Smart wireless health care system using graph LSTM pollution prediction and dragonfly node localization

Bačanin, Nebojša; Šarac, Marko; Budimirović, Nebojša; Živković, Miodrag; AlZubi, Ahmad Ali; Bashir, Ali Kashif

doi:10.1016/j.suscom.2022.100711

Cited by 35 publications

(24 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the current era, health care is an important domain in which a lot of research work has been carried out, various researchers working in the healthcare domain to solve the problems of healthcare applications, Sultan et al [ 1 ] introduced a hybrid approach for Alzheimer patients through video summarization. Another research work in the health care domain is carried out by Bacanin et al [ 2 ]; the authors used wireless sensing network technology to monitor human health, pollution predictions, and some other related factor that are useful for human health. Artificial Intelligence and machine learning techniques are very commonly used in the health care sector and researchers get very good results.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique

Alazemi

Jehangir

Imran

et al. 2023

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Lung cancer has the highest death rate of any other cancer in the world. Detecting lung cancer early can increase a patient’s survival rate. The corresponding work presents the method for improving the computer-aided detection (CAD) of nodules present in the lung area in computed tomography (CT) images. The main aim was to get an overview of the latest tools and technologies used: acquisition, storage, segmentation, classification, processing, and analysis of biomedical data. After the analysis, a model is proposed consisting of three main steps. In the first step, threshold values and component labeling of 3D components were used to segment the lung volume. In the second step, candidate nodules are identified and segmented with an optimal threshold value and rule-based trimming. It also selects 2D and 3D features from the candidate segmented node. In the final step, the selected features are used to train the SVM and classify the nodes and classify the non-nodes. To assess the performance of the proposed framework, experiments were performed on the LIDC data set. As a result, it was observed that the number of false positives in the nodule candidate was reduced to 4 FP per scan with a sensitivity of 95%.

show abstract

Section: Introductionmentioning

confidence: 99%

An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique

Alazemi

Jehangir

Imran

et al. 2023

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…This family of metaheuristic approaches has been extensively utilized to address numerous practical real-world problems with NP-hard complexity from the domain of heterogeneous real-world domains. Some notable examples of this kind of applications include cloud-edge computing and task scheduling [30,31], wireless sensors networks (WSNs) challenges such as node localization and prolonging the overall lifetime of the network [32,33], healthcare applications and pollution estimation [34], ANNs challenges including feature selection and hyperparameters' optimization tasks [3,[35][36][37][38], cryptocurrency trends estimations [39], computer-guided illness detection [40][41][42], and lastly the occurring COVID-19 global epidemic-associated applications [43][44][45][46].…”

Section: Swarm Intelligencementioning

confidence: 99%

Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection

Jovanovic¹,

Antonijevic

Stanković

et al. 2022

Mathematics

Self Cite

View full text Add to dashboard Cite

Recent advances in online payment technologies combined with the impact of the COVID-19 global pandemic has led to a significant escalation in the number of online transactions and credit card payments being executed every day. Naturally, there has also been an escalation in credit card frauds, which is having a significant impact on the banking institutions, corporations that issue credit cards, and finally, the vendors and merchants. Consequently, there is an urgent need to implement and establish proper mechanisms that can secure the integrity of online card transactions. The research presented in this paper proposes a hybrid machine learning and swarm metaheuristic approach to address the challenge of credit card fraud detection. The novel, enhanced firefly algorithm, named group search firefly algorithm, was devised and then used to a tune support vector machine, an extreme learning machine, and extreme gradient-boosting machine learning models. Boosted models were tested on the real-world credit card fraud detection dataset, gathered from the transactions of the European credit card users. The original dataset is highly imbalanced; to further analyze the performance of tuned machine learning models, in the second experiment performed for the purpose of this research, the dataset has been expanded by utilizing the synthetic minority over-sampling approach. The performance of the proposed group search firefly metaheuristic was compared with other recent state-of-the-art approaches. Standard machine learning performance indicators have been used for the evaluation, such as the accuracy of the classifier, recall, precision, and area under the curve. The experimental findings clearly demonstrate that the models tuned by the proposed algorithm obtained superior results in comparison to other models hybridized with competitor metaheuristics.

show abstract

“…The algorithms from this group have been used in a wide spectrum of different challenges with NP-hardness from the computer science field. These applications include the problem of global numerical optimization [37], scheduling of tasks in the cloud-edge environments [38][39][40], health care systems and pollution prediction [41], the problems of wireless sensors networks including localization and lifetime maximization [42][43][44], artificial neural networks optimization [45][46][47][48][49][50][51][52][53][54][55][56][57], feature selection in general [58,59], text document clustering [48], cryptocurrency values prediction [60], computer-aided medical diagnostics [61][62][63][64], and, finally, the ongoing COVID-19 pandemic related applications [65][66][67].…”

Section: Swarm Intelligencementioning

confidence: 99%

Multi-Swarm Algorithm for Extreme Learning Machine Optimization

Bačanin

Stoean

Živković

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which makes it suitable for integration within products that require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to the recent literature. Extreme learning machines still face several challenges that need to be addressed. The most significant downside is that the performance of the model heavily depends on the allocated weights and biases within the hidden layer. Finding its appropriate values for practical tasks represents an NP-hard continuous optimization challenge. Research proposed in this study focuses on determining optimal or near optimal weights and biases in the hidden layer for specific tasks. To address this task, a multi-swarm hybrid optimization approach has been proposed, based on three swarm intelligence meta-heuristics, namely the artificial bee colony, the firefly algorithm and the sine–cosine algorithm. The proposed method has been thoroughly validated on seven well-known classification benchmark datasets, and obtained results are compared to other already existing similar cutting-edge approaches from the recent literature. The simulation results point out that the suggested multi-swarm technique is capable to obtain better generalization performance than the rest of the approaches included in the comparative analysis in terms of accuracy, precision, recall, and f1-score indicators. Moreover, to prove that combining two algorithms is not as effective as joining three approaches, additional hybrids generated by pairing, each, two methods employed in the proposed multi-swarm approach, were also implemented and validated against four challenging datasets. The findings from these experiments also prove superior performance of the proposed multi-swarm algorithm. Sample code from devised ELM tuning framework is available on the GitHub.

show abstract

Smart wireless health care system using graph LSTM pollution prediction and dragonfly node localization

Cited by 35 publications

References 29 publications

An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique

An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique

Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection

Multi-Swarm Algorithm for Extreme Learning Machine Optimization

Contact Info

Product

Resources

About