Disaster situations either natural or made-man caused a large number of deaths and injured people. Morocco has experienced several disasters recently, the last one was the railway accident on 16 October 2018, which caused 127 serious injuries and 7 deaths. This large number was a big problem for the hospital to manage the received victims in right direction, which caused lives lost and disability. In this article, in collaboration with Mohammed (V) hospital in Casablanca city in Morocco, we suggested a solution that saves lives and eliminates number of disability by using a hybrid algorithm to size the hospital resources in the case of a massive influx of victims. We also suggested a support decision tool that is called Emergency Support Decision Tool. This helpful tool gives an idea about the needed resources that support these emergencies according to the victim’s number. The proposed solution consisted in making a hybrid algorithm that mixed the theoretical simulation process and the experience feedback by developing hybrid genetic and hybrid heuristic algorithms. These algorithms using as an input the matrix solutions that generated under ARENA software and the solution generated by neural networks that based on experiences feedback. The objective was to provide a solution based on available resources. In fact, the results showed that the hybrid heuristic algorithm is more performant than the hybrid genetic algorithm.
There has been increasing demand in improving service provisioning in hospital resources
management. Hospital industries work with strict budget constraint at the same time assures quality care.
To achieve quality care with budget constraint an efficient prediction model is required. Recently there has
been various time series based prediction model has been proposed to manage hospital resources such
ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider
the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The
issues with existing prediction are that the training suffers from local optima error. This induces overhead
and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient
inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to
evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed
model reduces RMSE and MAPE over existing back propagation based artificial neural network. The
overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in
improving the quality of health care management
Agent Oriented Patient Scheduling System: A Concurrent Metatem Based ApproachIJERA Editor
The Problem of Patient Scheduling[6] is a major issue in a Medical Healthcare System[4]. In India, Healthcare
is an 80 billion dollar Industry and is growing at an average rate of 17% annually. However, quality healthcare
is still out of reach for many. Each year thousands of fatalities arise simply due to the fact that patient could not
be provided with proper medical facilities at the right time. A software agent may be a member of a Multi-Agent
System[2][5] (MAS) which is collectively performing a range of complex and intelligent tasks. Using
Concurrent Metatem[3], a Multi-Agent Language, we have attempted to model a patient scheduling
system[4][6] that can help hospitals collaborate among them through a Liaison-Agent, in order to provide
patients with the best care possible. Patients should no longer have to be turned down when hospitals are packed
to capacity; instead, they could simply be shifted to another hospital. Hospitals and even doctors are assigned to
patients after an automated process of matching patient needs with doctor expertise and hospital infrastructureleading
to reduced waiting-time while maximizing efficiency and potentially saving lives.
Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...sushantparte
Research Project - The objective of this project is to predict the outcome of animals placed in shelters given features such as the animal’s age, breed, and colour. There are 5 possible outcomes for each animal with euthanasia being the worst outcome. The shelter hopes to be able to determine which animals are likely to be euthanized as well as find trends into what features increase the chance for adoption. This provides a chance for shelters to try to aid animals with a low chance of adoption. The overall goal is to decrease the yearly number of animals euthanized.
World population keeps growing up and injuries related death statistics is increasing. Optimizing healthcare logistic processes became then a vital need to lead patient cares to higher performances. Moreover, Worldwide healthcare systems
are facing the challenge of the sophistical facilities rising costs as well as patients’ requirement of high-quality care at lower cost. In the other hand, undetected behaviors of citizens and
environmental constraints are influencing the quality of
deployment which amplifying the response time threshold. In the
present paper, we regulate vehicles capacities to optimize patients picking for each incident nature. We proposed also a dynamic vehicle relocation and routing using a decision making processes. We are considering for each decision to take, the aspect of the variable emergency constraints influence to satisfy different scenarios of daily life.
There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.
Emergency Facilities Readiness Project Overview and Rationale .docxgidmanmary
Emergency Facilities Readiness Project
Overview and Rationale
This project is designed to provide you with hands-on experiences in performing simulation techniques – a method that students learn to use for decision-making purposes. You are asked to use simulation to evaluate the readiness and competency of a local emergency facilities.
Course Outcomes
This assignment is directly linked to the following key learning outcomes from the course syllabus:
CO1: Use descriptive, Heuristic and prescriptive analysis to drive business strategies and actions
CO3: Analyze the role of analytics in supporting decision making for various other stakeholder groups within and outside of your organization
Assignment Summary
University administers at a local University have recently launched several planning projects to determine how effectively local emergency facilities such as local hospitals can handle natural disasters (weather, fire,…). One of these projects focused on the transport of disaster victims from the campus to the five major hospitals in the area 1:
(i) Beth Israel Deaconess Medical Center
(ii) Tufts Medical Center
(iii) Massachusetts General Hospital
(iv) Boston Medical Center
(v) Brigham and Women’s Hospital
The project team would like to determine how many victims each hospital might expect in a disaster, and how long it would take to transport victims to the hospitals. However, because of lack of historical data on disasters in the area, the project team have decided to assume that the total number of victims is best approximated by a triangular probability distribution with a minimum and a maximum of respectively 20 and 300 victims, and with a peak of 80 victims.
The emergency facilities and capabilities at the five hospitals vary. The following table describes how victims will be distributed to the hospitals in the event of a disaster situation:
Hospital
Allocation of Disaster Victims
Beth Israel Medical
30%
Tufts Medical
15%
Massachusetts General
20%
Boston Medical
25%
Brigham and Women’s
10%
The proximity of the hospitals to the university campus also varies. It is estimated that the transport times to each of the hospitals is exponentially distributed with a certain average that depends on that hospital. The following table indicates the average of the transport time for each hospital.
Hospital
Average in Minutes
Beth Israel Medical
7
Tufts Medical
10
Massachusetts General
15
Boston Medical
15
Brigham and Women’s
20
For simplicity, we may assume that each hospital has two emergency vehicles, so that one leaves the university campus when the other one leaves the hospital. This means that one emergency vehicle arrives at the campus when the other arrives at the hospital. Therefore,
the total transport time for each hospital will be the sum of transporting each victim to that hospital.
Project Instructions:
1. Perform a simulation analysis consisting of 5,000 simulations to determine: a. Average ...
Advancing the cybersecurity of the healthcare system with self- optimising an...Petar Radanliev
This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing,
and adapting the healthcare system to cope with future pandemics. The core objective is to develop a concept healthcare
system supported by autonomous artificial intelligence that can use edge health devices with real-time data. The article constructs two case scenarios for applying cybersecurity with autonomous artificial intelligence for (1) self-optimising predictive cyber risk analytics of failures in healthcare systems during a Disease X event (i.e., undefined future pandemic), and (2) self-adaptive forecasting of medical production and supply chain bottlenecks during future pandemics. To construct the two testing scenarios, the article uses the case of Covid-19 to synthesise data for the algorithms – i.e., for optimising and securing digital healthcare systems in anticipation of Disease X. The testing scenarios are built to tackle the logistical challenges and disruption of complex production and supply chains for vaccine distribution with optimisation algorithms.
There has been increasing demand in improving service provisioning in hospital resources
management. Hospital industries work with strict budget constraint at the same time assures quality care.
To achieve quality care with budget constraint an efficient prediction model is required. Recently there has
been various time series based prediction model has been proposed to manage hospital resources such
ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider
the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The
issues with existing prediction are that the training suffers from local optima error. This induces overhead
and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient
inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to
evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed
model reduces RMSE and MAPE over existing back propagation based artificial neural network. The
overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in
improving the quality of health care management
Agent Oriented Patient Scheduling System: A Concurrent Metatem Based ApproachIJERA Editor
The Problem of Patient Scheduling[6] is a major issue in a Medical Healthcare System[4]. In India, Healthcare
is an 80 billion dollar Industry and is growing at an average rate of 17% annually. However, quality healthcare
is still out of reach for many. Each year thousands of fatalities arise simply due to the fact that patient could not
be provided with proper medical facilities at the right time. A software agent may be a member of a Multi-Agent
System[2][5] (MAS) which is collectively performing a range of complex and intelligent tasks. Using
Concurrent Metatem[3], a Multi-Agent Language, we have attempted to model a patient scheduling
system[4][6] that can help hospitals collaborate among them through a Liaison-Agent, in order to provide
patients with the best care possible. Patients should no longer have to be turned down when hospitals are packed
to capacity; instead, they could simply be shifted to another hospital. Hospitals and even doctors are assigned to
patients after an automated process of matching patient needs with doctor expertise and hospital infrastructureleading
to reduced waiting-time while maximizing efficiency and potentially saving lives.
Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...sushantparte
Research Project - The objective of this project is to predict the outcome of animals placed in shelters given features such as the animal’s age, breed, and colour. There are 5 possible outcomes for each animal with euthanasia being the worst outcome. The shelter hopes to be able to determine which animals are likely to be euthanized as well as find trends into what features increase the chance for adoption. This provides a chance for shelters to try to aid animals with a low chance of adoption. The overall goal is to decrease the yearly number of animals euthanized.
World population keeps growing up and injuries related death statistics is increasing. Optimizing healthcare logistic processes became then a vital need to lead patient cares to higher performances. Moreover, Worldwide healthcare systems
are facing the challenge of the sophistical facilities rising costs as well as patients’ requirement of high-quality care at lower cost. In the other hand, undetected behaviors of citizens and
environmental constraints are influencing the quality of
deployment which amplifying the response time threshold. In the
present paper, we regulate vehicles capacities to optimize patients picking for each incident nature. We proposed also a dynamic vehicle relocation and routing using a decision making processes. We are considering for each decision to take, the aspect of the variable emergency constraints influence to satisfy different scenarios of daily life.
There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.
Emergency Facilities Readiness Project Overview and Rationale .docxgidmanmary
Emergency Facilities Readiness Project
Overview and Rationale
This project is designed to provide you with hands-on experiences in performing simulation techniques – a method that students learn to use for decision-making purposes. You are asked to use simulation to evaluate the readiness and competency of a local emergency facilities.
Course Outcomes
This assignment is directly linked to the following key learning outcomes from the course syllabus:
CO1: Use descriptive, Heuristic and prescriptive analysis to drive business strategies and actions
CO3: Analyze the role of analytics in supporting decision making for various other stakeholder groups within and outside of your organization
Assignment Summary
University administers at a local University have recently launched several planning projects to determine how effectively local emergency facilities such as local hospitals can handle natural disasters (weather, fire,…). One of these projects focused on the transport of disaster victims from the campus to the five major hospitals in the area 1:
(i) Beth Israel Deaconess Medical Center
(ii) Tufts Medical Center
(iii) Massachusetts General Hospital
(iv) Boston Medical Center
(v) Brigham and Women’s Hospital
The project team would like to determine how many victims each hospital might expect in a disaster, and how long it would take to transport victims to the hospitals. However, because of lack of historical data on disasters in the area, the project team have decided to assume that the total number of victims is best approximated by a triangular probability distribution with a minimum and a maximum of respectively 20 and 300 victims, and with a peak of 80 victims.
The emergency facilities and capabilities at the five hospitals vary. The following table describes how victims will be distributed to the hospitals in the event of a disaster situation:
Hospital
Allocation of Disaster Victims
Beth Israel Medical
30%
Tufts Medical
15%
Massachusetts General
20%
Boston Medical
25%
Brigham and Women’s
10%
The proximity of the hospitals to the university campus also varies. It is estimated that the transport times to each of the hospitals is exponentially distributed with a certain average that depends on that hospital. The following table indicates the average of the transport time for each hospital.
Hospital
Average in Minutes
Beth Israel Medical
7
Tufts Medical
10
Massachusetts General
15
Boston Medical
15
Brigham and Women’s
20
For simplicity, we may assume that each hospital has two emergency vehicles, so that one leaves the university campus when the other one leaves the hospital. This means that one emergency vehicle arrives at the campus when the other arrives at the hospital. Therefore,
the total transport time for each hospital will be the sum of transporting each victim to that hospital.
Project Instructions:
1. Perform a simulation analysis consisting of 5,000 simulations to determine: a. Average ...
Advancing the cybersecurity of the healthcare system with self- optimising an...Petar Radanliev
This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing,
and adapting the healthcare system to cope with future pandemics. The core objective is to develop a concept healthcare
system supported by autonomous artificial intelligence that can use edge health devices with real-time data. The article constructs two case scenarios for applying cybersecurity with autonomous artificial intelligence for (1) self-optimising predictive cyber risk analytics of failures in healthcare systems during a Disease X event (i.e., undefined future pandemic), and (2) self-adaptive forecasting of medical production and supply chain bottlenecks during future pandemics. To construct the two testing scenarios, the article uses the case of Covid-19 to synthesise data for the algorithms – i.e., for optimising and securing digital healthcare systems in anticipation of Disease X. The testing scenarios are built to tackle the logistical challenges and disruption of complex production and supply chains for vaccine distribution with optimisation algorithms.
Potential of Artificial Intelligence in Removal of Medical Waste IIJSRJournal
With the inventive development of advanced technologies in the realm of medicine, new issues such as biomedical waste management are becoming more prevalent. Hazardous waste generated by hospitals must be managed in a timely manner, which can be accomplished with the use of computer science technology. The Biomedical Waste (BMW) problem is tackled using Artificial Intelligence, which takes route optimization into account. Route optimization is critical in BMW management because there are numerous risks connected with moving the BMW from the hospital to the depot (disposal site), such as traffic, vehicle malfunction. To avoid the harmful impacts of BMW on persons and the environment, the distance must be optimized. It can aid in the promotion of a healthy and risk-free lifestyle.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Usability evaluation of a discrete event based visual hospital management sim...hiij
Hospital Management is a complex and dynamic organisational challenge. Hospital managers (HMs)
are responsible for the effective use of valuable resources and assets, which is a significant issue in
healthcare. Due to factors such as the increase in health care costs and political pressure, HMs have
been compelled to examine new ways to improve efficiency and reduce healthcare delivery costs whilst
improving patient satisfaction. Healthcare managers require tools that will allow them to review the
current system or identify areas of improvement and quantify the possible changes.
This paper covers an evaluation of a hospital simulator developed by the authors. A usability test of the
simulator was carried out with hospital managers to provide real-world feedback on the simulator. This
has provided lessons to be applied in the development and use of such a tool. For instance, use of traffic
light colours in assisting management of hospital areas and Sensitivity Analysis supporting multiple or
more complex scenarios.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESamsjournal
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASES pijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT)
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Making India to a global healthcare hub, it is not only about bringing new technology but also we have to take care of the
existing technology. The healthcare hub is the leading factor for current economic growth of India. Human Factor Engineering
(HFE) plays a vital role in this field. In medical or healthcare, the field is named as Medical Human Factor Engineering (MHFE).
This paper discusses on how MHFE responsible for strengthen the Technology Management of Hospital, Hazards from device
failure and use related, Human Factors consideration in medical device use and case study on (Infusion Pumps) errors committed by
users in each clinical area. Now the challenging issue for HFE is to design a proper workspace to avoid human errors and the four
workspace design principles of Sanders & McCormick (1993) is also discussed. This paper deals with the Computer-aided-design
(CAD) systems and a failure mode and effects analysis (FMEA) technique with Simple Organizational Structure of HFE in designing the workspace.
The Case Study of an Early Warning Models for the Telecare Patients in TaiwanIJERA Editor
To propose a practical early warning analysis model for the telecare patients, this study applied data mining
technology as a basis to investigate the classification of patient groups by disease severity and incidence using
data contained in a telecare database regarding the number of a clinic. The ultimate purpose of this study was to
provide a new direction for telecare system planning and developing strategies.
The subject of this case study was a private clinic which is providing telecare system to patients in Taiwan, and
we used three data mining techniques including discriminant analysis, logistic regression and artificial neural
network to construct an early warning analysis model based on several factors such as: Demographic variables,
pathological signals, health management index, diagnosis and treatment records, emergency notification signal.
According the results, the telecare system can build stronger physician-patient relationship in advance through
previously paying attention to patients’ physiological conditions, reminding them to do self-management, even
taking them to the hospital for observation. A comparison of discriminative rates showed that the artificial neural
network model had the highest overall correct classification rate, 85.52%, and thus is a tool worthy of
recommendation
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
More Related Content
Similar to A hybrid algorithm to size the hospital resources in the case of a massive influx of victims
Potential of Artificial Intelligence in Removal of Medical Waste IIJSRJournal
With the inventive development of advanced technologies in the realm of medicine, new issues such as biomedical waste management are becoming more prevalent. Hazardous waste generated by hospitals must be managed in a timely manner, which can be accomplished with the use of computer science technology. The Biomedical Waste (BMW) problem is tackled using Artificial Intelligence, which takes route optimization into account. Route optimization is critical in BMW management because there are numerous risks connected with moving the BMW from the hospital to the depot (disposal site), such as traffic, vehicle malfunction. To avoid the harmful impacts of BMW on persons and the environment, the distance must be optimized. It can aid in the promotion of a healthy and risk-free lifestyle.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Usability evaluation of a discrete event based visual hospital management sim...hiij
Hospital Management is a complex and dynamic organisational challenge. Hospital managers (HMs)
are responsible for the effective use of valuable resources and assets, which is a significant issue in
healthcare. Due to factors such as the increase in health care costs and political pressure, HMs have
been compelled to examine new ways to improve efficiency and reduce healthcare delivery costs whilst
improving patient satisfaction. Healthcare managers require tools that will allow them to review the
current system or identify areas of improvement and quantify the possible changes.
This paper covers an evaluation of a hospital simulator developed by the authors. A usability test of the
simulator was carried out with hospital managers to provide real-world feedback on the simulator. This
has provided lessons to be applied in the development and use of such a tool. For instance, use of traffic
light colours in assisting management of hospital areas and Sensitivity Analysis supporting multiple or
more complex scenarios.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESamsjournal
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASES pijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT)
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Making India to a global healthcare hub, it is not only about bringing new technology but also we have to take care of the
existing technology. The healthcare hub is the leading factor for current economic growth of India. Human Factor Engineering
(HFE) plays a vital role in this field. In medical or healthcare, the field is named as Medical Human Factor Engineering (MHFE).
This paper discusses on how MHFE responsible for strengthen the Technology Management of Hospital, Hazards from device
failure and use related, Human Factors consideration in medical device use and case study on (Infusion Pumps) errors committed by
users in each clinical area. Now the challenging issue for HFE is to design a proper workspace to avoid human errors and the four
workspace design principles of Sanders & McCormick (1993) is also discussed. This paper deals with the Computer-aided-design
(CAD) systems and a failure mode and effects analysis (FMEA) technique with Simple Organizational Structure of HFE in designing the workspace.
The Case Study of an Early Warning Models for the Telecare Patients in TaiwanIJERA Editor
To propose a practical early warning analysis model for the telecare patients, this study applied data mining
technology as a basis to investigate the classification of patient groups by disease severity and incidence using
data contained in a telecare database regarding the number of a clinic. The ultimate purpose of this study was to
provide a new direction for telecare system planning and developing strategies.
The subject of this case study was a private clinic which is providing telecare system to patients in Taiwan, and
we used three data mining techniques including discriminant analysis, logistic regression and artificial neural
network to construct an early warning analysis model based on several factors such as: Demographic variables,
pathological signals, health management index, diagnosis and treatment records, emergency notification signal.
According the results, the telecare system can build stronger physician-patient relationship in advance through
previously paying attention to patients’ physiological conditions, reminding them to do self-management, even
taking them to the hospital for observation. A comparison of discriminative rates showed that the artificial neural
network model had the highest overall correct classification rate, 85.52%, and thus is a tool worthy of
recommendation
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
2. Int J Elec & Comp Eng ISSN: 2088-8708
A hybrid algorithm to size the hospital resources in the case of a massive ... (Abderrahmane Ben Kacem)
1007
The aim of this article is to propose a decision support tool that helps hospital managers to organize
and allocate the needs of human and material resources according to the number of victims. Sizing
the needed resources is frequently encountered in hospitals in order to optimize critical resources such as
operating rooms, beds, etc. [3]. Research studies interested in sizing hospital resources used different solving
approaches. Kadri et al [4] proposed a decision support system for the management of tension situations,
which allows the proactive steering of the Emergency Department (ED). Kao [5] used Markov's chains to
analyze the different patient flows in the care units and estimated their stay times at the hospital to calculate
the needed resources in care staff and hospital beds. Nouaouri et al [6] also proposed two integer linear
programming models to provide first the optimal number of operating rooms that allow the treatment of all
the victims, and then to determine the latest ready dates of surgical staffs. Lamiri et al [7] proposed
a stochastic model for operating room planning with two classes of patients: elective patients and emergency
patients. Cochran [8] used queuing theory to propose a model based on patient flows to size the emergency
department surface according to certain pre-established criteria. The simulation is also used in the ED to
adjust the number of medical teams by sizing the number of beds needed to accommodate patients [9].
Ridge et al [10] used simulation model for bed capacity planning in intensive care. John et al [11] described
a set of models for allocating the surgical resources in hospitals. Hans et al [12] proposed various
constructive heuristics and local search methods that use statistical information on surgery durations to
exploit the portfolio effect, and thereby to minimize the required slack. Fiedrich et al [13] introduced
a dynamic optimization model that detailed descriptions of both the operational areas and the available
resources to calculate the resource performance and efficiency for different tasks related to the response after
earthquake disasters.
Although a lot of tasks concerning the sizing of the hospital resources have appeared in
the literature, we have noticed that little of them have focused on resources sizing in disaster situations
generating a massive influx of victims who require urgent care [6]. Also, most of them interested in two
material resources: (operating rooms and beds). One exception is the study of Berquedich et al [14] which
used the artificial immune system as a decision support tool to manage the allocation of necessary resources
during a tension situation. This method is based on the affinity of each case with respect to the available
solutions to assign the most suitable one.
Our research aim is to suggest a solution for the decision maker in the Moroccan hospital to save
lives and eliminate disability in natural and human disasters situations using the following:
a. Reorganize the management process of victims.
b. Estimate the hospital human’s resources (doctors, nurses,) and material resources (beds, operating
rooms...) according to the number of victims and the available resources.
c. Make a decision support tool for the hospital managers.
In addition, this article will address the problem of lack of resources and propose solutions for
resizing emergency departments. These solutions consist in suggesting a hybrid heuristic algorithm that
improve another hybrid genetic algorithm (GA). These algorithms use two inputs:
a. The matrix of solutions proposed by simulation using OptQuest ARENA as theoretical needs according
the new proposed process.
b. The solution proposed by Neural Networks (NN) based on experiences feedback.
This study and simulations of our tasks was in collaboration with Mohammed (V) hospital in Casablanca city
in Morocco.
2. RESEARCH METHOD
The methodology used is a hybrid algorithms (GA and HA). This method is a blending between
the simulation (ARENA) and machine learning using NN. The simulation of our proposed process
contributed a matrix of theory solutions. This matrix is the first input for the hybrid algorithms. The machine
learning using NN is the tool with which we generate a solution that based on experiences feedback.
This solution is the second input as an evaluation vector for the hybrid algorithms. The choice of these
algorithms aims to have a best solution between the theoretical needs and the experiences feedback that
reflects the available resources, which applied in similar cases. The idea is to suggest a realistic solution
based on the available resources. This method have been recommended by Mohamed (V) hospital managers
because it combines theory and real practice (feedback) to produce a feasible and effective solution in order
to save the maximum number of victims within a reasonable time.
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 1, February 2020 : 1006 - 1016
1008
3. THE DIFFERENT LEVELS OF EXCEPTIONAL SITUATIONS
The massive influx of victims is the arrival of a large number of victims for the same cause,
or following different causes, except that in the first case the ED is supposed to receive in addition to
the victims, their families, the medias and of course the authorities. When there is a massive influx of victims
in a HC, the normal situation of the ED is transformed into a tension or a crisis. These levels of situations are
classified according to the charge/capacity ratio of the HC which defines the need in human and material
resources necessary to return to the normal situation [15].
The main factors that can affect the charge /capacity ratio are:
a. The factors related to the number of victims received by the HC.
b. The factors related to the skills of human resources (feedback on experiences, training and so on).
c. Internal and external transfer capacity (availability of downstream care services) [16].
4. PROCESS MANAGEMENT IN THE CASE OF A MASSIVE INFLUX OF VICTIMS
4.1. Current process management within the Mohammed V HPC
At the occurrence of a tension or exceptional situation, the plan called the Hospital Emergency Plan
(HEP) of Mohammed V HC consists to create a coordination and command team, led by the prefectural
authority. At the arrival of the victims, the team members start to receive, guide and register the victims.
Then, they begin the triage and orientation operation of victims for treatment according to their emergency
degrees (relative emergency, absolute emergency). Death Cases are placed in the morgue [2]. The Figure 1
summarizes the processes organization in the event of a massive influx of victims.
Figure 1. Organization chart with victim’s circuit applicable to crisis situations at CH Mohammed 5
Depending on the catastrophic (exceptional) situation, type and number of victims, the HEP
envisages two major scenarios:
a. Plan A is the case when the center receives either more than twenty victims in a serious case or fifty
victims of any severity.
b. Plan B is the case when the center receives either five serious victims requiring immediate treatment
(Such as surgical cases or respiratory distress) or ten victims of any severity.
According to these two scenarios, we noticed that the triage is done at the HC. In addition, there are two
levels of triage which the relative emergency and the absolute emergency, also there is no vision to predict
the needed resources according to the victim’s number.
4.2. Proposed process organization
4.2.1. Proposed triage of victims
The triage operation is a key step to better manage the victims handling, it identifies the order in
which victims should be evacuated and treated following the emergency degrees to save a maximum number
of victims. This operation determines:
a. The condition and the emergency degrees of victims.
b. The evacuation priority to the HC.
c. The degree of the treatment priority that should follow.
d. For this purpose, there are several scales and models for classifying victims [17].
4. Int J Elec & Comp Eng ISSN: 2088-8708
A hybrid algorithm to size the hospital resources in the case of a massive ... (Abderrahmane Ben Kacem)
1009
Taking into account these different triage scales [18-20] and in consultation with CH managers,
we proposed a five emergency levels model. At each level we have associated a color from the most urgent
case (red) to the less urgent case (blue), in order to save a large number of victims as possible and to save
time for the available resources. Our proposition suggests that the triage should be conducted at the incident
site to avoid prioritizing less urgent case [15]. The Table 1 summarizes the proposed triage model.
Table 1. Emergency levels from 1 to 5
Emergency levels Colors Status Recommended time frame
Dead victims Black Neurological Death impairment (coma, bilateral mydriasis) Morgue
1 : Immediate Red Failure of one vital function at least (neurological, cardiac, respiratory) Immediate
2 : Very urgent Orange Unstable victim (hemorrhage, CT, visceral damage, burns, gas poisoning) 5 Munites max
3 : Urgent Yellow Victim with a wound Musculoskeletal injury (fractures) 20 Munites
4 :Less urgent Green Stable victim no emergency but requires an assessment 60 Munites
5 : No-urgent Blue Can be treated on the accident site Treatment outside HC
4.2.2. Process organization
At the occurrence of an exceptional situation, the HC creates a local regulation post whose mission
is to communicate the information received to the Delegate, the Director of the HC and the heads of
departments (nursing, medical, administrative), also of the city's other HC. When a tension/ crisis situation
occurs, it is essential for us to applicate the EHP in order to reconfigure the HC before the arrival of the first
victims (possibly families, the media and the authorities). At the accident site, the triage operation starts, it is
done by an emergency doctor and emergency or state nurses. The goal is to identify first the most urgent
cases that need an immediate treatment and at the last time those that are less urgent. Non-urgent cases will
be handled on the accident site to do not clutter up the CH. If the capacity of the HC is reached,
the remaining victims must be oriented to the nearest HC.
The different step to manage an exceptional situation are as follows:
1) On the accident site:
a. Framing and distribution of the accident site.
b. Consulting and triage of victims according to the five levels proposed to better orient them.
c. Identifying the victims by the labels that indicate their emergency level.
d. Less urgent and non-urgent victims should be monitored.
2) Before the arrival of the victims at the HC:
a. The HC must be evacuated.
b. Prepare the operating rooms, beds, equipment and necessary materials to treat victims.
3) At the arrival of the victims at the HC:
a. Treat them following their emergency levels.
b. Allocate the necessary human and material resources for each case.
4) Communicate information in real time to the local regulation post in all steps of the process.
The Figure 2 summarizes the proposed process management:
Figure 1. Victim’s process management
5. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 1, February 2020 : 1006 - 1016
1010
5. IMPLEMENTATION AND RESULTS
Sizing of human and material resources is the second step after the triage operation, in this stage,
the HC managers should have a decision support tool that give them an idea about the number of resources
needed to treat victims. To this end, we proposed a three hybrid algorithms that combine between simulation
and experience feedback.
5.1. Sizing the hospital resources based on a simulation by ARENA software
The simulation is one of most used solution to solve sizing problems. In this regard, we used
ARENA software because it makes it possible to get a structure of the software model same to that of the real
system to be simulated. In addition, it has Opt Quest tool that gives us the opportunity for sizing resources.
The Figure 3 represents the ARENA model of our management process of victims.
Figure 2. The ARENA model of our proposed process management of victims
The configuration of the different block parameters was carried out in consultation with the HC
managers in order to capitalize on their experiences. So that, we used Arena software version 11,
the Capacity resources of the HC as a constraints and the number of victims as an input. The sizing of
the resources provided the results in Table 2. To get the best matrix of solution, we made many simulations.
The best solution is the line in blue. In order to improve these results, we used them as the input matrix of our
tow hybrid algorithms proposed in this article.
Table 2. Results of simulation by ARENA software
5.2. Sizing the hospital resources based on neural networks
In order to propose an effectiveness decision support tool, we used NN as tool of machine learning
to produce a solution based on experience feedback, this idea aims to refer to resources used in the real
experiences.
6. Int J Elec & Comp Eng ISSN: 2088-8708
A hybrid algorithm to size the hospital resources in the case of a massive ... (Abderrahmane Ben Kacem)
1011
5.2.1. Algorithm architecture
Figure 4 shown the neural network architecture.
Figure 4. Neural network architecture
5.2.2. Description of our algorithm
1. Collecting data from experience feedback.
2. Dividing the data into two groups, group A contains 70% of the data and group B contains 30% of
the data, this choice has been validated after several tests to help the NN to train well on the data.
3. Learning of NN in three steps, training step, test step and validation step.
4. In this proposition, the training and test phases were done by the group A data, for this we used
the algorithm of the gradient descent and the procedure of the K-FOLD cross validation [21]
5. Calculating of the performance indicators of the algorithm.
6. Displaying the solution.
The sizing of the resources contributed the following result in Table 3. The obtained result constitute
the evaluation vector for our three hybrid algorithms.
Table 3. Result of NN
Neural network solution (NN)
Ambulances Surgicals Nurses Nurses SR Beds Doctors Radiologs Reanimators Surgical rooms
13 13 13 10 102 19 5 5 4
5.3. Hybrid algorithms to size hospital resources
A hybrid algorithm is an algorithm that combines two or more other algorithms that solve the same
problem. The hybridization using GA used by a lot of researches to get the best optimal solution [22-26].
Although GA's are effective complete search algorithms with crossover and mutation operators, GA can be
improved using local search methods and they can be made competitive with others when the search space is
too large to explore. So if one uses some local optimization algorithm for making good blending between
global exploration and local exploitation.
The proposed solution consists in making a hybrid algorithm that mixes the theoretical simulation
process and the experience feedback by developing hybrid genetic and hybrid heuristic algorithms. These
algorithms using as an input the matrix solutions that generated under ARENA software and the solution
generated by neural networks that based on experiences feedback. We used the Manhattan distance to
measure of the similarity between these two inputs solutions, as we suggest one of the best solution.
7. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 1, February 2020 : 1006 - 1016
1012
5.3.1. Choice of Manhattan disatance
Manhattan distance is a function that measures the similarity between two vectors, Xi =[xi1, xi2, ...,
.., xi N] and Xj =[xj1, xj2, ..., xjD], producing a non-negative real number, representing the degree of
divergence between the two data points. It is the sum of the differences of their corresponding components.
The distance between a point x = (x1, x2... xn) and a point y = (y1, y2,... yn) is:
( ) ∑ | | (1)
The choice of Manhattan distance for the analysis of our data is based on the literature which shows
through the results obtained that Manhattan distance is the best the in terms of efficiency [27-29].
This is what prompted us to adopt this distance metric. In this article, we used the Manhattan distance to
measure the distance between the solutions simulated under ARENA and the evaluation vector calculated
under NN. The objective is to reproduce a new solution that has a minimum distance to approach between
theory and experience feedback.
5.3.2. Hybrid GA
GA have been developed by John Holland and his team. The goals of their research have been
twofold: (1) to abstract and rigorously explain the adaptive processes of natural systems, and (2) to design
artificial systems software that retains the important mechanisms of natural systems. This approach has lead
to important discoveries in both natural and artificial systems sciences [30].
a. Algorithm architecture
Figure 5 show hybrid genetic algorithm architecture.
Figure 5. Hybrid genetic algorithm architecture
b. Algorithm description
This algorithm used the decimal representation for genes, one point crossover, and uniform
mutation. Steps of the GA: The first step is to define the inputs. The evaluation vector of NN and the initial
population based on the proposed solutions of ARENA Software (matrix), each chromosome (solution or
individual) in the population will definitely have 9 genes, one gene for each resource. But the difficulty is to
find how many solutions per the initial population? There is no fixed value for that and we can select
the value that fits well with the number of victims. After preparing the initial population, the next steps
consists to calculate the fitness function, which allows selecting first the best individuals within the current
population as parents for mating. After, is to apply the GA variants (crossover and mutation) to produce
the new individuals of the next generation, creating then the new population by appending both parents and
new individuals, and repeating such steps for a number of iterations/generations. The algorithm will stop
when the fitness could not be optimized.
8. Int J Elec & Comp Eng ISSN: 2088-8708
A hybrid algorithm to size the hospital resources in the case of a massive ... (Abderrahmane Ben Kacem)
1013
5.3.3. Hybrid HA
a. Algorithm architecture
The hybrid heuristic algorithm architecture can see in Figure 6.
Figure 6. Hybrid heuristic algorithm architecture
b. Algorithm description
The basic idea is to hybrid the same inputs used in GA by a new HA using the Manhattan distance.
This algorithm is proposed in order to generate another solution better than the solution provided by GA.
Steps of the HA: The first step is to define the matrix of solutions based on the simulation under ARENA
Software, each vector (solution) in the matrix will definitely have 09 resources. Based on the fitness function,
we select 50% of the best solutions from the matrix, next is to calculate the difference between each resource
(element of the vector) in the matrix and its similar element in the evaluation vector, creating then a new
vector that contains the minimal values of each column, after creating the new solution which the sum of
the evaluation vector and the vector of the minimal values. The idea is to propose a new solution that has
more resources that near to the experience feedback solution.
5.3.4. Analysis and discussion
In order to validate our propositions, we implemented the two hybrid algorithms which use the same
inputs and display the desired results, then we have set up an application called Emergency Support Decision
Tool. The results are shown in Figure 7.
Figure 7. Results of three hybrid algorithm
9. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 1, February 2020 : 1006 - 1016
1014
5.4. Analysis and discussion of a result
After many simulations with different number of victims, the applied algorithm’s results shown in
the Figure 7 considering 127 victims (3 red, 28 orange, 44 yellow, 34 green and 18 blue). These results are
based on 12 hours of work per team with the following repartition in Table 4.
Table 4. Resources by stage
Operating room Observation room Putting into packaging Triage
Surgical+ Reanimator +
Nurses SR
Doctor + Reanimator +
Nurses
Doctor + Reanimator+
Radiolog
Doctor + Nurses
The performance indicators calculated are, the error (R² = 0.98) and the execution time
(T= 06 seconds with a microcomputer with the following characteristics: I5 of a 2.6G processor and 6G of
RAM). The idea of these algorithms consisted in finding the optimal number of resources that approaches
theory and feedback. The goal is to provide a solution that is first feasible according to available resources.
Second, it is in accordance with theoretical needs, in order to save the large number of lives.
The solutions proposed by simulation improved with the hybrid GA that provided a new one.
Although, this GA suggested number of resources less than the feedback of experiences. In consequence, we
proposed a new hybrid HA that provided a solution that approaching first between the theory and feedback of
experiences, next, it suggested number of resources more than the solution based on experience feedback.
By comparing the results of the two proposed algorithms, we concluded that the hybrid HA not only had an
effectiveness performance fitness, but also illustrated the idea that the number of resources should more or
equal than feedback of experiences. The result of the HA is more feasible than the results found
by [6, 14] considering the available resources in the Hospital Center.
6. CONCLUSION
Our research aim is to save handers of lives and eliminate thousands of disabilities in disaster
situations according the available resources in the hospital center. In fact, we proposed an improvement of
Moroccan disaster management plan (EHP) through three methods: (a) Reorganize the management process
of victims by proposing 5 levels of triage to avoid prioritizing less urgent case; (b) Estimate the hospital
humans and material resources according to the number of victims and the available resources using a Hybrid
Heuristic algorithm; (c) Make a decision support tool for the hospital managers that helps them to make an
efficient decision.
As we can see in the railway accident, they were 7 deaths and 127 injuries. If we used our solution,
we could save more lives and eliminate teens of disabilities. By using this hybrid algorithm, we can save
handers of lives and injuries in unsuspected disasters. In perspective, we will adopt this approach for a HC
network to treat the rest of victims in the case of a crisis situation (lack of capacity for one HC), especially
for more urgent cases that require immediate surgical procedures of operating rooms in a single HC is
limited. In the other hand, we will adopt a prognostic approach by using big data and machine learning to get
a system that predicts the number of victims expected especially for road accidents
REFERENCES
[1] OMS, “Six - year WHO strategic plan to reduce the impact of emergencies and disasters (in French),” 2019.
[2] Minister of Health, “Emergency Hospital Plan,” 2018.
[3] F. Jawab, et al., “Hospital Logistics Activities,” Proceedings of the International Conference on Industrial
Engineering and Operations Management, pp. 3228-3237, 2018.
[4] F. Kadri, et al., “Towards the Resilience of Hospital Emergency Services: Effective Management of Voltage
Situations (in French),” 2014.
[5] E. P. C. Kao, “Modeling the movement of coronary patients within a hospital by semi-Markov processes,”
Oper. Res., vol. 22, pp. 683-699, 1974.
[6] D. J. I. Nouaouri and J. Ch. Nicolas, “Disaster management in Hospital: sizingcritical resources,” J. Comput.,
vol. 8380, pp. 1-41, 2012.
[7] M. Lamiri, et al., “A stochastic model for operating room planning with elective and emergency demand for
surgery,” Eur. J. Oper. Res., vol. 185, pp. 1026-1037, 2008.
[8] J. K. Cochran and K. T. Roche, “A multi-class queuing network analysis methodology for improving hospital
emergency department performance,” Comput. Oper. Res., vol. 36, pp. 1497-1512, 2009.
[9] T. Wang, et al., “Modelling and simulation of emergency services with ARIS and Arena. case study: The
emergency department of Saint Joseph and Saint Luc hospital,” Prod. Plan. Control, vol. 20, pp. 484-495, 2009.
10. Int J Elec & Comp Eng ISSN: 2088-8708
A hybrid algorithm to size the hospital resources in the case of a massive ... (Abderrahmane Ben Kacem)
1015
[10] J. C. Ridge, et al., “Capacity planning for intensive care units,” Eur. J. Oper. Res., vol. 105, pp. 346-355, 1998.
[11] M. W. Carter and John T. Blake A., “A goal programming approach to strategic resource allocation in acute care
hospitals,” Eur. J. Oper. Res., vol. 140, pp. 541-561, 2002.
[12] E. Hans, et al., “Robust surgery loading,” Eur. J. Oper. Res., vol. 185, pp. 1038-1050, 2008.
[13] F. Fiedrich, et al., “Optimized resource allocation for emergency response after earthquake disasters,” Saf. Sci.,
vol. 35, pp. 41-57, 2000.
[14] M. Berquedich, et al., “Agile decision support system for the management of tensions in emergency services using
AIS techniques,” 2017 Int. Colloq. Logist. Supply Chain Manag. Compet. Innov. Automob. Aeronaut. Ind.
LOGISTIQUA 2017, pp. 118-123, 2017.
[15] A. B. Kacem, et al., “The reconfigurability of a hospital center facing a massive influx of victims,” 2018
International Colloquium on Logistics and Supply Chain Management, LOGISTIQUA 2018, vol. 0021266798,
pp. 105-110, 2018.
[16] M. Berquedich, et al., “Organizational methodology of processes in an uncertain and disturbed environment:
Application to the hospital domain (in French),” Xème Conférence Internationale: Conception et Production
Intégrées, 2015.
[17] A. Armand-perroux, “Triage in emergency structure Formalized recommendations of experts (in French),” 2013.
[18] Agency for Healthcare Research and Quality, “di Ne ric w Emergency Severity Index ( ESI ),” Comput. Methods
Programs Biomed., vol. 117, pp. 61-70, 2012.
[19] J. Windle, “Manchester Triage System: A global solution,” 2013.
[20] M. J. Bullard, et al., “Revisions to the Canadian Emergency Department Triage and Acuity Scale (CTAS)
Guidelines 2016,” Can. J. Emerg. Med., vol. 19, pp. S18-S27, 2017.
[21] R. Kohavi, “Cross validation number,” vol. 5, 1995.
[22] A. N. Fauziyah and W. F. Mahmudy, “Hybrid Genetic Algorithm for Optimization of Food Composition
on Hypertensive Patient,” International Journal of Electrical and Computer Engineering (IJECE), vol. 8(6),
pp. 4673-4683, 2018.
[23] A. K. Ariyani, et al., “Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Routing
Problem with Time Windows,” International Journal of Electrical and Computer Engineering (IJECE), vol. 8(6),
pp. 4713-4723, 2018.
[24] J. H. Goldberg and D. E. Holland, “GUEST EDITORIAL Genetic Algorithms and Machine Learning,” Mach.
Learn., vol. 3, pp. 95-99, 1988.
[25] T. Elmihoub, et al., “Performance of Hybrid Genetic Algorithms Incorporating Local Search,” Proc. 18th Eur.
Simul. Multiconference, vol. 4, pp. 154-160, 2004.
[26] A. Konak and A. E. Smith, “A hybrid genetic algorithm approach for backbone design of communication
networks,” Proc. 1999 Congr. Evol. Comput. CEC 1999, vol. 3, pp. 1817-1823, 1999.
[27] D. S. S. K. M. Ponnmoli, “Analysis of Face Recognition using Manhattan Distance Algorithm with Image
Segmentation,” vol. 3, pp. 18-27, 2014.
[28] R. Paredes, et al., “Pattern recognition and image analysis,” 7th Iberian conference, IbPRIA 2015 Santiago de
Compostela, Spain, june 17–19, 2015 proceedings, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif.
Intell. Lect. Notes Bioinformatics), vol. 9117, 2015.
[29] O. David, “Comparative Effect of Distance Metrics on Selected Texture Features for Content-Based Image
Retrieval System,” Journal of Information Engineering and Applications, vol. 4, pp. 29-36, 2014.
[30] J. B. Song and D. E. Hardt, “Estimation of weld bead depth for in-process control,” American Society of
Mechanical Engineers, Dynamic Systems and Control Division (Publication) DSC, vol. 22. pp. 39-45, 1990.
BIOGRAPHIES OF AUTHORS
Abderrahmane Ben Kacem is a phd candidate in logistics at the Abdelmalek Essaadi
University, Tangier, Morocco his resaerch intersts focus on hospital logistics. He received
the logistics engineering master degree from hassan 2 university of casablanca in 2015.
Oulaid Kamach is a full professor in Industrial Engineering and logistics at Abdelmalek
ESSAADI University. He is a Head of High Master study on Management and monitoring of
Industrial system. He received his Ph.D in 2004 in science of engineering from INSA in Lyon
France, and holds DEA degrees in applied mathematics form the Besancon Franche Comte
university, France.
11. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 1, February 2020 : 1006 - 1016
1016
Samir Chafik received his engineering degree in industrial computer science from EMSI of
Casablanca, Morocco, in 1993. He received the DEA (M.S.) and Ph.D in automatic control
from INSA of Lyon in 1996 and 2000 respectively. Since 2000, he has been a teacher in
the Industrial Engineering Department and a member of the AMPERE laboratory of the INSA
of Lyon. He joined EMSI of Casablanca in 2013 where he is currently an assistant Professor.
He is a member of both the LRP laboratory of the EMSI Casablanca and the LTI laboratory of
Abdelmalek Saadi University in Tangier, Morocco. His current research interests include Petri
nets modelling, diagnosis and prognosis of failures, health logistics and artificial intelligence.
Mohammed Ait Hammou is a doctor and a Head of Emergency Department, Mohammed V
Hospital Center Casablanca, Morocco. He holds a doctorate in medicine from the Faculty of
Medicine and Pharmacy of Rabat in 1993 and a certificate in emergency medicine from
the Faculty of Medicine and Pharmacy of Casablanca in 2005, he held several positions such
as Acting Director of the AL HASSANNI prefectoral hospital centre and Chief Medical
Officer of the Medical Affairs Department of the Mohammed V