Secure Cloud Storage for Medical IoT Data using Adaptive Neuro-Fuzzy Inference System

Mohiyuddin, Aqsa; Javed, Abdul Rehman; Chakraborty, Chinmay; Rizwan, Muhammad; Shabbir, Maryam; Nebhen, Jamel

doi:10.1007/s40815-021-01104-y

Cited by 79 publications

(36 citation statements)

References 32 publications

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“…To solve these challenges, many advanced artificial intelligence ( AI ) technologies [ 1 – 3 ] have been combined with healthcare to boost the availability of medical resources, such as applying pattern recognition methods on medical images [ 4 , 5 ] and leveraging natural language processing ( NLP) technologies to design medical chatbots [ 6 , 7 ]. The medical chatbot is mainly aimed to offer the medical assistants including disease identification, self-reports based medical suggestions for drugs, foods and checks, and medical front desk service guiding the patient to suitable healthcare service department, etc [ 8 , 9 ]. It has a significant potential to simplify the diagnostic process and relieve the cost of collecting information from patients.…”

Section: Introductionmentioning

confidence: 99%

MKA: A Scalable Medical Knowledge-Assisted Mechanism for Generative Models on Medical Conversation Tasks

Liang

Wu²,

Gu³

2021

Computational and Mathematical Methods in Medicine

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Using natural language processing (NLP) technologies to develop medical chatbots makes the diagnosis of the patient more convenient and efficient, which is a typical application in healthcare AI. Because of its importance, lots of researches have come out. Recently, the neural generative models have shown their impressive ability as the core of chatbot, while it cannot scale well when directly applied to medical conversation due to the lack of medical-specific knowledge. To address the limitation, a scalable medical knowledge-assisted mechanism (MKA) is proposed in this paper. The mechanism is aimed at assisting general neural generative models to achieve better performance on the medical conversation task. The medical-specific knowledge graph is designed within the mechanism, which contains 6 types of medical-related information, including department, drug, check, symptom, disease, and food. Besides, the specific token concatenation policy is defined to effectively inject medical information into the input data. Evaluation of our method is carried out on two typical medical datasets, MedDG and MedDialog-CN. The evaluation results demonstrate that models combined with our mechanism outperform original methods in multiple automatic evaluation metrics. Besides, MKA-BERT-GPT achieves state-of-the-art performance.

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Section: Introductionmentioning

confidence: 99%

MKA: A Scalable Medical Knowledge-Assisted Mechanism for Generative Models on Medical Conversation Tasks

Liang

Wu²,

Gu³

2021

Computational and Mathematical Methods in Medicine

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“…Many solutions have been presented to initially analyze the medical symptoms using constraint-oriented IoMT biosensors and support different services on emergency cases [7][8][9]. These medical sensors are directly communicated with a local coordinator and further collaborate with sink nodes to send health data to the public cloud [10][11][12]. However, the management of healthcare resources efficiently with the slower response time from medical experts is one of the critical research challenges.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient IoT e-Health Using Artificial Intelligence Model with Homomorphic Secret Sharing

et al. 2021

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Internet of Things (IoT) is a developing technology for supporting heterogeneous physical objects into smart things and improving the individuals living using wireless communication systems. Recently, many smart healthcare systems are based on the Internet of Medical Things (IoMT) to collect and analyze the data for infectious diseases, i.e., body fever, flu, COVID-19, shortness of breath, etc. with the least operation cost. However, the most important research challenges in such applications are storing the medical data on a secured cloud and make the disease diagnosis system more energy efficient. Additionally, the rapid explosion of IoMT technology has involved many cyber-criminals and continuous attempts to compromise medical devices with information loss and generating bogus certificates. Thus, the increase in modern technologies for healthcare applications based on IoMT, securing health data, and offering trusted communication against intruders is gaining much research attention. Therefore, this study aims to propose an energy-efficient IoT e-health model using artificial intelligence with homomorphic secret sharing, which aims to increase the maintainability of disease diagnosis systems and support trustworthy communication with the integration of the medical cloud. The proposed model is analyzed and proved its significance against relevant systems.

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“…Internet of Things (IoT)-based network systems (IoT-net) are considered as emerging advancements in the field of technology, where cloud network-based servers provide communication, storage, and problem-solving facilities, but these sorts of systems also contain security threats and issues as well (1)(2)(3)(4). The network user can access its facility by using an internet source (5).…”

Section: Introductionmentioning

confidence: 99%

“…Medical patient health records (PHR) are an emerging version of IT and smart healthcare records (5). However, here we encounter problems of data security and privacy of PHR (1,(14)(15)(16)(17)(18). To overcome the PHR security issue, many researchers have been working on using blockchain and many other artificial intelligence techniques, which allows clinical expertise, patients, laboratories, and the world to be connected.…”

Section: Introductionmentioning

confidence: 99%

“…One of the most effective artificial intelligence techniques is called the artificial neural inference system (ANFIS), which is the combination of an artificial neural network (ANN) and a fuzzy inference system (FIS). ANFIS is mainly used as a computational model for resolving uncertainties, reasoning, and reducing security threats from networks and cloud servers ( 1 ). It helps the systems and servers examine the risk of data and information.…”

Section: Introductionmentioning

confidence: 99%

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Trustworthy Intrusion Detection in E-Healthcare Systems

Akram

Zhao

et al. 2021

Front. Public Health

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In Internet of Things (IoT)-based network systems (IoT-net), intrusion detection systems (IDS) play a significant role to maintain patient health records (PHR) in e-healthcare. IoT-net is a massive technology with security threats on the network layer, as it is considered the most common source for communication and data storage platforms. The security of data servers in all sectors (mainly healthcare) has become one of the most crucial challenges for researchers. This paper proposes an approach for effective intrusion detection in the e-healthcare environment to maintain PHR in a safe IoT-net using an adaptive neuro-fuzzy inference system (ANFIS). In the proposed security model, the experiments present a security tool that helps to detect malicious network traffic. The practical implementation of the ANFIS model on the MATLAB framework with testing and training results compares the accuracy rate from the previous research in security.

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Secure Cloud Storage for Medical IoT Data using Adaptive Neuro-Fuzzy Inference System

Cited by 79 publications

References 32 publications

MKA: A Scalable Medical Knowledge-Assisted Mechanism for Generative Models on Medical Conversation Tasks

MKA: A Scalable Medical Knowledge-Assisted Mechanism for Generative Models on Medical Conversation Tasks

Energy-Efficient IoT e-Health Using Artificial Intelligence Model with Homomorphic Secret Sharing

Trustworthy Intrusion Detection in E-Healthcare Systems

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