A sustainable IoHT based computationally intelligent healthcare monitoring system for lung cancer risk detection

Mishra, Sushruta; Thakkar, Hiren Kumar; Mallick, Pradeep Kumar; Tiwari, Prayag; Alamri, Atif

doi:10.1016/j.scs.2021.103079

Cited by 80 publications

(22 citation statements)

References 33 publications

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“…After data analysis, users are prompted to sit upright, and corresponding guidance schemes are given to relax body and mind and avoid excessive mental pressure [ 16 ]. Wang Feixiang and other scholars pointed out that smart textiles based on fiber sensors have great applications in the field of respiratory and heartbeat monitoring with high sensitivity, large dynamic response range, and outstanding antielectromagnetic interference performance by utilizing the principles of fiber macrobending and microbending [ 17 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Effect of Intelligent Vital Sign Monitoring System on Postoperative Nursing Care of Severe Patients

Zhang

Liu

2021

Contrast Media & Molecular Imaging

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In order to study the observation of postoperative vital sign data of critically ill patients, this paper developed a set of vital sign data acquisition system based on intelligence and realized the design of vital sign data acquisition system APP based on Android Studio programming software, which was used to realize the information collection of vital sign data of patients. PDA is connected with a vital sign measuring equipment through Bluetooth. The patient’s wristband is scanned with PDA to read the patient’s information, and then the measured vital sign data are obtained automatically by measuring APP. The initial alarm value is set to be greater than 1%, and it needs more than 60 and less than or equal to 120 RR interval data to judge apnea. Information collection of intelligent vital sign detection can not only save the time of nursing staff but also improve the nursing quality.

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Section: Literature Reviewmentioning

confidence: 99%

Effect of Intelligent Vital Sign Monitoring System on Postoperative Nursing Care of Severe Patients

Zhang

Liu

2021

Contrast Media & Molecular Imaging

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“…In recent years, there have been countless applications of machine learning [ 19 ] and reinforcement learning [ 20 ] in the diversified areas such as healthcare predictions [ 21 ], cloud resource management [ 22 ], and mobile robot navigation [ 23 ]. Moreover, a significant surge is also observed in cyber frauds, as well as the corresponding model to counter them, such as credit card fraud detection, telecom churn prediction [ 2 – 5 ], and detecting rare medical diseases.…”

Section: Related Workmentioning

confidence: 99%

Clairvoyant: AdaBoost with Cost-Enabled Cost-Sensitive Classifier for Customer Churn Prediction

Thakkar

Desai

Ghosh³

et al. 2022

Computational Intelligence and Neuroscience

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Customer churn prediction is one of the challenging problems and paramount concerns for telecommunication industries. With the increasing number of mobile operators, users can switch from one mobile operator to another if they are unsatisfied with the service. Marketing literature states that it costs 5–10 times more to acquire a new customer than retain an existing one. Hence, effective customer churn management has become a crucial demand for mobile communication operators. Researchers have proposed several classifiers and boosting methods to control customer churn rate, including deep learning (DL) algorithms. However, conventional classification algorithms follow an error-based framework that focuses on improving the classifier’s accuracy over cost sensitization. Typical classification algorithms treat misclassification errors equally, which is not applicable in practice. On the contrary, DL algorithms are computationally expensive as well as time-consuming. In this paper, a novel class-dependent cost-sensitive boosting algorithm called AdaBoostWithCost is proposed to reduce the churn cost. This study demonstrates the empirical evaluation of the proposed AdaBoostWithCost algorithm, which consistently outperforms the discrete AdaBoost algorithm concerning telecom churn prediction. The key focus of the AdaBoostWithCost classifier is to reduce false-negative error and the misclassification cost more significantly than the AdaBoost.

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“…Some significant existing works based on usage of smart sensors are presented. e usage of modern technologies for assisting users with vision impairments has generated positive reviews like the use of wearables [13], electronic traveling support [14], mobile assisting tools [15], machine vision-enabled models [16], haptics usage and design [17], substituting sensory equipment [18], and electronic navigation help [19]. With modelbased state-feedback control, a smart cane and obstacle detection device for visually impaired people with multiple sensors has been developed.…”

Section: Sensors Based Existingmentioning

confidence: 99%

Vision Navigator: A Smart and Intelligent Obstacle Recognition Model for Visually Impaired Users

Suman

Mishra

Sahoo

et al. 2022

Mobile Information Systems

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Vision impairment is a major challenge faced by humanity on a large scale throughout the world. Affected people find independently navigating and detecting obstacles extremely tedious. Thus, a potential solution for accurately detecting obstacles requires an integrated deployment of the Internet of Things and predictive analytics. This research introduces “Vision Navigator,” a novel framework for assisting visually impaired users in obstacle analysis and tracking so that they can move independently. An intelligent stick named “Smart-fold Cane” and sensor-equipped shoes called “Smart-alert Walker” are the main constituents of our proposed model. For object detection and classification, the stick uses a single-shot detection (SSD) mechanism, which is followed by frame generation using the recurrent neural network (RNN) model. Smart-alert Walker is a lightweight shoe that acts as an emergency unit that notifies the user regarding the presence of any obstacle within a short distance range. This intelligent obstacle detection model using the SSD-RNN approach was deployed in real time and its performance was validated in indoor and outdoor environments. The SSD-RNN model computed an optimum accuracy of 95.06% and 87.68% indoors and outdoors, respectively. The model was also evaluated in the context of users’ distance from obstacles. The proposed SSD-RNN model had an accuracy rate of 96.4% and 86.8% for close and distant obstacles, respectively, outperforming other models. Execution time for the SSD-RNN model was 4.82 s with the highest mean accuracy rate of 95.54% considering all common obstacles.

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A sustainable IoHT based computationally intelligent healthcare monitoring system for lung cancer risk detection

Cited by 80 publications

References 33 publications

Effect of Intelligent Vital Sign Monitoring System on Postoperative Nursing Care of Severe Patients

Effect of Intelligent Vital Sign Monitoring System on Postoperative Nursing Care of Severe Patients

Clairvoyant: AdaBoost with Cost-Enabled Cost-Sensitive Classifier for Customer Churn Prediction

Vision Navigator: A Smart and Intelligent Obstacle Recognition Model for Visually Impaired Users

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