A new robust fuzzy c-means clustering method based on adaptive elastic distance

Amaranthus hybridus waste solid biofuel: comparative and machine learning studies

The diminishing supply of fossil fuels, their detrimental environmental effects, and the challenges associated with the disposal of agro-waste necessitated the development of renewable and sustainable alternative energy sources. This study aims at developing bio-briquettes from Amaranthus hybridus waste, with cassava starch as a binder; both are agricultural wastes. Before and following delignification, alkali-treated Amaranthus hybridus (TAHB) and untreated (UAHB) briquettes were evaluated in terms of combustion and physicochemical parameters. FTIR and SEM were utilized to monitor the morphological transformation and bond restructuring of TAHB and UAHB samples. EDXRF was used to assess the Potential Toxic Elements (PTEs) composition and environmental friendliness of both TAHB and UAHB. Furthermore, Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy c-means (FCM) clustering machine learning models were used to optimize the production process and predict the efficiency of bio-briquettes. After delignification, a lower lignin value of 11.47 ± 0.00% in TAHB compared to 12.31 ± 0.01% (UAHB) was recorded. Calorific values of 10.43 ± 0.25 MJ kg-1 (UAHB) and 12.53 ± 0.30 MJ kg-1 (TAHB) were recorded at p < 0.05. EDXRF results showed a difference of 0.016% in Pb concentration in both samples. SEM reveals morphological restructuring, while FTIR reveals a 4 cm-1 difference in the C-O stretch. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) gave values of 0.0249, 2.104, and, 0.0249; (MAE, training) and 0.0223 (MAE, testing) respectively. This shows that the model's predictions match the reality, thereby suggesting a strong agreement between the predicted and experimental data. The finding of this study shows that delignification-disruption improved the solid biofuel's ability to burn cleanly and sustainably.

Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics

The latest breakthroughs in spatially resolved transcriptomics technology offer comprehensive opportunities to delve into gene expression patterns within the tissue microenvironment. However, the precise identification of spatial domains within tissues remains challenging. In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance. Particularly, AVGN attempts to balance the model's attention focus on local and global structures by utilizing MHA blocks, an aspect that current graph neural networks have not extensively addressed. Benchmark testing demonstrates its significant efficacy in elucidating tissue anatomy and interpreting tumor heterogeneity, indicating its potential in advancing spatial transcriptomics research and understanding complex biological phenomena.

A clustering-optimized segmentation algorithm and application on food quality detection

For solving the problem of quality detection in the production and processing of stuffed food, this paper suggests a small neighborhood clustering algorithm to segment the frozen dumpling image on the conveyor belt, which can effectively improve the qualified rate of food quality. This method builds feature vectors by obtaining the image's attribute parameters. The image is segmented by a distance function between categories using a small neighborhood clustering algorithm based on sample feature vectors to calculate the cluster centers. Moreover, this paper gives the selection of optimal segmentation points and sampling rate, calculates the optimal sampling rate, suggests a search method for optimal sampling rate, as well as a validity judgment function for segmentation. Optimized small neighborhood clustering (OSNC) algorithm uses the fast frozen dumpling image as a sample for continuous image target segmentation experiments. The experimental results show the accuracy of defect detection of OSNC algorithm is 95.9%. Compared with other existing segmentation algorithms, OSNC algorithm has stronger anti-interference ability, faster segmentation speed as well as more efficiently saves key information ability. It can effectively improve some disadvantages of other segmentation algorithms.

An improved fuzzy C-means clustering algorithm using Euclidean distance function

The fuzzy c-mean (FCM) clustering algorithm is a typical algorithm using Euclidean distance for data clustering and it is also one of the most popular fuzzy clustering algorithms. However, FCM does not perform well in noisy environments due to its possible constraints. To improve the clustering accuracy of item varieties, an improved fuzzy c-mean (IFCM) clustering algorithm is proposed in this paper. IFCM uses the Euclidean distance function as a new distance measure which can give small weights to noisy data and large weights to compact data. FCM, possibilistic C-means (PCM) clustering, possibilistic fuzzy C-means (PFCM) clustering and IFCM are run to compare their clustering effects on several data samples. The clustering accuracies of IFCM in five datasets IRIS, IRIS3D, IRIS2D, Wine, Meat and Apple achieve 92.7%, 92.0%, 90.7%, 81.5%, 94.2% and 88.0% respectively, which are the highest among the four algorithms. The final simulation results show that IFCM has better robustness, higher clustering accuracy and better clustering centers, and it can successfully cluster item varieties.

Enhancement of K-means clustering in big data based on equilibrium optimizer algorithm

Data mining’s primary clustering method has several uses, including gene analysis. A set of unlabeled data is divided into clusters using data features in a clustering study, which is an unsupervised learning problem. Data in a cluster are more comparable to one another than to those in other groups. However, the number of clusters has a direct impact on how well the K-means algorithm performs. In order to find the best solutions for these real-world optimization issues, it is necessary to use techniques that properly explore the search spaces. In this research, an enhancement of K-means clustering is proposed by applying an equilibrium optimization approach. The suggested approach adjusts the number of clusters while simultaneously choosing the best attributes to find the optimal answer. The findings establish the usefulness of the suggested method in comparison to existing algorithms in terms of intra-cluster distances and Rand index based on five datasets. Through the results shown and a comparison of the proposed method with the rest of the traditional methods, it was found that the proposal is better in terms of the internal dimension of the elements within the same cluster, as well as the Rand index. In conclusion, the suggested technique can be successfully employed for data clustering and can offer significant support.

Possibilistic c-means clustering based on the nearest-neighbour isolation similarity

Clustering is widely used in data mining and machine learning. The possibilistic c-means clustering (PCM) method loosens the constraint of the fuzzy c-means clustering (FCM) method to solve the problem of noise sensitivity of FCM. But there is also a new problem: overlapping cluster centers are not suitable for clustering non-cluster distribution data. We propose a novel possibilistic c-means clustering method based on the nearest-neighbour isolation similarity in this paper. All samples are taken as the initial cluster centers in the proposed approach to obtain k sub-clusters iteratively. Then the first b samples farthest from the center of each sub-cluster are chosen to represent the sub-cluster. Afterward, sub-clusters are mapped to the distinguishable space by using these selected samples to calculate the nearest-neighbour isolation similarity of the sub-clusters. Then, adjacent sub-clusters can be merged according to the presented connecting strategy, and finally, C clusters are obtained. Our method proposed in this paper has been tested on 15 UCI benchmark datasets and a synthetic dataset. Experimental results show that our proposed method is suitable for clustering non-cluster distribution data, and the clustering results are better than those of the comparison methods with solid robustness.