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An IoT-Aware System for Managing Patients’ Waiting Time Using Bluetooth Low-Energy Technology

Computer Systems Science & Engineering 2022年第1期40卷 1-16页

作者： reham alabduljabbar Information Technology Department College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia

It is a common observation that whenever patients arrives at the front desk of a hospital,outpatient clinic,or other health-associated centers,they have to first queue up in a line and wait to fill in their registration form to get *** long waiting time without any status updates is the most common complaint,concerning health *** this paper,UrNext,a location-aware mobile-based solution using Bluetooth low-energy(BLE)technology is presented to solve the ***,a technology-oriented method,the Internet of Things(IoT),has been gaining popularity in helping to solve some of the healthcare sector’s *** implementation of this solution could be illustrated through a simple example of when a patient arrives at a clinic for a *** of having to wait in long lines,that patient will be greeted automatically,receive a push notification of an admittance along with an estimated waiting time for a consultation *** will not only provide the patients with a sense of freedom but would also reduce the uncertainty levels that are generally observed,thus saving both time and *** work aims to improve the clinics’quality of services,organize queues and minimize waiting times,leading to patients’comfort while reducing the burden on nurses and *** results demonstrate that the presented system is successful in its performance and helps achieves a plea-sant and conducive clinic visitation process with higher productivity.

关键词： Internetofthings(IoT) location-aware Bluetoothlowenergy beacon

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Intelligent Multiclass Skin Cancer Detection Using Convolution Neural Networks

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Computers, Materials & Continua 2021年第10期69卷 831-847页

作者： reham alabduljabbar Hala Alshamlan Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia

The worldwide mortality rate due to cancer is second only to cardiovascular *** discovery of image processing,latest artificial intelligence techniques,and upcoming algorithms can be used to effectively diagnose and prognose cancer faster and reduce the mortality *** applying these latest techniques has increased the survival chances during recent *** research community is making significant continuous progress in developing automated tools to assist dermatologists in decision *** datasets used for the experimentation and analysis are ISBI 2016,ISBI 2017,and HAM *** this work pertained models are used to extract the efficient *** pertained models applied are ResNet,InceptionV3,and classical feature extraction *** that,efficient preprocessing is conducted on dermoscopic images by applying various data augmentation ***,for classification,convolution neural networks were *** classify dermoscopic images on HAM 1000 Dataset,the maximum attained accuracy is 89.30%for the proposed *** other parameters for measuring the performance attained 87.34%(Sen),86.33%(Pre),88.44%(F1-S),and 11.30%false-negative rate(FNR).The class with the highest TP rate is 97.6%for Melanoma;whereas,the lowest TP rate was for the Dermatofibroma *** dataset ISBI2016,the accuracy achieved is 97.0%with the proposed classifier,whereas the other parameters for validation are 96.12%(Sen),97.01%(Pre),96.3%(F1-S),and further 3.7%(FNR).For the experiment with the ISBI2017 dataset,Sen,Pre,F1-S,and FNR were 93.9%,94.9%,93.9%,and 5.2%,respectively.

关键词： Convolution neural networks skin cancer artificial intelligence dermoscopy feature extraction classification

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