Effective detection of Alzheimer's disease by optimizing fuzzy K-nearest neighbors based on salp swarm algorithm

Lu, Dongwan; Yue, Yinggao; Hu, Zhongyi; Xu, Minghai; Tong, Yinsheng; Ma, Hongwei

doi:10.1016/j.compbiomed.2023.106930

Cited by 24 publications

(9 citation statements)

References 33 publications

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“…For the purposes of feature selection and dimensionality reduction, the imputed data (i.e., normalized medical data record) is collected at this stage 44 , 45 . Since the GFS technique offers the best and most optimal way to choose the most significant characteristics from the available data, it is employed in this study to accomplish this purpose.…”

Section: Proposed Methodologymentioning

confidence: 99%

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

Nandagopal,

Seerangan,

Govindaraju

et al. 2024

Sci Rep

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In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients’ medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier’s error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL’s effectiveness and efficiency in identifying diseases is evaluated and compared.

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Section: Proposed Methodologymentioning

confidence: 99%

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

Nandagopal,

Seerangan,

Govindaraju

et al. 2024

Sci Rep

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“…Swarm intelligence algorithms can be roughly divided into three categories according to the source of inspiration: It is derived from the simple foraging behavior of animals. For example, the Shuffled Frog Leaping Algorithm (SFLA) [ 7 ] proposed by Eusuff et al in 2003 and the Whale Optimization Algorithm (WOA) [ 8 ] proposed by Mirjalili et al in 2016; It stems from the pure social behavior of biological populations, such as the artificial bee colony algorithm (ABC) proposed by Karaboga in 2005 [ 9 ] and the firefly algorithm (FA) proposed by Yang in 2008 [ 10 ]. The cuckoo search (CS) method proposed in 2009 [ 11 ] and the Mayfly Algorithm (MA) proposed by Konstantinos et al in 2020 [ 12 ]; Social behavior and foraging behavior derived from biological populations, such as the Bacterial Foraging Optimization (BFO) proposed by Passino in 2002 [ 13 ], the Bat Algorithm (BA) proposed by Yang in 2010 [ 14 ], and the Sparrow Search Algorithm (SSA) proposed by Xue et al in 2020 [ 15 ].…”

Section: Overview Of Swarm Intelligence Optimization Algorithmsmentioning

confidence: 99%

“…It is derived from the simple foraging behavior of animals. For example, the Shuffled Frog Leaping Algorithm (SFLA) [ 7 ] proposed by Eusuff et al in 2003 and the Whale Optimization Algorithm (WOA) [ 8 ] proposed by Mirjalili et al in 2016;…”

Section: Overview Of Swarm Intelligence Optimization Algorithmsmentioning

confidence: 99%

Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Cao

et al. 2023

Biomimetics

Self Cite

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Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected.

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“…A swarm intelligence optimization system called particle swarm mimics how birds fly while looking for food in a multidimensional search environment. Particle position and velocity are the two key aspects of the PSO method optimization [ 37 , 38 ]. Each one of them is referred to as a particle, and each particle’s initial position and velocity in the search space are initialized at random [ 39 ].…”

Section: Butterfly Optimization Algorithm Optimized By Psomentioning

confidence: 99%

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

et al. 2023

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The features of the kernel extreme learning machine—efficient processing, improved performance, and less human parameter setting—have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend with accuracy and space–time issues as a result of the vast and quick, multi-label, and concept drift features of the developing data streams in the practical application sector. The KELM training procedure still has a difficulty in that it has to be repeated numerous times independently in order to maximize the model’s generalization performance or the number of nodes in the hidden layer. In this paper, a kernel extreme learning machine multi-label data classification method based on the butterfly algorithm optimized by particle swarm optimization is proposed. The proposed algorithm, which fully accounts for the optimization of the model generalization ability and the number of hidden layer nodes, can train multiple KELM hidden layer networks at once while maintaining the algorithm’s current time complexity and avoiding a significant number of repeated calculations. The simulation results demonstrate that, in comparison to the PSO-KELM, BBA-KELM, and BOA-KELM algorithms, the PSOBOA-KELM algorithm proposed in this paper can more effectively search the kernel extreme learning machine parameters and more effectively balance the global and local performance, resulting in a KELM prediction model with a higher prediction accuracy.

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Effective detection of Alzheimer's disease by optimizing fuzzy K-nearest neighbors based on salp swarm algorithm

Cited by 24 publications

References 33 publications

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

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