Artificial Intelligence Based Memory Stash Alzheimer’s Aid

Sheri, Bhagia; Kumari, Pooja; Siddiqui, Isma Farah; Noman, Hira

doi:10.1109/icisct49550.2020.9080030

Cited by 7 publications

(7 citation statements)

References 7 publications

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“…AD is one of the most prevalent neurological disorders, which contributes to 60-80% of the total cases of dementia around the world. Vulnerability towards AD increases with age, almost doubling in frequency of occurrence every five years after the age of 65 years [ 4 - 6 ]. Currently, approximately 47 million people worldwide suffer from AD [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Neuroprotective Potential of Caffeic Acid Phenethyl Ester (CAPE) in CNS Disorders: Mechanistic and Therapeutic Insights

Kulkarni

Vaidya

Narula

et al. 2021

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: Neurological disorders like Alzheimer’s disease (AD), Parkinson’s disease (PD), stroke, amyotrophic lateral sclerosis, Huntington’s disease (HD), epilepsy, traumatic brain injury (TBI), depression and anxiety are responsible for thousands of deaths worldwide every year. With the increase in life expectancy, there has been a rise in the prevalence of these disorders. Age is one of the major risk factors for these neurological disorders and with the aged population is set to rise to 1.25 billion by 2050. There is a growing concern to look for new therapeutic molecules to treat age-related diseases. Caffeic acid phenethyl ester (CAPE) is a molecule obtained from a number of botanical sources such as the bark of conifer trees as well as propolis which is extracted from beehives. Though CAPE remains relatively unexplored in human trials, it possesses antioxidant, anti-inflammatory, antimitogenic and anti-cancer activities as shown by preclinical studies. Apart from this, it also exhibits tremendous potential for the treatment of neurological disorders through modulation of multiple molecular pathways and attenuation of behavioural deficits. In the present article, we have reviewed the therapeutic potential of CAPE and its mechanisms in the treatment of neurological disorders.

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Section: Introductionmentioning

confidence: 99%

Neuroprotective Potential of Caffeic Acid Phenethyl Ester (CAPE) in CNS Disorders: Mechanistic and Therapeutic Insights

Kulkarni

Vaidya

Narula

et al. 2021

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“…Esta aplicación fue desarrollada para las personas con Alzheimer considerando todas sus etapas, utiliza Aprendizaje automático, reconocimiento de imagen, conversión de voz a texto, chatbot, conversión de texto a voz y juegos (Sheri et al, 2018).…”

Section: Aidunclassified

“…Fuente. En base (Guerrero & García, 2015;Sheri et al, 2018;Toribio et al, 2018) Los softwares IA descritos en la tabla 1 de acuerdo con las investigaciones consultadas han demostrado una mayor efectividad en el tratamiento alternativo de la EA, para determinar criterios específicos de utilidad se tomaron en cuenta ámbitos fundamentales para el uso específico en el DCL por EA dentro de su etapa inicial de olvido casual, estos fueron: La memoria operativa La atención, Memoria instantánea, Cálculo, Orientación y Lenguaje. Siendo estos ámbitos seleccionados debido a ser los de mayor afectación dentro de la EA, también se estimó adecuado que el software tenga elementos lúdicos o juegos que mediante estos desarrolles los ámbitos descritos antes (Orejarena et al, 2017).…”

Section: Aidunclassified

“…Fuente. Elaboración propia en base a (Guerrero & García, 2015;Sheri et al, 2018;Toribio et al, 2018) De acuerdo con tabla 2, Gradior software IA provee de un conjunto de herramientas integradas que tienen las características necesarias para ayudar en la terapia alternativa para DCL en pacientes EA, el uso de herramientas IA para tratar padecimiento o afectaciones cognitivas se acogen al principios de neuro plasticidad, la cual es una propiedad única del cerebro de acuerdo con la comunidad científica mundial trabaja para desentrañar su funcionamiento y como este procesa los datos que recibe, con el fin de idear tecnológicas computacionales que puedan curarlo cuando esté presente problemas o falle y restaurar funciones críticas a pacientes con condiciones neurológicas (Banks, 2015, p.11).…”

Section: Educamigos Brainamicsunclassified

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Desempeño de algoritmos de inteligencia artificial (IA) en problemas cognitivos con enfermos de alzheimer (EA)

Orovio¹,

Togra

Astudillo³

et al. 2023

Ciencia Latina

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El presente estudio aborda el análisis del Desempeño de algoritmos basados en Inteligencia Artificial (IA) para la ayuda de problemas cognitivos en enfermos de Alzheimer (EA), el estudio se lo realizo con a 5 adultos mayores de la Fundación Clemencia, Asilo “Sofía Ratinoff” situada al norte de la ciudad de Guayaquil. La metodología empleada fue mixta de tipo cualitativo y cuantitativo de carácter descriptivo y exploratorio al contexto ecuatoriano. El instrumento de medición fue el Test cognitivo “Mini Mental State Example” de los autores Folstein y McHugh (1975) con la respectiva adaptación por al contexto de parte del Ministerio de Inclusión Económica y Social (MIES, 2018) en base a la traducción de los autores Beaman, García, Villa (2003) el cual cuenta con cuatro dimensiones Orientación, Memoria inmediata, Atención, cálculo Lenguaje y praxis constructiva. Mediante el análisis del promedio por individuo obtenido en el Test se halló que el 60% de los pacientes presente un diagnóstico de tipo demencia grabe, y el 40% restante de pacientes está en un nivel de deterioro cognitivo leve o DCL. Tomando como criterio de validez el grado de aplicabilidad en áreas de memoria operativa e instantánea, cálculo, orientación y lenguaje, se tomó como referencia los siguientes softwares basados en algoritmo de IA siendo estos: Gradior, Smart Brain, Proyecto ELENA (Electronic cognitive stimulation). En base a estos resultados se elaboró la propuesta aplicando una Estrategia metodológica de intervención alternativa para enfermos con Alzheimer mediante el uso de Inteligencia Artificial (Gradior).

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“…Therefore, this study primarily focuses on CXR images. Deep convolutional neural networks (CNN) perform remarkably well when working with images [4]. ImageNet dataset models that are trained on 14 million images for 20,000 classes are extremely successful transfer learning models because of their quick, fast and accurate training processes.…”

Section: Introductionmentioning

confidence: 99%

MDEV Model: A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

Shaikh¹,

Siddiqui²,

Arain³

et al. 2023

Computer Systems Science and Engineering

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Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful; thus, catching it early is crucial. Medical physicians' time is limited in outdoor situations due to many patients; therefore, automated systems can be a rescue. The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists' experience. Therefore, radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays. In medical classifications, deep convolution neural networks are commonly used. This research aims to use deep pretrained transfer learning models to accurately categorize CXR images into binary classes, i.e., Normal and Pneumonia. The MDEV is a proposed novel ensemble approach that concatenates four heterogeneous transfer learning models: Mobile-Net, DenseNet-201, EfficientNet-B0, and VGG-16, which have been finetuned and trained on 5,856 CXR images. The evaluation matrices used in this research to contrast different deep transfer learning architectures include precision, accuracy, recall, AUC-roc, and f1-score. The model effectively decreases training loss while increasing accuracy. The findings conclude that the proposed MDEV model outperformed cutting-edge deep transfer learning models and obtains an overall precision of 92.26%, an accuracy of 92.15%, a recall of 90.90%, an auc-roc score of 90.9%, and f-score of 91.49% with minimal data pre-processing, data augmentation, finetuning and hyperparameter adjustment in classifying Normal and Pneumonia chests.

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Artificial Intelligence Based Memory Stash Alzheimer’s Aid

Cited by 7 publications

References 7 publications

Neuroprotective Potential of Caffeic Acid Phenethyl Ester (CAPE) in CNS Disorders: Mechanistic and Therapeutic Insights

Neuroprotective Potential of Caffeic Acid Phenethyl Ester (CAPE) in CNS Disorders: Mechanistic and Therapeutic Insights

Desempeño de algoritmos de inteligencia artificial (IA) en problemas cognitivos con enfermos de alzheimer (EA)

MDEV Model: A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

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