Health Monitoring Methods in Heart Diseases Based on Data Mining Approach: A Directional Review

Harouni, Majid; Karimi, Mohsen; Nasr, Afrooz; Mahmoudi, Helia; Arab Najafabadi, Zakieh

doi:10.1007/978-981-19-2057-8_5

Cited by 4 publications

(2 citation statements)

References 118 publications

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“…Cancer is detected based on interfering methods such as surgery, radiotherapy and chemotherapy. As studies revealed, using new computer technologies, such as image processing mechanisms have been successful in the processes related to cancers identification and segmentation [7][8][9]. During the computations and processes related to tumor exact segmentation in brain tumor scanned images, it can be referred to some challenges, such as hardy in distinguishing tumor tissue and the tissue of cysts of the main tissue of the human brain, three-dimensional tumor tissue surrounded a wide part of the brain (but twodimension images have been used in the processing system), and the lack of the probability of diagnosing the depth of tumor and diagnosing three-dimension area of tumor [10].…”

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Brain Tumor Identification in Magnetic Resonanceaging using Super-pixel and Fast Primal Dual Algorithm

Emadi

Dehkordi

Mobarakeh

2023

IJE

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Brain tumors are one of the most common causes of death that have been widely investigated by scholars in research areas, including care and prevention. Despite various empirical studies on the brain tumor segmentatin, there is still a need for further investigation. This fact is more needed in the automatic methods of brain tumors detection. In the present study, a new method for improving brain tumor segmentation accuracy based on super-pixel and fast primal dual (PD) algorithms has been proposed. The proposed method detects brain tumor tissue in Flair-MRI imaging in BRATS2012 dataset. This method detects the primary borders of tumors using a super-pixel algorithm, and improves brain tumor borders using fast PD in Markov random field optimization. Then, post-processing processes are used to delete white brain areas. Finally, an active contour algorithm was employed to display tumor area. Different experiments were carried on the proposed method and qualitative and quantitative criteria such as dice similarity measure, accuracy and F-measure were used for evaluation. The obtained results showed the efficiency of the proposed method, such that in the accuracy and sensitivity of 86.59 and 88.57% and F1-Measure 86.37 were obtained, respectively.

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Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Brain Tumor Identification in Magnetic Resonanceaging using Super-pixel and Fast Primal Dual Algorithm

Emadi

Dehkordi

Mobarakeh

2023

IJE

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“…However, it is worth noting that this bodily state can be susceptible to manipulation or imitation ( Schuller and Schuller, 2021 ). Facial expressions and their alterations are commonly utilized for emotion recognition; however, these expressions can be intentionally modified by individuals, posing challenges in accurately discerning their genuine emotions ( Aryanmehr et al, 2018 ; Dzedzickis et al, 2020 ; Harouni et al, 2022 ). EEG (electroencephalography) is a technique employed to monitor brain activity through the measurement of voltage changes generated by the collective neural activity within the brain ( San-Segundo et al, 2019 ; Dehghani et al, 2020 , 2022 , 2023 ; Sadjadi et al, 2021 ; Mosayebi et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Enhancing the accuracy of electroencephalogram-based emotion recognition through Long Short-Term Memory recurrent deep neural networks

Yousefi,

Dehghani,

Taghaavifar

2023

Front. Hum. Neurosci.

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IntroductionEmotions play a critical role in human communication, exerting a significant influence on brain function and behavior. One effective method of observing and analyzing these emotions is through electroencephalography (EEG) signals. Although numerous studies have been dedicated to emotion recognition (ER) using EEG signals, achieving improved accuracy in recognition remains a challenging task. To address this challenge, this paper presents a deep-learning approach for ER using EEG signals.BackgroundER is a dynamic field of research with diverse practical applications in healthcare, human-computer interaction, and affective computing. In ER studies, EEG signals are frequently employed as they offer a non-invasive and cost-effective means of measuring brain activity. Nevertheless, accurately identifying emotions from EEG signals poses a significant challenge due to the intricate and non-linear nature of these signals.MethodsThe present study proposes a novel approach for ER that encompasses multiple stages, including feature extraction, feature selection (FS) employing clustering, and classification using Dual-LSTM. To conduct the experiments, the DEAP dataset was employed, wherein a clustering technique was applied to Hurst’s view and statistical features during the FS phase. Ultimately, Dual-LSTM was employed for accurate ER.ResultsThe proposed method achieved a remarkable accuracy of 97.5% in accurately classifying emotions across four categories: arousal, valence, liking/disliking, dominance, and familiarity. This high level of accuracy serves as strong evidence for the effectiveness of the deep-learning approach to emotion recognition (ER) utilizing EEG signals.ConclusionThe deep-learning approach proposed in this paper has shown promising results in emotion recognition using EEG signals. This method can be useful in various applications, such as developing more effective therapies for individuals with mood disorders or improving human-computer interaction by allowing machines to respond more intelligently to users’ emotional states. However, further research is needed to validate the proposed method on larger datasets and to investigate its applicability to real-world scenarios.

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Optimizing Software-Defined Networks with Fuzzy Logic-Based Enhancement of Openflow Protocol

Hasan

2024

BIO Web Conf.

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Today, humans have a strong need to control their devices from a distance so that they can control the world more than before and explore it for various purposes such as how the universe came into being, discovering the way of creation, observing the events in Global situation and so on. Communication with remote devices can be possible in various ways. SDN networks provide a possibility to exchange information between heterogeneous nodes. Considering that in SDN networks, the nodes are very expensive and these nodes themselves are performing many tasks and various vital tasks; Therefore, the cost of each byte of memory occupied on these nodes is very expensive and must be managed in such a way that they have the highest efficiency. Therefore, to solve this problem, it is very necessary and costly to carry out large projects. In the proposed method of this research, by improving the OpenFlow protocol in software-based networks, it is tried to avoid the cooperation of nodes in the directional distribution (not dissemination) of a small data, from the accumulation of extra information in the nodes' memories. Finally, after the simulation, it was observed that the improvement rate of the proposed method has improved by 0.38%, 0.05%, and 0.04%, respectively, compared to RD, FLCFP, and LEACH2013 methods. The improvement rate of the proposed method compared to RD, FLCFP, and LEACH2013 methods was 0.65%, 0.059%, and 0.331%, respectively.

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Health Monitoring Methods in Heart Diseases Based on Data Mining Approach: A Directional Review

Cited by 4 publications

References 118 publications

Improving the Accuracy of Brain Tumor Identification in Magnetic Resonanceaging using Super-pixel and Fast Primal Dual Algorithm

Improving the Accuracy of Brain Tumor Identification in Magnetic Resonanceaging using Super-pixel and Fast Primal Dual Algorithm

Enhancing the accuracy of electroencephalogram-based emotion recognition through Long Short-Term Memory recurrent deep neural networks

Optimizing Software-Defined Networks with Fuzzy Logic-Based Enhancement of Openflow Protocol

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