Development of an Integrate E-Medical System Using Software Defined Networking and Machine Learning

Ahmed, Brwa Khalil Abdullah; Ibrahim, Sura Khalil; Zahra, Musaddak Maher Abdul; Salih, Mohanad Ahmed; Jaleel, Refed Adnan

doi:10.14704/web/v19i1/web19224

Cited by 6 publications

(5 citation statements)

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“…To improve the security and stability of network communication, machine learning technology and SDN network were combined to ensure system security and stability, display and extract network characteristic data, and identify and screen risk data. The proposed model had good feasibility after comparing several risk detection models [11].…”

Section: Related Workmentioning

confidence: 93%

“…In equation (11), i p represents the probability of the protocol type. In the SDN framework, the characteristic data of DDoS attacks can also be extracted by analyzing the flow.…”

Section: ( )mentioning

confidence: 99%

“…Jing Zhang, Yong Ma, Yu Lu and Jun Wang. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] …”

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confidence: 99%

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ReliefF-RF model application in communication security risk detection of distribution network

Zhang,

Ma,

et al. 2023

Applied Mathematics and Nonlinear Sciences

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The construction of an intelligent power grid system has promoted innovation and development in the power grid industry, but the distribution network covers a wide range and has more access data, and the network attack risk is the focus of attention. In this regard, the stochastic forest model is introduced to build the distribution network risk detection model based on the software-defined network. The first is to study the power grid system based on a software-defined network, realize the analysis and extraction of the characteristics of abnormal power grid engineering data, and realize the diagnosis of risk data through a random forest model. At the same time, considering the long modeling time of the random forest model and the low classification accuracy in unbalanced samples, the feature selection model is introduced to optimize the random forest model, and the sampling weight function is set to improve the insufficient sampling accuracy of random forest model in small samples. The model classification effect is evaluated during the model performance test. The proposed model is the most accurate of the three data sets. In the three data classification tests of Satimage, Senbased, and Cleveland, the accuracy of MICRO Average is respectively 0.886, 0.986 and 0.856. At the same time, the proposed model is superior to other models in terms of training time and variance stability test. When the data set is 18000, the model accuracy is 0.968, which is better than the other two models. The research content has important reference value for maintaining communication security and improving system stability in distribution networks.

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Section: Related Workmentioning

confidence: 93%

“…In equation (11), i p represents the probability of the protocol type. In the SDN framework, the characteristic data of DDoS attacks can also be extracted by analyzing the flow.…”

Section: ( )mentioning

confidence: 99%

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ReliefF-RF model application in communication security risk detection of distribution network

Zhang,

Ma,

et al. 2023

Applied Mathematics and Nonlinear Sciences

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“…Pankaj et al [21] used a machine learning approach to diagnose diabetes using the M-Health application. Abed et al [22] introduced an integrated electronic medical system that enables efficient medical monitoring. Here, the machine learning based traffic flow classification model was utilized for reducing the time delay and traffic during data transmission/communication.…”

Section: Related Workmentioning

confidence: 99%

AI based Dynamic Prediction Model for Mobile Health Application System

Ramesh¹,

Subbaraya²,

Kumar³

2023

IJACSA

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In recent decades, mobile health (m-health) applications have gained significant attention in the healthcare sector due to their increased support during critical cases like cardiac disease, spinal cord problems, and brain injuries. Also, m-health services are considered more valuable, mainly where facilities are deficient. In addition, it supports wired and advanced wireless technologies for data transmission and communication. In this work, an Artificial Intelligence (AI)based deep learning model is implemented to predict healthcare data, where the data handling is performed to improve the dynamic prediction performance. It includes the working modules of data collection, normalization, AI-based classification, and decision-making. Here, the m-health data are obtained from the smart devices through the service providers, which comprises the health information related to blood pressure, heart rate, glucose level, etc. The main contribution of this paper is to accurately predict Cardio Vascular Disease (CVD) from the patient dataset stored in cloud using the AIbased m-health system. After obtaining the data, preprocessing can be performed for noise reduction and normalization because prediction performance highly depends on data quality. Consequently, we use the Gorilla Troop Optimization Algorithm (GTOA) to select the most relevant functions for classifier training and testing. Classify his CVD type according to a selected set of features using bidirectional long-term memory (Bi-LSTM). Moreover, the proposed AI-based prediction model's performance is validated and compared using different measures.

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“…[15] have indicated that emerging technology and advanced information technologies have opened new doors to innovation nding and enhancement for everlasting solutions to producing effective and resolute healthcare provision in our daily lives [15]. The spring in predictive systems has also been faced with challenges in the developmental stages which require improvement since the issues regarding Covid-19 are numerous and lack clear "standard operating procedures," in addressing it medically [16], [17]. This according [18] requires a potential to automate systems' tasks and improve in existing systems to be able to improve healthcare provision and patient care in general [19].…”

Section: Introductionmentioning

confidence: 99%

An adaptive ml model for covid-19 diagnosis in a smart hospital environment

Duodu,

Benuwa,

Techie-Menson

2023

Preprint

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The Covid-19 pandemic has since 2019 caused worldwide socio-economic unrest, fear, and panic among all individuals, nations, races, and continents thereby forcing governments to introduce This necessitated the integration of predictive models into the healthcare support system for effective diagnostic and prediction of Covid-19. The need for modeling existing models to provide satisfactory models, give a clear understanding of the existing model contribution and further improve these models has become significantly necessary since the lack of confidence in predictive health systems would slow the early diagnostics and detection of Covid-19 in the smart health environment and in the world at large. This study is an adaptive study to experiment with existing models to ascertain and confirm the effectiveness of the model and further attempt to improve the performance of existing models to give healthcare system designers the edge to build and increase the effectiveness of Covid-19 predictive systems in a smart hospital environment. The study model 3 separate Arthurs conducted to produce a real-time intelligent Covid-19 predictive model using dataset from the Kaggle dataset repository, which can be implemented in smart hospitals to help eliminate physical contact treatment by healthcare professionals, prevent long queues which lead to long waiting at the healthcare facility. The experimental result confirms the efficacy of the models proposed by the authors and a further moderation to implement the stacking ensemble classifier techniques outperformed the modeling studies by producing an accuracy result of 96.00% and scoring an error rate of 0.040 representing 4%, having 1% higher than previous studies which used random forest with an accuracy of 95%. The study, therefore, confirms and recommends the models by the previous Arthurs as effective predictive models for diagnosing and predicting COVID-19 in a smart hospital environment.

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Development of an Integrate E-Medical System Using Software Defined Networking and Machine Learning

Cited by 6 publications

References 0 publications

ReliefF-RF model application in communication security risk detection of distribution network

ReliefF-RF model application in communication security risk detection of distribution network

AI based Dynamic Prediction Model for Mobile Health Application System

An adaptive ml model for covid-19 diagnosis in a smart hospital environment

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