Efficient Approaches for Prediction of Brain Tumor using Machine Learning Techniques

Alzubi, Jafar A.; Kumar, Ambeshwar; Alzubi, Omar A.; Manikandan, R.

doi:10.5958/0976-5506.2019.00298.5

Cited by 46 publications

(14 citation statements)

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“…[7][8][9][10] The aspect of living in a healthy environment with the capability of instant decision-making can be achieved through smart sensors and data transmission technologies. 11,12 In this manner, IoT technology has changed the traditional architectures of monitoring procedures. [13][14][15][16][17] In addition to this, time-sensitive statistic compilation has extended the scope of real-time monitoring of an individual under observation.…”

Section: Research Domainmentioning

confidence: 99%

IoT‐assisted irregular environmental event determination for health analysis of pregnant females

Manocha

2021

Trans Emerging Tel Tech

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Indoor environment pollution is one of the significant concerns related to the individual's health of every category. As a majority of the population is spending their time indoors, the research on environment monitoring is realized on priority. In this research, the correlation between the indoor environment and health is determined to predict the health severity of pregnant females in real-time. The proposed solution employs IoT-enabled smart sensors for data acquisition. The accumulated data is processed over the fog-cloud platform for data quantification. To classify the irregular events from the processed data, the machine learning-assisted Weighted-Naïve Bayes (W-NB) classification technique is deployed on the fog layer. Furthermore, a deep learning-inspired multilayered convolutional neural network-long short-term memory (M-CNN-LSTM) model is proposed to determine the scale of health severity. Moreover, a two-phased alert generation mechanism is proposed to deal with the case of a medical emergency by generating notifications to the concerned medical specialists. The performance of the system is validated by performing several experimental simulations over the environmental and health datasets comprised of nearly 38 356 instances. The performance of the proposed solution is evaluated in terms of event classification, severity prediction, temporal delay efficiency, and overall performance stability. The calculated outcomes registered considerable performance over the several state-of-the-art decision-making approaches.

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Section: Research Domainmentioning

confidence: 99%

IoT‐assisted irregular environmental event determination for health analysis of pregnant females

Manocha

2021

Trans Emerging Tel Tech

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“…All the layers have a data transformation for superior and more abstract levels. Usually, DL refers to novel deep networks related to standard ML techniques utilizing big data [6].…”

Section: Introductionmentioning

confidence: 99%

An Automated Glowworm Swarm Optimization with an Inception-Based Deep Convolutional Neural Network for COVID-19 Diagnosis and Classification

et al. 2022

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Recently, the COVID-19 epidemic has had a major impact on day-to-day life of people all over the globe, and it demands various kinds of screening tests to detect the coronavirus. Conversely, the development of deep learning (DL) models combined with radiological images is useful for accurate detection and classification. DL models are full of hyperparameters, and identifying the optimal parameter configuration in such a high dimensional space is not a trivial challenge. Since the procedure of setting the hyperparameters requires expertise and extensive trial and error, metaheuristic algorithms can be employed. With this motivation, this paper presents an automated glowworm swarm optimization (GSO) with an inception-based deep convolutional neural network (IDCNN) for COVID-19 diagnosis and classification, called the GSO-IDCNN model. The presented model involves a Gaussian smoothening filter (GSF) to eradicate the noise that exists from the radiological images. Additionally, the IDCNN-based feature extractor is utilized, which makes use of the Inception v4 model. To further enhance the performance of the IDCNN technique, the hyperparameters are optimally tuned using the GSO algorithm. Lastly, an adaptive neuro-fuzzy classifier (ANFC) is used for classifying the existence of COVID-19. The design of the GSO algorithm with the ANFC model for COVID-19 diagnosis shows the novelty of the work. For experimental validation, a series of simulations were performed on benchmark radiological imaging databases to highlight the superior outcome of the GSO-IDCNN technique. The experimental values pointed out that the GSO-IDCNN methodology has demonstrated a proficient outcome by offering a maximal sensy of 0.9422, specy of 0.9466, precn of 0.9494, accy of 0.9429, and F1score of 0.9394.

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“…AI is found to be broadly benefitting modern medicine and clinical practice [ 2 ]. A range of AI-based applications, such as clinical and genomic diagnostics [ 3 , 4 ], chronic disease management [ 5 ], blockchain technology [ 6 ] and sophisticated surgical robots [ 7 ] have deeply reshaped medical practice in the 21st century. As an emerging field, AI will only continue to transform the way medical science advances and become an integral part of medicine in the future [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study

Jiang

Jong

et al. 2022

IJERPH

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Medical students learning to use artificial intelligence for medical practices is likely to enhance medical services. However, studies in this area have been lacking. The present study investigated medical students’ perceptions of and behavioral intentions toward learning artificial intelligence (AI) in clinical practice based on the theory of planned behavior (TPB). A sum of 274 Year-5 undergraduates and master’s and doctoral postgraduates participated in the online survey. Six constructs were measured, including (1) personal relevance (PR) of medical AI, (2) subjective norm (SN) related to learning medical AI, (3) perceived self-efficacy (PSE) of learning medical AI, (4) basic knowledge (BKn) of medical AI, (5) behavioral intention (BI) toward learning medical AI and (6) actual learning (AL) of medical AI. Confirmatory factor analysis and structural equation modelling were employed to analyze the data. The results showed that the proposed model had a good model fit and the theoretical hypotheses in relation to the TPB were mostly confirmed. Specifically, (a) BI had a significantly strong and positive impact on AL; (b) BI was significantly predicted by PR, SN and PSE, whilst BKn did not have a direct effect on BI; (c) PR was significantly and positively predicted by SN and PSE, but BKn failed to predict PR; (d) both SN and BKn had significant and positive impact on PSE, and BKn had a significantly positive effect on SN. Discussion was conducted regarding the proposed model, and new insights were provided for researchers and practitioners in medical education.

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Efficient Approaches for Prediction of Brain Tumor using Machine Learning Techniques

Cited by 46 publications

References 0 publications

IoT‐assisted irregular environmental event determination for health analysis of pregnant females

IoT‐assisted irregular environmental event determination for health analysis of pregnant females

An Automated Glowworm Swarm Optimization with an Inception-Based Deep Convolutional Neural Network for COVID-19 Diagnosis and Classification

Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study

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