Influence-Based Nano Fuzzy Swarm Oxygen Deficiency Detection and Therapy

Raz, Nasibeh Rady; Akbarzadeh-T, Mohammad-R.; Setayeshi, Saeed

doi:10.1109/tsmc.2023.3252899

Cited by 2 publications

(3 citation statements)

References 53 publications

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“…In this section, we propose a feasibility study on nano fuzzy alarming system (NFABT) for blood transfusion requirement. Previously, inspired by red blood cells 24 and cancer cell scaffold 25 , we proposed bio-inspired nanomachines for cancer drug delivery 26 , and oxygen deficiency cases 7 . Here, utilizing red blood cell-inspired nanomachines, we propose a nano-alarming system for blood transfusion.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…We previously in Ref. 7 proved that using triangular MFs, the sum of the multiplication of FBFs is equivalent to a common fuzzy system composed of a singleton fuzzifier, centroid defuzzifier, and product inference engine. Here, applying the proof, we use ( 2 ) for designing our fuzzy system.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Also, an online monitoring and oxygen delivery system using a swarm of nanomachines is discussed in Ref. 7 for avoiding oxygen deficiency cases.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nano fuzzy alarming system for blood transfusion requirement detection in cancer using deep learning

Rady Raz,

Anoushirvani,

Rahimian

et al. 2024

Sci Rep

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Periodic blood transfusion is a need in cancer patients in which the disease process as well as the chemotherapy can disrupt the natural production of blood cells. However, there are concerns about blood transfusion side effects, the cost, and the availability of donated blood. Therefore, predicting the timely requirement for blood transfusion considering patient variability is a need, and here for the first-time deal with this issue in blood cancer using in vivo data. First, a data set of 98 samples of blood cancer patients including 61 features of demographic, clinical, and laboratory data are collected. After performing multivariate analysis and the approval of an expert, effective parameters are derived. Then using a deep recurrent neural network, a system is presented to predict a need for packed red blood cell transfusion. Here, we use a Long Short-Term Memory (LSTM) neural network for modeling and the cross-validation technique with 5 layers for validation of the model along with comparing the result with networking and non-networking machine learning algorithms including bidirectional LSTM, AdaBoost, bagging decision tree based, bagging KNeighbors, and Multi-Layer Perceptron (MLP). Results show the LSTM outperforms the other methods. Then, using the swarm of fuzzy bioinspired nanomachines and the most effective parameters of Hgb, PaO2, and pH, we propose a feasibility study on nano fuzzy alarming system (NFABT) for blood transfusion requirements. Alarming decisions using the Internet of Things (IoT) gateway are delivered to the physician for performing medical actions. Also, NFABT is considered a real-time non-invasive AI-based hemoglobin monitoring and alarming method. Results show the merits of the proposed method.

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Section: The Proposed Methodsmentioning

confidence: 99%

Section: The Proposed Methodsmentioning

confidence: 99%

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Nano fuzzy alarming system for blood transfusion requirement detection in cancer using deep learning

Rady Raz,

Anoushirvani,

Rahimian

et al. 2024

Sci Rep

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Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review

Lu,

Ma,

Zhang

2024

IEEE Trans. Fuzzy Syst.

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Machine learning draws its power from various disciplines, including computer science, cognitive science, and statistics. Although machine learning has achieved great advancements in both theory and practice, its methods have some limitations when dealing with complex situations and highly uncertain environments. Insufficient data, imprecise observations, and ambiguous information/relationships can all confound traditional machine learning systems. To address these problems, researchers have integrate machine leaning from different aspects, and fuzzy techniques including fuzzy sets, fuzzy systems, fuzzy logic, fuzzy measures, fuzzy relations, and so on. This paper presents a systematic review of fuzzy machine learning, from theory, approach to application, with the overall objective of providing an overview of recent achievements in the field of fuzzy machine learning. To this end, the concepts and frameworks discussed are divided into five categories: (a) fuzzy classical machine learning; (b) fuzzy transfer learning; (c) fuzzy data stream learning; (d) fuzzy reinforcement learning; and (e) fuzzy recommender systems. The literature presented should provide researchers with a solid understanding of the current progress in fuzzy machine learning research and its applications.

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Influence-Based Nano Fuzzy Swarm Oxygen Deficiency Detection and Therapy

Cited by 2 publications

References 53 publications

Nano fuzzy alarming system for blood transfusion requirement detection in cancer using deep learning

Nano fuzzy alarming system for blood transfusion requirement detection in cancer using deep learning

Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review

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