LungNet: A hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data

Faruqui, Nuruzzaman; Yousuf, Mohammad Abu; Whaiduzzaman, Md; Azad, A. K. M.; Barros, Alistair; Moni, Mohammad Ali

doi:10.1016/j.compbiomed.2021.104961

Cited by 123 publications

(50 citation statements)

References 35 publications

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“…CNN ( 37 , 38 ) is a type of feedforward neural network designed to deal with deep network configurations. In CNN design, there are three levels layers: (1) Convolution layer: Translation inversion is provided by a convolutional layer.…”

Section: Proposed Methodologymentioning

confidence: 99%

Novel Internet of Things based approach toward diabetes prediction using deep learning models

Naseem

Habib

Naz

et al. 2022

Front. Public Health

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The integration of the Internet of Things with machine learning in different disciplines has benefited from recent technological advancements. In medical IoT, the fusion of these two disciplines can be extremely beneficial as it allows the creation of a receptive and interconnected environment and offers a variety of services to medical professionals and patients. Doctors can make early decisions to save a patient's life when disease forecasts are made early. IoT sensor captures the data from the patients, and machine learning techniques are used to analyze the data and predict the presence of the fatal disease i.e., diabetes. The goal of this research is to make a smart patient's health monitoring system based on machine learning that helps to detect the presence of a chronic disease in patient early and accurately. For the implementation, the diabetic dataset has been used. In order to detect the presence of the fatal disease, six different machine learning techniques are used i.e., Support Vector Machine (SVM), Logistic Regression, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The performance of the proposed model is evaluated by using four evaluation metrics i.e., accuracy, precision, recall, and F1-Score. The RNN outperformed remaining algorithms in terms of accuracy (81%), precision (75%), and F1-Score (65%). However, the recall (56%) for ANN was higher as compared to SVM and logistic regression, CNN, RNN, and LSTM. With the help of this proposed patient's health monitoring system, doctors will be able to diagnose the presence of the disease earlier.

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Section: Proposed Methodologymentioning

confidence: 99%

Novel Internet of Things based approach toward diabetes prediction using deep learning models

Naseem

Habib

Naz

et al. 2022

Front. Public Health

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“…IoT based systems are helping people to make cheap irrigation systems [30], Seamless Microservice Execution [31], Big Data Processing [32] and blockchain technology [33]. IoT is helping lung cancer diagnosis [34]. Fog computing plays a vital role for augmenting resource utilization [35].…”

Section: Literature Reviewmentioning

confidence: 99%

IoT based Low-cost Posture and Bluetooth Controlled Robot for Disabled and Virus Affected People

Akhund¹,

Hossain²,

Kubra³

et al. 2022

IJACSA

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IoT-based robots can help people to a great extent. This work results in a low-cost posture recognizer robot that can detect posture signs from a disabled or virus-affected person and move accordingly. The robot can take images with the Raspberry Pi camera and process the image to identify the posture with our designed algorithm. In addition, it can also take instructions via Bluetooth from smartphone apps. The robot can move 360 degrees depending on the input posture or Bluetooth. This system can assist disabled people who can move a few organs only. Moreover, this system can assist virus-affected persons as they can instruct the robot without touching it. Finally, the robot can collect data from a distant place and send it to a cloud server without spreading the virus.

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“…Using an Internet of Things (IoT) architecture, Convolution Neural Networks (CNN) are utilised to categorise strokes from CT scans to distinguish between a healthy brain and an ischaemic or haemorrhagic stroke [ 48 ]. Given the incidence of lung malignancies, attention has been directed to the possibility of deep reinforcement learning for early lung cancer diagnosis [ 49 ].…”

Section: Iot and Machine Learningmentioning

confidence: 99%

A Review of Emerging Technologies for IoT-Based Smart Cities

Whaiduzzaman

Barros

Chanda

et al. 2022

Sensors

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Smart cities can be complemented by fusing various components and incorporating recent emerging technologies. IoT communications are crucial to smart city operations, which are designed to support the concept of a “Smart City” by utilising the most cutting-edge communication technologies to enhance city administration and resident services. Smart cities have been outfitted with numerous IoT-based gadgets; the Internet of Things is a modular method to integrate various sensors with all ICT technologies. This paper provides an overview of smart cities’ concepts, characteristics, and applications. We thoroughly investigate smart city applications, challenges, and possibilities with solutions in recent technological trends and perspectives, such as machine learning and blockchain. We discuss cloud and fog IoT ecosystems in the in capacity of IoT devices, architectures, and machine learning approaches. In addition we integrate security and privacy aspects, including blockchain applications, towards more trustworthy and resilient smart cities. We also highlight the concepts, characteristics, and applications of smart cities and provide a conceptual model of the smart city mega-events framework. Finally, we outline the impact of recent emerging technologies’ implications on challenges, applications, and solutions for futuristic smart cities.

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LungNet: A hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data

Cited by 123 publications

References 35 publications

Novel Internet of Things based approach toward diabetes prediction using deep learning models

Novel Internet of Things based approach toward diabetes prediction using deep learning models

IoT based Low-cost Posture and Bluetooth Controlled Robot for Disabled and Virus Affected People

A Review of Emerging Technologies for IoT-Based Smart Cities

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