At present times, the real-time requirement on the multiaccess healthcare monitoring system, information mining, and efficient disease diagnosis of health conditions is a difficult process. The recent advances in information technology and the internet of medical things (IoMT) have fostered extensive utilization of the smart system. A complex, 24/7 healthcare service is needed for effective and trustworthy monitoring of patients on a daily basis. To accomplish this need, edge computing and cloud platforms are highly required to satisfy the requirements of smart healthcare systems. This paper presents a new effective training scheme for the deep neural network (DNN), called ETS-DNN model in edge computing enabled IoMT system. The proposed ETS-DNN intends to facilitate timely data collection and processing to make timely decisions using the patterns that exist in the data. Initially, the IoMT devices sense the patient’s data and transfer the captured data to edge computing, which executes the ETS-DNN model to diagnose it. The proposed ETS-DNN model incorporates a Hybrid Modified Water Wave Optimization (HMWWO) technique to tune the parameters of the DNN structure, which comprises of several autoencoder layers cascaded to a softmax (SM) layer. The SM classification layer is placed at the end of the DNN to perform the classification task. The HMWWO algorithm integrates the MWWO technique with limited memory Broyden–Fletcher-Goldfarb-Shannon (L-BFGS). Once the ETS-DNN model generates the report in edge computing, then it will be sent to the cloud server, which is then forwarded to the healthcare professionals, hospital database, and concerned patients. The proposed ETS-DNN model intends to facilitate timely data collection and processing to identify the patterns exist in the data. An extensive set of experimental analysis takes place and the results are investigated under several aspects. The simulation outcome pointed out the superior characteristics of the ETS-DNN model over the compared methods.

/pdf/an-effective-training-scheme-for-deep-neural-network-in-edge-38ow5qsefm.pdf

An Effective Training Scheme for Deep Neural Network in Edge Computing Enabled Internet of Medical Things (IoMT) Systems

Utilizing IoT wearable medical device for heart disease prediction using higher order Boltzmann model: A classification approach

Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction

A dynamic and interoperable communication framework for controlling the operations of wearable sensors in smart healthcare applications

Holistic big data integrated artificial intelligent modeling to improve privacy and security in data management of smart cities

Future Internet of Things (FIoT) is a service concentric distributed architecture that is used by the smart city users for information sharing and access. The design of FIoT focuses in achieving reliable service and response to the growing user demands through different interoperability features. In this manuscript, concurrent service access and management framework is introduced to improve the swiftness in user concentric request processing. Based on the availability of the services and the density of the users, the concurrency in information access is provided to the users in a reliable manner. The framework incorporates convolution neural learning process in linear and differential manner for improving the access and service usage rates of the requesting users. The access sessions are differentiated for the accessible and offloaded requests to the available service providers based on the learning instances. The proposed framework is assessed using the metrics access rate, service usage rate, access delay, time lag, and failure ratio.

/pdf/concurrent-service-access-and-management-framework-for-user-4yc4qhczik.pdf

Concurrent service access and management framework for user-centric future internet of things in smart cities

Now-a-day’s brain abnormality is one among the dangerous neurological disorders that occurs because of the birth defects, brain stroke, brain injuries, genetic mutation and brain tumor. This brain disorder creates continue melancholia, bipolar disorder, stress disorder (PTSD) and so on. Due to this serious impact of the brain abnormalities, need to be identified within the beginning stage for eliminating the difficulties in humans day to day life. So, the automatic brain abnormality prediction process is created by utilizing electroencephalogram (EEG) for avoiding risk factor in future. As per the discussion, this paper introduces the evolutionary gravitational Neocognitron neural network(GNNN) for recognizing brain abnormalities with effective manner and it is especially suited for humans in war field. Initially, EEG signal is collected from patient; unwanted signal information is eliminated by using multi-linear principal component analysis from pre-processed signal, various features are extracted using affine invariant component analysis method and greedy global optimized features are chosen. The chosen features are analyzed using multi-layer virtual cortex model for predicting abnormal features. Finally the potency of the brain related abnormality prediction process developed using MATLAB tool and efficiency is examined using F-measure, Mathew correlation coefficient error rate, sensitivity, specificity, and accuracy. Along these lines the proposed framework effectively perceives the cerebrum variation from the norm with most astounding precision up to 99.48% with error rate.

Identifying brain abnormalities from electroencephalogram using evolutionary gravitational neocognitron neural network

Numerical Function Optimization in Brain Tumor Regions Using Reconfigured Multi-Objective Bat Optimization Algorithm

Design of proportional integral and derivative (PID) controller for a higher order discrete system is presented in this paper. The design and tuning of PID controller parameters proportional gain Kp, integral gain Ki and derivative gain Kd are complex and time consuming process for a higher order system. To overcome this, an approximate second order equivalent model is obtained for the higher order system which preserves the stability and retains the characteristics of original system. The proposed method of model reduction uses the advantages of Routh stability array and Particle swarm optimization (PSO). PID controller is designed using reduced order model and is cascaded with the original system. The performance specifications of the closed loop system with the designed controller are analysed. The proposed algorithm is illustrated through numerical examples.

	 

	Key words: Cumulative error index, model reduction, particle swarm optimisation, proportional integral and derivative (PID) controller, Routh array, stability.

Particle swarm optimisation based proportional integral and derivative (PID) controller design for linear discrete time system using reduced order model

/pdf/detection-of-cardiac-arrest-using-internet-of-things-2pry00ljty.pdf

Detection of Cardiac Arrest Using Internet of Things

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