Hospital is a very important institution to provide health care for people. It is not surprising that nowadays the people's demands for hospital is increasing.. However, due to the rising cost of healthcare services, hospitals need to consider efficiencies in order to overcome these two problems. This paper deals with an integrated strategy of staff capacity management and bed allocation planning to tackle these problems. Mathematically, the strategy can be modeled as an integer linear programming problem. We solve the model using a direct neighborhood search approach, based on the notion of superbasic variables.

https://iopscience.iop.org/article/10.1088/1757-899X/300/1/012016/pdf

Optimization Model for Capacity Management and Bed Scheduling for Hospital

https://iopscience.iop.org/article/10.1088/1742-6596/1361/1/012069/pdf

Smart Health Model With A Linear Integer Programming Approach

It has been said that dynamic guard channel (DGC) assignment scheme based on traffic intensities averages QoS and performs better than the prioritized guard channel assignment scheme with queue (QPGC) during traffic congestion. This work has extended the investigation to DGC with queue (QDGC) and carried out its comparison with other call traffic channel assignment schemes. Mathematical analysis of the models was done using Markov chain and simulation was carried out in MATLAB. When traffic arrival rates were symmetric, the QPGC had the lower call blocking probability than the QDGC but became the same when the system was congested. However, the performance of the two queuing schemes was the same when handover call traffic was more than new calls traffic.

Performance Evaluation of Dynamic Guard Channels Assignment with Queue and Prioritized Schemes

Proposed algorithm for smart grid DDoS detection based on deep learning

Prioritized guard channel (PGC) assignment with queue was designed to reduce call dropping probability associated with a base station congested with handover call traffic. The Markov chain was used in the analysis of the PGC scheme and queuing discipline was FIFO for a PGC+MAHO scheme. Simulation was carried out using MATLAB. The results showed that increasing buffer size reduces call dropping probability which becomes discontinuous at some value of traffic arrival rate for each buffer size. It was found out that some queuing parameters such as queue product form becomes undefined at an arrival rate for a large buffer size. This limits the extent to which buffer size can be increased. System computational speed was also a contributory factor.

Effect of Increasing Buffer Size on Prioritized Guard Channels with Queue during Call Traffic Congestion

In cellular networks, there exist fixed and dedicated channel for the entire duration of a set up call. The signal strength and interference at various locations differs within a cell and this affects the quality of the on-going call including handoff. This may require the transfer of the on-going call to another cell with a better signal strength. Given a fixed channel at a cell site, and assuming the arrivals of originating calls and handoff requests to be Poissonian, either process could result in queuing. This paper by using Matlab seeks to evaluate in terms of probability of blocking, which queuing system is more suitable when either or both priority are given and when no priority is given. It is observed that, for cell sites with traffic intensity to channel ratio of 0 - 0.75 which is considered as not congested, a system of either queuing the originating or handoff calls can be employed. For a congested system in which the traffic intensity to channel ratio is 0.76 - 1 there should be separate queuing of both the originating calls and the handoff calls. When the cell site is very congested that is, having a traffic intensity to channel ratio of greater than 1, queuing of handoff calls provides the best network optimization.

/pdf/performance-analysis-of-queuing-priority-schemes-in-cellular-4sla5vuut3.pdf

Performance Analysis of Queuing Priority Schemes in cellular communication

3 Abstract: The recent demand for higher data rate services from wireless network users is overwhelming. Social media influx as well as the proliferation of broadband enabled smart-phones, tablet computers and other newly improved wireless devices has erupted a new trend in wireless network traffic need where average capacity and speed is no longer appreciable. In order to cope with this trend in traffic requirement, wireless network operators are considering a gradual rollover of an existing third generation (3G) network to a fourth generation (4G) network with orthogonal frequency division multiple access (OFDMA) based technologies such as Fourth Generation Long Term Evolution (4G LTE) and Worldwide Interoperability for Microwave Access (WiMAX). This paper is devoted to the performance evaluation of Adaptive Modulation and Coding (AMC) in downlink of an orthogonal frequency division multiple access (OFDMA) network, considering Partial Usage of Sub-channels (PUSC). By using MATLAB Simulink and Origin 61, the performance of Bit Error Rate (BER) and Spectral Efficiency in two channel environments, i.e. non-fading and fading channels were examined. Results suggest better performance for AMC over individual MCS in all channel environments. Moreover, non-fading Addictive White Gaussian Noise (AWGN) channels significantly perform better than fading (Rayleigh and Rician) channels. In Rician channel environment, however, flat fading Rician channels perform better than frequency selective Rician channel which interestingly records a degraded performance against Rayleigh channels. Keywords: third generation (3G), fourth generation (4G), Orthogonal Frequency Division Multiple Access (OFDMA), Long Term Evolution (LTE), Worldwide Interoperability for Microwave Access (WiMAX), Bit Error Rate (BER), Adaptive Modulation and Coding (AMC), Partial Usage of Sub-channels (PUSC), Rayleigh channels and Rician channels

/pdf/performance-analysis-of-adaptive-modulation-and-coding-1tdc09ehxr.pdf

Performance Analysis of Adaptive Modulation and Coding Schemes on OFDMA Physical Layer

Abstract Supervisory Control and Data Acquisition (SCADA) systems play an important role in Smart Grid. Though the rapid evolution provides numerous advantages it is one of the most desired targets for malicious attackers. So far security measures deployed for SCADA systems detect cyber-attacks, however, the performance metrics are not up to the mark. In this paper, we have deployed an intrusion detection system to detect cyber-physical attacks in the SCADA system concatenating the Convolutional Neural Network and Gated Recurrent Unit as a collective approach. Extensive experiments are conducted using a benchmark dataset to validate the performance of the proposed intrusion detection model in a smart metering environment. Parameters such as accuracy, precision, and false-positive rate are compared with existing deep learning models. The proposed concatenated approach attains 98.84% detection accuracy which is much better than existing techniques. 

/pdf/on-the-performance-metrics-for-cyber-physical-attack-1x30yy84.pdf

On the performance metrics for cyber-physical attack detection in smart grid

Supervisory Control and Data Acquisition system linked to Intelligent Electronic Devices over a communication network keeps an eye on smart grids’ performance and safety. The lack of algorithms protecting the power system communication protocols makes them vulnerable to cyberattacks, which can result in a hacker introducing false data into the operational network. This can result in delayed attack detection, which might harm the infrastructure, cause financial loss, or even result in fatalities. Similarly, attackers may be able to feed the system with fake information to hoax the operator and the algorithm into making bad decisions at crucial moments. This paper attempts to identify and classify such cyber-attacks by using numerous deep learning algorithms and optimizing the data features with a metaheuristic algorithm. We proposed a Restricted Boltzmann Machine-based nature-inspired artificial root foraging optimization algorithm. Using a publicly available dataset produced in Mississippi State University’s Oak Ridge National Laboratory, simulations are run on the Jupiter Notebook. Traditional supervised machine learning algorithms like Artificial Neural Networks, Convolutional Neural Networks, and Support Vector Machines are measured with the proposed algorithm to demonstrate the effectiveness of the algorithms. Simulations show that the proposed algorithm produced superior results, with an accuracy of 97.8% for binary classification, 95.6% for three-class classification, and 94.3% for multi-class classification. Thereby outperforming its counterpart algorithms in terms of accuracy, precision, recall, and f1 score.

Sayawu Yakubu Diaba

Papers

Performance Analysis of Queuing Priority Schemes in cellular communication

Proposed algorithm for smart grid DDoS detection based on deep learning

Performance Analysis of Adaptive Modulation and Coding Schemes on OFDMA Physical Layer

On the performance metrics for cyber-physical attack detection in smart grid

Cyber Security in Power Systems Using Meta-Heuristic and Deep Learning Algorithms