A Novel Deep Learning Model to Secure Internet of Things in Healthcare

Ahmad, Usman; Song, Hong; Bilal, Awais; Mahmood, Shahid; Alazab, Mamoun; Jolfaei, Alireza; Ullah, Asad; Saeed, Uzair

doi:10.1007/978-3-030-57024-8_15

Cited by 14 publications

(6 citation statements)

References 23 publications

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“…Normally, deep learning techniques are implemented to improve the performance to predict the security threats in IoT [2]. e PIMA dataset is used for disease prediction, which is subjected to pre-processing for eliminating the unwanted noises in the data.…”

Section: Internet Of Health Ingsmentioning

confidence: 99%

“…Usman Ahmad and others. [2] employed an artificial neural network (ANN) that effectively predicted the disease in the case of small datasets. Yet, this method is ineffective for larger datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(ii) It is challenging to combine the larger medical information in the form of websites or applications with higher detection accuracy [1]. (iii) One of the main challenges is to get higher disease prediction performance for larger datasets on which most of the classifier depends [2].…”

Section: Challengesmentioning

confidence: 99%

“…Widely known deep learning methods, such as recurrent neural networks (RNN), deep belief networks (DBN), convolutional neural networks (CNN), hybrid neural networks, and deep neural networks (DNN), are employed to process the health data. In the last few years, deep learning methods have gained attention in the field of IoT to enhance security by finding solutions to threats [2].…”

Section: Introductionmentioning

confidence: 99%

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Smart Healthcare: Disease Prediction Using the Cuckoo-Enabled Deep Classifier in IoT Framework

Kumar¹,

Reddy²,

Mohammad³

et al. 2022

Scientific Programming

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The Internet of Things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Presently, disease detection is performed using MRI images, X-rays, CT scans, and so on for diagnosing the diseases. The manual detection process is found to be time-consuming and may result in detection errors that affect the diagnosis. Hence, there is a need for an automatic system for which the deep learning methods gain a major interest. Hence, the idea to combine deep learning and disease prediction to effectively predict the disease is initiated. In this research, the deep learning method is combined with deep learning for the effective prediction of diseases, where the IoT network is employed in the data collection from the patients. The proposed cuckoo-based deep convolutional long-short term memory (deep convLSTM) classifier is employed for disease prediction, where the cuckoo search optimization is utilized for tuning the deep convLSTM classifier. The proposed method is compared with the conventional methods, and it achieved a training percentage of 97.591%, 95.874%, and 97.094%, respectively, for accuracy, sensitivity, and specificity. The comparative analysis proved that the proposed method obtained higher accuracy than other methods.

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Section: Internet Of Health Ingsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Smart Healthcare: Disease Prediction Using the Cuckoo-Enabled Deep Classifier in IoT Framework

Kumar¹,

Reddy²,

Mohammad³

et al. 2022

Scientific Programming

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“…The use of technology to assist people with emotion detection (Yoon et al, 2019) is a relatively growing area. Currently, most research has been performed on automating the recognition of facial expression from the video (Manogaran et al, 2019), spoken word from audio (Ahmad et al, 2021), written expression from text and physiology expressions from wearable devices (Mukhopadhyay et al, 2020). Emotion identification (Alazab,2020) from text is a recent essential research area in the field of natural language processing (NLP) and deep learning (Mamoun Alazab et al, 2019), which may reveal some valuable input for a variety of purposes.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Emotion Recognition Analysis in Text Streams

Liu

Kirubakaran

J.³

2022

International Journal of Technology and Human Interaction

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Social media sites employ various approaches to track feelings, including diagnosing neurological problems, including fear, in people or assessing a population public sentiment. One essential obstacle for automatic emotion recognition principles is variable with fluctuating limitations, language, and interpretation shifts. Therefore, in this paper, a deep learning-based emotion recognition (DL-EM) system has been proposed to describe the various relational effects in emotional groups. A soft classification method is suggested to quantify the tendency and allocate a message to each emotional class. A supervised framework for emotions in text streaming messages is developed and tested. Two of the major activities are offline teaching assignments and interactive emotion classification techniques. The first challenge offers templates in text responses to describe sentiment. The second activity includes implementing a two-stage framework to identify live broadcasts of text messages for dedicated emotion monitoring.

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