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Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

Computers, Materials & Continua 2023年第4期75卷 785-798页

作者： Kamalrulnizam Bin Abu Bakar Fatima Tul Zuhra Babangida Isyaku Fuad A.Ghaleb Faculty of Computing Universiti Teknologi MalaysiaJohor Bahru81310Malaysia Faculty of Science and Technology Shaheed Benazir Bhutto University Sanghar CampusPakistan Faculty of Computing and Information Technology Sule Lamido UniversityKafin HausaP.M.B.048Nigeria

The Internet of Medical Things (IoMT) emerges with the visionof the wireless body sensor network (WBSN) to improve the health monitoringsystems and has an enormous impact on the healthcare system forrecognizing the levels of risk/severity factors (premature diagnosis, treatment,and supervision of chronic disease i.e., cancer) via wearable/electronic healthsensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy playsa very significant role in the detection of gastric cancer. Convolutional Neuralnetwork (CNN) has been widely used to diagnose gastric cancer based onvarious feature extraction models, consequently, limiting the identificationand categorization performance in terms of cancerous stages and gradesassociated with each type of gastric cancer. This paper proposed an optimizedAI-based approach to diagnose and assess the risk factor of gastric cancerbased on its type, stage, and grade in the endoscopic images for smarthealthcare applications. The proposed method is categorized into five phasessuch as image pre-processing, Four-Dimensional (4D) image conversion,image segmentation, K-Nearest Neighbour (K-NN) classification, and multigradingand staging of image intensities. Moreover, the performance of theproposed method has experimented on two different datasets consisting ofcolor and black and white endoscopic images. The simulation results verifiedthat the proposed approach is capable of perceiving gastric cancer with 88.09%sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.

关键词： Artificial intelligence internet of things internet of medical things wireless body sensor network wireless endoscopic capsule gastric cancer

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Secured ECG Signal Transmission Using Optimized EGC with Chaotic Neural network in WBSN

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Computer Systems Science & Engineering 2023年第2期44卷 1109-1123页

作者： Ishani Mishra Sanjay Jain Vivek Maik Department of ECE New Horizon College of EngineeringBengaluru560103India CMR Institute of Technology Bengaluru560037India SRM University Chennai603203India

In wireless body sensor network(WBSN),the set of electrocardiogram(ECG)data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a *** transmit-ting these collected data some adversaries may capture and misuse it due to the compromise of ***,the major aim of this work is to enhance secure trans-mission of ECG signal in *** attain this goal,we present Pity Beetle Swarm Optimization Algorithm(PBOA)based Elliptic Galois Cryptography(EGC)with Chaotic Neural *** optimize the key generation process in Elliptic Curve Cryptography(ECC)over Galoisﬁeld or EGC,private key is chosen optimally using PBOA *** the encryption process is enhanced by presenting chaotic neural network which is used to generate chaotic sequences or cipher *** of this work show that the proposed cryptogra-phy algorithm attains better encryption time,decryption time,throughput and SNR than the conventional cryptography algorithms.

关键词： wireless body sensor network ECG pity beetle swarm optimization algorithm elliptic galois cryptography and chaotic neural network

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Patient Centered Real-Time Mobile Health Monitoring System

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E-Health Telecommunication Systems and networks 2016年第4期5卷 75-94页

作者： Won-Jae Yi Jafar Saniie Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago IL USA

In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.

关键词： Patient Remote Health Monitoring Real-Time sensor Data Processing wireless body sensor network Fall Detection Heart Monitoring

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