ADEPTNESS: Alzheimer’s Disease Patient Management System Using Pervasive Sensors - Early Prototype and Preliminary Results

Akhund, Tajim Md. Niamat Ullah; Mahi, Md. Julkar Nayeen; Tanvir, A. N. M. Hasnat; Mahmud, Mufti; Kaiser, M. Shamim

doi:10.1007/978-3-030-05587-5_39

Cited by 59 publications

(11 citation statements)

References 23 publications

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“…These organizations have been losing profits for the COVID-19 situation in Bangladesh. According to Bangladesh Tutor Providers' Association, the number of students seeking private tutors has declined by 70% compared to the pre-COVID-19 pandemic situation [7]. ZI Nixon, managing director of the Teacher's Café, has been running his organization for six years.…”

Section: Literature Reviewmentioning

confidence: 99%

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Covid-19 Effects on Private Tuition in Bangladesh and Internet of Things based support system

Akhund¹,

Newaz²,

Sarker³

2023

IJCDS

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Private tutoring is an important matter in Bangladesh. Many students take private tutor for various study. Many teachers and senior students become private tutor to get some extra income or primary income. During the COVID-19 pandemic time this profession and teaching status are changed in a great extent. This work makes a survey about private tuition among thousands of students and teachers from Dhaka city in Bangladesh. This work analyzed the collected data in multiple aspects and suggested some support system with the Internet of Things and Information Technology. The proposed IoT based system can help the students and teachers with remote monitoring. With the IoT based system the tutor and student both can check each other's physical condition from a remote place without affecting them with virus. The collected data shows a clear concept about the condition of private tuition and the IoT based solution worked successfully. The outcome of the system showed good results.

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Section: Literature Reviewmentioning

confidence: 99%

“…ZI Nixon, managing director of the Teacher's Café, has been running his organization for six years. But he is counting losses since the beginning of the pandemic in the country [7]. However, for the present situation, to ensure students' education their parents or guardians are willing to appoint private tuition online.…”

Section: Literature Reviewmentioning

confidence: 99%

Covid-19 Effects on Private Tuition in Bangladesh and Internet of Things based support system

Akhund¹,

Newaz²,

Sarker³

2023

IJCDS

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“…The perception layer is the lower layer where the sensors of the wearable band capture the raw data from the patient. An optical heart rate sensor, gyroscope, non-invasive blood glucose monitoring sensor, blood pressure monitoring sensor, flex sensor, temperature sensor, and GPS tracker are needed to build this band [11].…”

Section: Perception Layermentioning

confidence: 99%

Explainable AI-based Alzheimer’s Prediction and Management Using Multimodal Data

Jahan¹,

Taher²,

Kaiser³

et al. 2022

Preprint

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According to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is raising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer’s diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data. To solve these issues, this paper proposes a novel explainable Alzheimer’s disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, Freesurfer MRI segmentation data, and psychological data. For Alzheimer’s disease vs cognitively normal prediction, the random forest classifier provides 100% accuracy. Furthermore, Alzheimer’s disease and non-Alzheimer’s dementia should be classified properly because their symptoms are similar. To the best of our knowledge, we are the first to present a three-class classification on Alzheimer’s disease vs cognitively normal vs non-Alzheimer’s dementia and achieved 99.86% accuracy using an ensemble model. Besides, a novel Alzheimer’s patient management architecture is also proposed in this work..

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“…In recent years artificial intelligence (AI) has been applied in diverse problem domains to solve various challenging problems including student engagement [10], virtual reality exposure therapy [11], text classification [12,13,14,15], cyber security [16,17,18,19], neurological disease detection [20,21,22] and management [23,24,25,26,27,28], elderly care [29,30], biological data mining [31,32], fighting pandemic [33,34,35,36,37,38,39], and healthcare service delivery [40,41,42]. In particular, convolutional neural network (CNN) has recently accomplished remarkable achievements in Pathology Informatics [43,44,45] and Bioinformatics [46,47,48], suggesting that CNN has the ability to solve these challenges.…”

Section: Introductionmentioning

confidence: 99%

Detection of Osteosarcoma using Deep Feature Extraction with Federated Learning and MLP from Histopathological Images

Chowdhury,

Mahmud,

Elahe

et al. 2023

Preprint

Self Cite

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Background: The most common type of cancerous bone tumour is Osteosarcoma. Microscopic analysis of coloured slides is mostly used for Osteosarcoma diagnosis. As Osteosarcoma is highly heterogeneous, pathologists struggle to get a consensus diagnosis. Moreover, manual analysis of microscopic images is laborious, time-consuming, and vulnerable to bias. Method: In this research, a framework that extracts features based on CNN is developed to predict Osteosarcoma to address previous studies' performance gaps and shortcomings. The dataset used for this experiment is obtained from pathology archives at Children's Medical Center in Dallas, which includes 1144 histology glass slides. First, data augmentation is used to extend the dataset size. This study found Blur, Contrast, and Random augmentation perform well for the Osteosarcoma dataset. Next, we primarily considered six pre-trained transfer learning (TL) models and modified the models to extract features. Then, we examined feature selectors like Genetic Algorithm, Principal Component Analysis, Recursive Feature Elimination and Baruto and found PCA selects the optimal feature set. Finally, we fed the selected features into a fine-tuned MLP called Grid search MLP (GsMLP) and utilised federated learning (FL) to ensure the diversity and privacy of the dataset. Results: Experiments have revealed that the modified Xception model performs better in extracting features for Osteosarcoma prediction. Also, the proposed framework performed better than state-of-the-art techniques such as Convolutional Neural Network, VGG19, Random Forest, and AdaBoost with 98.20% accuracy and 97.5% Receiver Operating Characteristic - Area Under the Curve.

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ADEPTNESS: Alzheimer’s Disease Patient Management System Using Pervasive Sensors - Early Prototype and Preliminary Results

Cited by 59 publications

References 23 publications

Covid-19 Effects on Private Tuition in Bangladesh and Internet of Things based support system

Covid-19 Effects on Private Tuition in Bangladesh and Internet of Things based support system

Explainable AI-based Alzheimer’s Prediction and Management Using Multimodal Data

Detection of Osteosarcoma using Deep Feature Extraction with Federated Learning and MLP from Histopathological Images

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