Dishonest behavior at self-service checkouts

Self-service technology could be argued as creating less personal transactions when compared to traditional checkouts involving a sales assistant for the entire transaction process, which may affect customer behavior. The aim of our study was to investigate the perceived influence of social presence at self-service checkouts by staff and its perceived effect on dishonest customer behavior. Twenty-six self-service checkout staff took part in a series of semi-structured interviews to describe customer behaviors with self-service. With respect to actual physical social presence, staff reported that more customer thefts occurred when the self-service checkouts were busy and their social presence was reduced. Staff also reported that perceived and actual social presence is likely to reduce thefts. Future research will elaborate to which extent the perceived social presence via technological systems might support staff in their task to assist customers and reduce dishonest behavior

Aportaciones para la mejora de la usabilidad en aplicaciones móviles de comunicación social

La presente Tesis Doctoral se enmarca en el auge en el uso de las aplicaciones de mensajería instantánea móvil (denominadas MIM) para dispositivos móviles inteligentes (o smartphones) y motivado por los diferentes estudios científicos que señalan los problemas de usabilidad que este tipo de aplicaciones presentan. Para ello, se ha realizado una evaluación sistemática de la usabilidad (con la aplicación de métodos de inspección de la usabilidad: análisis de tareas y evaluación heurística) de las aplicaciones MIM que actualmente se pueden encontrar en el mercado de las dos principales plataformas móviles (iOS y Android). Con los problemas de usabilidad detectados en ambas plataformas se ha propuesto un conjunto de recomendaciones de usabilidad para su aplicación en el diseño de aplicaciones MIM. Tras esta fase de evaluación y propuesta, se ha creado un prototipo de aplicación MIM para la plataforma Android que incorpora dicho conjunto de recomendaciones de usabilidad. El prototipo ha sido evaluado con métodos de inspección de la usabilidad para, tras una valoración satisfactoria, proceder a un experimento controlado con usuarios reales (en este experimento, los participantes han realizado una serie de actividades con el prototipo y un grupo de aplicaciones MIM existentes con buenos resultados de usabilidad derivados de las evaluaciones iniciales), que ha dado como resultado la validación de las recomendaciones de usabilidad para su uso en este tipo de aplicaciones. Como conclusiones, además de (1) la propuesta y validación de las recomendaciones de usabilidad para su aplicación en el contexto de la mensajería instantánea, se pueden extraer algunas conclusiones de la realización de esta investigación: (2) definición formal de las actividades que definen a una aplicación de mensajería instantánea, (3) ratificación de la existencia de problemas de usabilidad en las aplicaciones MIM actuales, (4) confirmación de que los buenos diseños de UI tienen un efecto positivo en el rendimiento de los usuarios de estas aplicaciones y (5) revelación de que son los propios contactos cercanos del usuario los que actúan como el ancla para que el usuario permanezca en la aplicación MIM (pese a tener disconformidades con la aplicación)

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models