use these platforms. For future research, it is recommended first, to investigate factors associated with culture; second, to stratify the results to assess differences between age groups; third, to study internal factors that can affect the intention to use these platforms, such as user experience, ease of use, perceived control, which were not considered; fourth, to perform an investigation that contemplates brand differences

A Recommender system for rental properties

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Computer-Based Information Systems (MSIS) at Strathmore UniversityLocating products or services online that meet users’ needs is increasingly difficult due to the large pool of choices to consider before arriving at the desired one. A user may spend a considerable amount of time exploring numerous online resources to locate items that fit his requirements. Furthermore, users may not always express their preferences in a manner that easily matches them to items that could meet them. Searching for items online has been done mainly through database queries that return a list of the most suitable items. Recommender systems technology can be applied to ease the task of locating desired items online. This study proposes a recommender system that enables users to carry out a preference-based search on rental properties and enables them to refine those preferences using example-critiquing in case they are not satisfied with initial search results. This recommendation approach has been shown to provide more accurate search results. This research adopted the Object-Oriented Systems Analysis and Design (OOAD) approach to the development of the system. The system was developed as a Web application using the Ruby on Rails framework. Furthermore, the system was tested to ascertain that it performed as designed

Main drivers for microtransactions as impulse purchases in e-commerce

M30, M31With mobile technology evolving at a very fast-paced level, consumers now have many choices of entertainment on their mobile devices. Thousands of games are available to download free of charge on virtually every smartphone and with them a new revenue model has emerged: microtransactions. Characterized by low price points, microtransaction have seldom been studied extensively. With great potential in the future, this type of revenue model is currently outgrowing traditional pay-to-play model types. By focusing on several types of mobile game item drivers and adapting some previous research and models, this study intends to identify and create a model with the main drivers of microtransactions that lead to impulse purchases in mobile game applications and understand if a price increase will lead to a lower purchase intention. A PLS-SEM analysis was conducted on a sample of 301 individuals. The measurement model showed a good fit of parameters, with AVE above 0.50 for all components, composite reliability superior to 0.70 for all components as well as an HTMT value inferior to 0.90 present in each component relationship. The six components considered explained 53.3% of the variance in impulse buying tendency. Significant component drivers from strongest to least robust were flow experience, social, hedonic/emotional and performance drivers. Functionality and low perceived risk were not drivers of impulse buying tendency.Com a tecnologia móvel a evoluir a um passo cada vez mais acelerado, os consumidores têm agora várias escolhas de entretenimento nos seus dispositivos móveis. Milhares de jogos estão disponíveis para descarregar de forma gratuita em virtualmente qualquer smartphone e com isso, um novo modelo de negócio tem emergido: microtransações. Caracterizado pelos seus preços baixos, as microtransações têm raramente sido estudadas extensivamente. Com um grande potencial no futuro, este tipo de modelo de negócio está a ultrapassar no presente os modelos tradicionais de comprar-para-jogar. Ao focar-se em vários tipos de drivers de itens de jogos móveis e adaptando pesquisas e modelos anteriores, este estudo pretende identificar e criar um modelo com os principais drivers das microtransações que originam compras por impulse em aplicações de jogos móveis e compreender se um aumento de preço leva a uma intenção de compra reduzida. Uma análise de PLS-SEM foi efetuada numa amostra de 301 indivíduos. O modelo medido demonstrou um bom índice dos seus parâmetros, com um AVE superior a 0.50 para todos os componentes, confiabilidade composta também superior a 0.70 para todos os componentes e um valor de HTMT inferior a 0.90 para cada relação entre os componentes. Os seis componentes originais considerados explicam 53.2% da variância da tendência de compra por impulso. Os drivers de conteúdo significantes do mais forte para o menos forte foram: fluidez de experiência, social, hedónico/emocional e performance. Funcionalidade e baixa perceção de risco não foram drivers de tendência de compra por impulso

The use of machine learning algorithms in recommender systems: A systematic review

The final publication is available at Elsevier via https://doi.org/10.1016/j.eswa.2017.12.020 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics.Natural Sciences and Engineering Research Council of Canada (NSERC) Ontario Research Fund of the Ontario Ministry of Research, Innovation, and Scienc

From communication to communigation: a conceptual model to strengthen South Africa’s government communication system – the case of Mpumalanga Province

Text in EnglishThis study adopted a quantitative approach in order to produce numbers in relation to the diffusion of the new media. A descriptive quantitative survey was conducted – with sampling done in multi-stage probability – which comprised clustering, simple random, systematic, stratified sampling techniques, convenience and census sampling. A sample size of 379 respondents was selected, comprising 347 citizen-respondents and 32 government communicators (heads of communication). Data was collected utilising two (2) standardised questionnaires – one tailor-made for the citizens and the other for government communicators. Informed by the Diffusion of Innovations theory, this study has established that new media channels have difussed extensively within communities in the Province of Mpumalanga. This has provided a strong motivation to recommend that the communication policy of the South African government be amended, to include new media channels, like social media, as official government communication channels.Communication ScienceD. Litt. et Phil. (Communication