Look Before You Leap into ERP Implementation: An Object-Oriented Approach to Business Process Modeling

Procedural due diligence is critical for effective adoption and use of enterprise systems. Such procedural review needs to be holistic, capturing both the mechanistic aspects of process workflows as well as the human behavioral influences. Traditional methodologies offer little guidance on how to capture the human dimension of business processes. This article draws upon the Object Oriented (OO) concepts to propose and validate an approach that simultaneously models the content of the process flows and the human behavioral context. The two companies that served as test sites greatly benefited from using this process modeling approach. The analysis results gave these companies—that were on the verge of full-scale Enterprise Resources Planning (ERP) implementation—reason to pause and reevaluate their current state of affairs

Nonlinear Systems in Healthcare towards Intelligent Disease Prediction

Healthcare is one of the key fields that works quite strongly with advanced analytical techniques for prediction of diseases and risks. Data being the most important asset in recent times, a huge amount of health data is being collected, thanks to the recent advancements of IoT, smart healthcare, etc. But the focal objective lies in making sense of that data and to obtain knowledge, using intelligent analytics. Nonlinear systems find use specifically in this field, working closely with health data. Using advanced methods of machine learning and computational intelligence, nonlinear analysis performs a key role in analyzing the enormous amount of data, aimed at finding important patterns and predicting diseases. Especially in the field of smart healthcare, this chapter explores some aspects of nonlinear systems in predictive analytics, providing a holistic view of the field as well as some examples to illustrate such intelligent systems toward disease prediction

Marco de trabajo de rasgos biométricos en queiloscopía mediante el uso de machine learning

La Queiloscopía es el estudio de las impresiones labiales que se producen a través del análisis de las líneas, fisuras, arrugas y estrías presentes en el labio. “Queilos” proviene del griego que significa labio y “scopia” examinar. Según Cardoso [3], fue el antropólogo R. Fischer el pionero en esta área. Éste describió los surcos en 1902, pero no fue hasta 1932 que Edmond Locard, reconocido criminalista francés, recomendó su uso para la identificación. No obstante, tuvieron que pasar veintiocho años para que en 1950 LeMonyne Snyder los utilice en un caso real. Aunque la Queiloscopía es un campo relativamente nuevo entre la gran cantidad de herramientas de identificación disponible para expertos forenses, de ésta se obtiene información sumamente útil como la identidad de una persona. Esto se debe a que permanecen relativamente estables y muestran diferencias en cuanto al género La Queiloscopía es un procedimiento manual donde se utilizan herramientas como lupas y escalas para analizar las huellas labiales. Esto lo convierte en una metodología propensa a errores humanos Para evitar esto y automatizarla, se precisa de un algoritmo. Por otro lado, Machine Learning (ML) es un subcampo de la Inteligencia Artificial (IA). Esta última se define como la inteligencia exhibida por una entidad artificial para resolver problemas complejos. Tal sistema generalmente supone ser una computadora o máquina . Dicho de otra forma, se puede decir que la IA es la habilidad que tiene dicha entidad de utilizar algoritmos para aprender de los datos y usar este conocimiento para tomar decisiones como lo haría un ser humano. A diferencia de este último, las máquinas que cuentan con IA corren con la ventaja de no precisar de descansos, analizar enormes cantidades de datos de forma simultánea y contar con una baja tasa de error . Si bien la IA y ML han estado presentes desde hace mucho tiempo, es solamente ahora que se cuenta con el poder computacional para efectivamente desarrollar Redes Neuronales Artificiales (RNA) lo suficientemente poderosas en un lapso de tiempo razonable . En el campo de la biometría, ML resalta por su capacidad de aumentar la precisión en el proceso de identificación. Las características biométricas tomadas en primera instancia no son siempre iguales a las tomadas una segunda vez. En consecuencia, el uso de técnicas de aprendizaje automático como neuronales, lógica difusa, informática evolutiva, etc., ha incrementado su demanda. En este contexto, el objetivo del proyecto es definir un marco de trabajo, utilizando ML, para determinar rasgos biométricos suaves de una persona, como el sexo y edad, a través de sus impresiones labiales.Red de Universidades con Carreras en Informátic

Machine Learning Integration in Cardiac Electrophysiology

Atrial fibrillation is a disorder in which there is a chaotic fire of electrical signals from the upper chambers of the heart. The identification of the location of the myocardium responsible for firing these signals and ablation of the area may potentially cure the problem. The electrophysiologists may have to insert the probes or catheters and do the cardiac mapping to identify and analyze the complex heart signals patterns and to identify the location of AF responsible electrical foci. Nowadays, machine learning has become crucial in every technology field. Automation with software using machine-learning algorithms may aid electrophysiologists to do cardiac mapping without struggle and detecting electrical foci by computers. ML algorithms may identify arrhythmia compared to a board-certified cardiologist and can be developed as a very fast and reliable diagnostic tool. (c) 2020, Institute of Advanced Scientific Research, Inc.. All rights reserved

Device for the Evaluation of Carotid Arterial Pressure Based on IoT and 3D-Printing: uFISIO

El análisis de la forma de onda de presión aórtica central (PAC) permite un seguimiento más específico de patologías tales como la hipertensión arterial, la enfermedad coronaria y la diabetes. Se diseñó un dispositivo inalámbrico, portátil y ergonómico (uFISIO) para realizar evaluaciones morfológicas de la presión de la arteria carótida (PACar), que está estrechamente vinculada al comportamiento de la PAC. La forma de onda PACar fue adquirida por la técnica de tonometría de aplanamiento y enviada a un nodo central de una red inalámbrica local, para ser procesada en un dispositivo móvil. Los resultados fueron posteriormente transmitidos a un servidor central, en virtud del concepto 'Internet de las Cosas' (IoT). Complementariamente se utilizó tecnología de impresión 3D, para el desarrollo del diseño. El dispositivo fue probado en 6 individuos jóvenes, donde fueron evaluados parámetros morfológicos de PCar tales como el factor de forma, índice de aumento, tiempos característicos e integrales temporales a través de la aplicación móvil. Las formas de onda fueron adquiridas, transmitidas, procesadas y almacenadas adecuadamente, obteniendo valores de acuerdo con publicaciones anteriores. El dispositivo mostró versatilidad y confortabilidad en su utilización para evaluar PACar, así como para la gestión de la información resultante. Se requieren estudios futuros para determinar su aplicabilidad clínica, especialmente en diferentes grupos etarios.Pulse wave analysis of central aortic pressure waveform (CAP) allows a detailed follow-up of pathologies such as hypertension, coronary artery disease and diabetes. A wireless, portable and ergonomic device (uFISIO) was designed to perform morphological evaluations of carotid artery pressure (APCar), which is closely linked to CAP behavior. APCar waveform was acquired by the applanation tonometry technique and sent to a hub node of a local wireless network, in order to be processed in a mobile device. The results were posteriorly transmitted to a central server, in virtue of the ‘Internet of Things’ (IoT) concept. 3D printing technology was also used to develop the design. The device was tested in 6 young individuals, where APCar morphological parameters such as form factor, augmentation index, characteristic times and temporal integrals were assessed by the mobile application. Waveforms were acquired, transmitted, processed and stored adequately, obtaining values in agreement with previous publications. The device showed versatility and comfortability in its use for APCar evaluation, as well as for the management of the resulting information. Future studies are required to determine its clinical applicability, especially in different age groups.Fil: De Luca, Martín A.. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; ArgentinaFil: Cymberknop, Leandro Javier. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; ArgentinaFil: Meyer, Iván. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; ArgentinaFil: Percunte, Maia Daniela. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; ArgentinaFil: Chatterjee, Parag. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; ArgentinaFil: Arbeitman, Claudia Roxana. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Armentano, Ricardo Luis. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; Argentin

Can AI democratize healthcare? TEDxUTN

From his own experience in musing on data (and life) and pondering how human intelligence can make artificial intelligence work to improve healthcare, Parag Chatterjee shares his views on the power of AI in democratizing healthcare. Graduated with a master’s degree in computer science from University of Calcutta, India, Parag works as a researcher and faculty member at the National Technological University in Buenos Aires, Argentina and at the University of the Republic in Uruguay.Agencia Nacional de Investigación e Innovación, UruguayUniversidad Tecnológica Nacional, Buenos Aires, ArgentinaUniversidad de la República, Urugua

Internet of Things and Artificial Intelligence in Healthcare During COVID-19 Pandemic—A South American Perspective

The shudders of the COVID-19 pandemic have projected newer challenges in the healthcare domain across the world. In South American scenario, severe issues and difficulties have been noticed in areas like patient consultations, remote monitoring, medical resources, healthcare personnel etc. This work is aimed at providing a holistic view to the digital healthcare during the times of COVID-19 pandemic in South America. It includes different initiatives like mobile apps, web-platforms and intelligent analyses toward early detection and overall healthcare management. In addition to discussing briefly the key issues toward extensive implementation of eHealth paradigms, this work also sheds light on some key aspects of Artificial Intelligence and the Internet of Things along their potential applications like clinical decision support systems and predictive risk modeling, especially in the direction of combating the emergent challenges due to the COVID-19 pandemic

Population density and habitat use of two sympatric small cats in a central Indian reserve.

Despite appreciable advances in carnivore ecology, studies on small cats remain limited with carnivore research in India being skewed towards large cats. Small cats are more specialized than their larger cousins in terms of resource selection. Studies on small cat population and habitat preference are critical to evaluate their status to ensure better management and conservation. We estimated abundance of two widespread small cats, the jungle cat, and the rusty-spotted cat, and investigated their habitat associations based on camera trap captures from a central Indian tiger reserve. We predicted fine-scale habitat segregation between these sympatric species as a driver of coexistence. We used an extension of the spatial count model in a Bayesian framework approach to estimate the population density of jungle cat and rusty-spotted cat and used generalized linear models to explore their habitat associations. Densities of rusty-spotted cat and jungle cat were estimated as 6.67 (95% CI 4.07-10.74) and 4.01 (95% CI 2.65-6.12) individuals/100 km2 respectively. Forest cover and evapotranspiration were positively associated with rusty-spotted cat occurrence whereas both factors had a significant negative relation with jungle cat occurrence. The results directed habitat segregation between these small cats with affinities of rusty-spotted cat and jungle cat towards well-forested and open scrubland areas respectively. Our estimates highlight the widespread applicability of this model for density estimation of species with no individual identification. Moreover, the study outcomes can aid in targeted management decisions and serve as the baseline for species conservation as these models allow robust population estimation of elusive species along with predicting their habitat preferences

Applied Approach to Privacy and Security for the Internet of Things

From transportation to healthcare, IoT has been heavily implemented into practically every professional industry, making these systems highly susceptible to security breaches. Because IoT connects not just devices but also people and other entities, every component of an IoT system remains vulnerable to attacks from hackers and other unauthorized units. This clearly portrays the importance of security and privacy in IoT, which should be strong enough to keep the entire platform and stakeholders secure and smooth enough to not disrupt the lucid flow of communication among IoT entities. Applied Approach to Privacy and Security for the Internet of Things is a collection of innovative research on the methods and applied aspects of security in IoT-based systems by discussing core concepts and studying real-life scenarios. While highlighting topics including malware propagation, smart home vulnerabilities, and bio-sensor safety, this book is ideally designed for security analysts, software security engineers, researchers, computer engineers, data scientists, security professionals, practitioners, academicians, and students seeking current research on the various aspects of privacy and security within IoT

Applied approach to privacy and security for the internet of things Advances in information security, privacy, and ethics (AISPE) book series./ Parag Chatterjee, Emmanuel Benoist, Asoke Nath.

"Premier Reference Source" -- taken from front cover.Includes bibliographical references and index."This book examines the conceptual aspects of security and privacy in IoT. It also explores the application of IoT systems in smart transports, smart cities, and smart healthcare"--Collision avoidance methodology in internet of things & wireless ad hoc network / Arundhati Arjaria, Priyanka Dixit -- Information security management system : a case study of employee management / Manoj Srivastav -- Anomaly detection in IoT frameworks using machine learning / Phidahunlang Chyne, Parag Chatterjee, Sugata Sanyal, Debdatta Kandar -- Real-time, cross-platform detection of spectre and meltdown attack variants / Xinxing Zhao, Chandra Veerappan, Peter Loh -- Trust models in IoT / Shiladitya Sengupta -- Vulnerabilities of smart home / Suchandra Datta -- Security and privacy vulnerabilities in automated driving / Suchandra Datta -- Key vulnerabilities in IoT systems / Shiladitya Sengupta -- IoT forensic : principles, processes and activities / Eoghan Casey, Hannes Spichiger, Elénore Ryser, Francesco Servida, David-Olivier Jaquet-Chiffelle -- IoT controlled railway gate system with ML object detection approach : applied approach for secured IOT system / Megha Kamble.1 online resource (xix, 295 pages)