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The characterization of the various rock types through petrophysical data analysis is essential for comprehending geological processes and enhancing the efficacy of geophysical approaches aimed at mineralization zones. In the present study, a Fuzzy C-Means (FCM) clustering algorithm was employed to automatically classify lithounits within the western sector of the North Singhbhum Mobile Belt based on the petrophysical properties. Laboratory measurements of 326 rock samples from the study area show a wide range of density (~2350–3150 kg/m3) and magnetic susceptibility (10−5 SI to 10−1 SI) values. Further FCM analysis reveals three distinct clusters: (i) cluster 1 displays high density and low magnetic susceptibility responses and comprises majorly metabasic, phyllite, and mica schist rocks, (ii) cluster 2 shows low density and low magnetic susceptibility characteristics and contains mainly metasedimentary rocks (phyllite, quartzite, and mica schist) and (iii) cluster 3 also primarily encompasses metasedimentary rocks, but it displays the low density and high magnetic susceptibility characteristics. Overlap of rock types in different clusters probably indicates the influence of secondary geological processes on the petrophysical measurements such as metamorphism, alteration, and weathering, which is also supported by the petrographical studies. Overall, the present study demonstrates the potential utility of the FCM algorithm for automatic lithology classification and inferring the associated geological processes from the petrophysical measurements. Furthermore, the correlation between the geophysical and petrophysical clusters highlights the role of petrophysical information in the automatic geological/mineral mapping. However, the complexity in cluster attributes on a detailed scale suggests that future studies in the NSMB should focus on comprehensive multi-parameter petrophysical and geochemical measurements. This approach will help in developing better strategies for 3D geophysical data inversion and resolve the complexities in petrophysical data interpretation. [ABSTRACT FROM AUTHOR]

Power load forecasting is an important part of modern smart grid operation management. Accurate forecasting guides the efficient and stable operation of the power system. In this paper, a fuzzy C-means clustering algorithm and an improved locally weighted linear regression model are proposed for short-term power load forecasting. First, the fuzzy C-means clustering algorithm is used to cluster the power load. Make the power consumption behavior of load data of the same category similar and use the power consumption load data of the same category as the training sample. Then, to solve the problem of large calculation and insufficient fitting of the locally weighted linear regression model, the k-nearest neighbor range constraint is introduced into the model for daily load forecasting. Finally, the effectiveness of the method is verified by a simulation example. Experimental results show that this method can effectively improve the accuracy and generalization ability of power load forecasting compared with other methods. [ABSTRACT FROM AUTHOR]

Machine learning (ML) techniques have gained substantial attention in many aspects of contemporary life during the last several years. As part of the digital revolution, the electricity industry is one of the leaders in implementing such appealing and effective technology for various applications. In general, low-frequency oscillations (LFO) are nonthreatening but slow-burning issues that, if not addressed appropriately, might lead to complete network collapse. Due to the significance of prominent ML family members in improving LFO damping in electric power system (EPS) networks, the applicability of the fuzzy c-means (FCM) clustering-based deep learning (DL) technique is modeled in this paper for two typical EPS networks by predicting the critical parameters of the power system stabilizers (PSS). The first network is a single-machine infinite bus (SMIB) network where the synchronous generator is equipped with a PSS. On the other hand, a unified power flow controller (UPFC) coordinated PSS is connected at one terminal of the synchronous generator of the second network. The clustering of the datasets obtained through the whale optimization algorithm (WOA) is performed based on the calculated silhouette values for both power networks. Then, several statistical performance indices (SPI) are evaluated to validate the robustness of the training and testing procedure of the DL method for the prepared data clusters using the FCM clustering technique. The efficacy of the proposed FCM-DL strategy in enhancing LFO damping for the two test networks is assessed based on standard analytical and time-domain analysis. Therefore, the minimum damping ratio (MDR), eigenvalue, rotor angle, and angular frequency with respect to time are simulated and analyzed. The article also includes a comparison of the findings of previous studies to illustrate the potential of the proposed FCM-DL strategy in improving EPS stability by damping out undesirable LFOs. It is worth noting that the developed FCM-DL models can predict the candidate parameters with a coefficient of determination (R2) value of more than 0.9974. During the implementation phase, the proposed strategy achieves competitive MDR, for instance, more than 0.50 and 0.74 MDR for the first and second networks, respectively. [ABSTRACT FROM AUTHOR]

In today's data-driven business landscape, effective and transparent decision making becomes relevant to maintain a competitive advantage over the competition, especially in customer service in B2B environments. This study presents a methodological framework that integrates Explainable Artificial Intelligence (XAI), C-means clustering, and the Analytic Hierarchical Process (AHP) to improve strategic decision making in business environments. The framework addresses the need to obtain interpretable information from predictions based on machine learning processes and the prioritization of key factors for decision making. C-means clustering enables flexible customer segmentation based on interaction patterns, while XAI provides transparency into model outputs, allowing support teams to understand the factors influencing each recommendation. The AHP is then applied to prioritize criteria within each customer segment, aligning support actions with organizational goals. Tested with real customer interaction data, this integrated approach proved effective in accurately segmenting customers, predicting support needs, and optimizing resource allocation. The combined use of XAI and the AHP ensures that business decisions are data-driven, interpretable, and aligned with the company's strategic objectives, making this framework relevant for companies seeking to improve their customer service in complex B2B contexts. Future research will explore the application of the proposed model in different business processes. [ABSTRACT FROM AUTHOR]

Collaborative filtering recommendation systems generate personalized recommendation results by analyzing and collaboratively processing a large numerous of user ratings or behavior data. The widespread use of recommendation systems in daily decision-making also brings potential risks of privacy leakage. Recent literature predominantly employs differential privacy to achieve privacy protection, however, many schemes struggle to balance user privacy and recommendation performance effectively. In this work, we present a practical privacy-preserving scheme for user-based collaborative filtering recommendation that utilizes fuzzy C-means clustering and Shapley value, FSPPCFs, aiming to enhance the recommendation performance while ensuring privacy protection. Specifically, (i) we have modified the traditional recommendation scheme by introducing a similarity balance factor integrated into the Pearson similarity algorithm, enhancing recommendation system performance; (ii) FSPPCFs first clusters the dataset through fuzzy C-means clustering and Shapley value, grouping users with similar interests and attributes into the same cluster, thereby providing more accurate data support for recommendations. Then, differential privacy is used to achieve the user’s personal privacy protection when selecting the neighbor set from the target cluster. Finally, it is theoretically proved that our scheme satisfies differential privacy. Experimental results illustrate that our scheme significantly outperforms existing methods. [ABSTRACT FROM AUTHOR]

To ensure the accuracy of image segmentation, selecting appropriate image features is crucial. The existing image segmentation methods mainly utilize spectral features of images to achieve image segmentation, which have low segmentation accuracy and weak robustness, and are difficult to adapt to the needs of complex image segmentation. Therefore, this paper proposes a new robust fuzzy clustering image segmentation based on Wasserstein distance in the Riemannian manifold feature space. At first, the spectral features of pixels in the neighborhood window around the current pixel are constructed into a Gaussian normal distribution structure model, and the original image is mapped to the Riemannian manifold feature space to achieve Riemannian manifold feature modeling of image feature information. Secondly, the Wasserstein distance is used to measure the difference between two Gaussian Riemannian manifolds, and a robust fuzzy clustering method for image segmentation is proposed in Riemannian manifold feature space. Finally, the local convergence of the algorithm is proved using the Zangwill theorem and bordered Hessian matrix. The experimental results demonstrate that the proposed algorithm has good segmentation performance and strong noise resistance. Compared with existing segmentation algorithms based on spectral feature space and Riemannian manifold feature space, this proposed algorithm is more effective and robust in segmenting images with or without noise. [ABSTRACT FROM AUTHOR]

Vaginoscopy is commonly employed to assess the different phases of the reproductive cycle in female canines. The technique can be used to confirm the onset of estrus, which is the period during which the female is receptive to mating. During proestrus, the vaginal mucosa exhibits a characteristic appearance that can be observed through a vaginoscope. The vaginal mucosa is pink-colored and shows edema with longitudinal primary folds. During the later phase of proestrus, it becomes pale and exhibits secondary folds. During the oestrum, the mucosa becomes paler, edema is relieved, and characteristic folds known as 'crenulations' are exhibited. Evaluating reproductive stages through manual examination is a labor-intensive procedure demanding substantial training to minimize inaccuracies. Furthermore, the consistency of results obtained through this method by veterinarians can be limited. In this work, an automated classification of the various stages in the reproductive cycle of female canines using machine learning is proposed. 179 vaginoscopic images are used after enhancement using color transfer algorithm in l α β space. Region of interest are segmented using fuzzy c-means clustering. Textural features extracted by the local binary pattern (LBP) method are exploited to classify regions of interest into different stages. The classification is performed using various methods, including Naive Bayes (NB), support vector machine (SVM), decision tree (DT), k-nearest neighbor (kNN), k-star, decision stump (DS), and rule learner (RL). The highest accuracy achieved is 85.26 % for kNN. Parameters namely sensitivity, F1 score, recall, and, specificity, are also obtained and found to be.853,.88,.853, and.79, respectively. This automated classification system will help veterinarians in identifying the reproductive stages of female canines thereby predicting the optimum mating period. [ABSTRACT FROM AUTHOR]

Abstract Collaborative filtering recommendation systems generate personalized recommendation results by analyzing and collaboratively processing a large numerous of user ratings or behavior data. The widespread use of recommendation systems in daily decision-making also brings potential risks of privacy leakage. Recent literature predominantly employs differential privacy to achieve privacy protection, however, many schemes struggle to balance user privacy and recommendation performance effectively. In this work, we present a practical privacy-preserving scheme for user-based collaborative filtering recommendation that utilizes fuzzy C-means clustering and Shapley value, FSPPCFs, aiming to enhance the recommendation performance while ensuring privacy protection. Specifically, (i) we have modified the traditional recommendation scheme by introducing a similarity balance factor integrated into the Pearson similarity algorithm, enhancing recommendation system performance; (ii) FSPPCFs first clusters the dataset through fuzzy C-means clustering and Shapley value, grouping users with similar interests and attributes into the same cluster, thereby providing more accurate data support for recommendations. Then, differential privacy is used to achieve the user’s personal privacy protection when selecting the neighbor set from the target cluster. Finally, it is theoretically proved that our scheme satisfies differential privacy. Experimental results illustrate that our scheme significantly outperforms existing methods.

A number of devices in Industrial Internet are various types in recent years. The monitored traffic data from different devices always unlabeled and contain various types of attack traffic. In other words, misjudgments occurring by the ambiguity with these various unlabeled traffic in situation assessment of Industrial Internet need to solve urgently for above complex network scenario. In this paper, a new self-supervised situation assessment method FCVnet (FCM-CNN-ViT Net) is proposed to reduce the misjudgement probability. An enhanced fuzzy c-means clustering method EFCM (Enhanced Fuzzy C-means Clustering), is designed for the unlabelled traffic data. Meanwhile the self-supervised pre-training is carried out by improving initial cluster centre selection to obtain more accurate labels. In order to capture more global features for better feature representation, MCFV (Multi-Convolutional Fusion and Vision Transformer) module combining Multi-Convolutional Neural Network and Vision Transformer (ViT) is designed to capture and fuse features from local details to broader context. Experimental results show that the precision and recall of the proposed FCVnet are improved by 7.51% and 15.16% on average with two data sets. [ABSTRACT FROM AUTHOR]

Infrared small target detection technology plays a pivotal role in critical military applications, including early warning systems and precision guidance for missiles and other defense mechanisms. Nevertheless, existing traditional methods face several significant challenges, including low background suppression ability, low detection rates, and high false alarm rates when identifying infrared small targets in complex environments. This paper proposes a novel infrared small target detection method based on a transformed Gaussian filter kernel and clustering approach. The method provides improved background suppression and detection accuracy compared to traditional techniques while maintaining simplicity and lower computational costs. In the first step, the infrared image is filtered by a new filter kernel and the results of filtering are normalized. In the second step, an adaptive thresholding method is utilized to determine the pixels in small targets. In the final step, a fuzzy C-mean clustering algorithm is employed to group pixels in the same target, thus yielding the detection results. The results obtained from various real infrared image datasets demonstrate the superiority of the proposed method over traditional approaches. Compared with the traditional method of state of the arts detection method, the detection accuracy of the four sequences is increased by 2.06%, 0.95%, 1.03%, and 1.01%, respectively, and the false alarm rate is reduced, thus providing a more effective and robust solution. [ABSTRACT FROM AUTHOR]

Electric spindles are a critical component of numerically controlled machine tools that directly affect machining precision and efficiency. The accurate identification of the modal parameters of an electric spindle is essential for optimizing design, enhancing dynamic performance, and facilitating fault diagnosis. This study proposes a covariance-driven stochastic subspace identification (SSI-cov) method integrated with a simulated annealing (SA) strategy and fuzzy C-means (FCM) clustering algorithm to achieve the automated identification of modal parameters for electric spindles. Using both finite element simulations and experimental tests conducted at 22 °C, the first five natural frequencies of the electric spindle under free, constrained, and dynamic conditions were extracted. The experimental results demonstrated experiment errors of 0.17% to 0.33%, 1.05% to 3.27%, and 1.29% to 3.31% for the free, constrained, and dynamic states, respectively. Compared to the traditional SSI-cov method, the proposed SA-FCM method improved accuracy by 12.05% to 27.32% in the free state, 17.45% to 47.83% in the constrained state, and 25.45% to 49.12% in the dynamic state. The frequency identification errors were reduced to a range of 2.25 Hz to 20.81 Hz, significantly decreasing errors in higher-order modes and demonstrating the robustness of the algorithm. The proposed method required no manual intervention, and it could be utilized to accurately analyze the modal parameters of electric spindles under free, constrained, and dynamic conditions, providing a precise and reliable solution for the modal analysis of electric spindles in various dynamic states. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To address the problem that water color image processing result is greatly affected by illumination changes, especially in bad weather. Based on infrared image a method of identifying water accumulation area was proposed, which overcame the limitation of color image processing method by illumination conditions. Further, a fusion algorithm for detecting the edges of infrared image water accumulation based on the Canny operator and fuzzy C-means clustering was proposed to solve the blurred edges and lack of clear temperature distribution laws in infrared imaging water accumulation. The results show that the algorithm achieves a good extraction effect on the fuzzy boundary, and the error between the image segmentation result and the actual area marked by manual is less than 7% . The ratio of pixels can quickly and accurately obtain the water accumulation area, which provides quantitative support for the evaluation of the wet runway surface condition and provides effective technical support for the safe operation of the aircraft on the wet runway surface. [ABSTRACT FROM AUTHOR]

Over the past few decades, the field of fuzzy logic has evolved significantly, leading to the development of diverse techniques and applications. Fuzzy logic has been successfully combined with other artificial intelligence techniques such as artificial neural networks, deep learning, robotics, and genetic algorithms, creating powerful tools for complex problem-solving applications. This article provides an informative description of some of the main concepts in the field of fuzzy logic. These include the types and roles of membership functions, fuzzy inference system (FIS), adaptive neuro-fuzzy inference system and fuzzy c-means clustering. The processes of fuzzification, defuzzification, implication, and determining fuzzy rules' firing strengths are described. The article outlines some recent developments in the field of fuzzy logic, including its applications for decision support, industrial processes and control, data and telecommunication, and image and signal processing. Approaches to implementing fuzzy logic models are explained and, as an illustration, Matlab (version R2024b) is used to demonstrate implementation of a FIS. The prospects for future fuzzy logic developments are explored and example applications of hybrid fuzzy logic systems are provided. There remain extensive opportunities in further developing fuzzy logic-based techniques, including their further integration with various machine learning algorithms, and their adaptation into consumer products and industrial processes. [ABSTRACT FROM AUTHOR]

Biomedical image segmentation is considered an important and challenging task. Automated biomedical image analysis plays a major role in the early and quick diagnosis of diseases. Accurate and precise segmentation can lead to early treatment planning and it demands sophisticated approaches. Inspired by this, a novel approach is proposed. This approach will be known as the Fuzzy modified cuckoo search with spatial exploration (FMCSSE). High correlation among pixels is an important property of image data and pixels surrounding a particular pixel possess similar feature information. Therefore, it is extremely essential to consider the spatial information to generate a meaningful segmented image. The traditional fuzzy clustering approach is not suitable for exploiting spatial information. Therefore, this work is designed to explore spatial information and find the optimal clusters from biomedical images with the help of the fuzzy-modified cuckoo search approach. This approach is applied to different biomedical images and compared with various state-of-the-art unsupervised approaches like FEMO, FMCS, MCS, and CS. The proposed approach does not suffer from the choice of the initial assignment of the cluster centers. The proposed approach uses the type-2 fuzzy system blended with the modified cuckoo search (McCulloch approach) and spatial exploration procedure. Both qualitative and quantitative results show the superiority of the FMCSSE approach in terms of performance. [ABSTRACT FROM AUTHOR]

In order to solve the problems of a high mismatching rate and being easily affected by noise and gray transformation, an improved product defect detection method combining centroid distance and textural information is proposed in this paper. Based on image preprocessing, the improved fuzzy C-means clustering method is used to extract the closed contour features. Then, the contour center distance description operator is used for bidirectional matching, and a robust coarse matching contour pair is obtained. After the coarse matching contour pair is screened, the refined matching result is obtained by using the improved local binary pattern operator. Finally, by comparing whether the number of fine matching pairs is consistent with the number of template outlines, the detection of good and bad industrial products is realized, and the closed contour extraction experiment, the anti-rotation matching experiment, the anti-gray difference matching experiment, and the defect detection experiment of three different products are designed. The experimental results show that the improved product defect detection method has good performance in relation to anti-rotation transformation and anti-gray difference, the detection accuracy can reach more than 90%, and the detection time is up to 362.6 ms, which can meet the requirements of industrial real-time detection. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation is highly stochastic and intermittent, which poses a challenge to the planning and operation of existing power systems. To enhance the accuracy of PV power prediction and ensure the safe operation of the power system, a novel approach based on seasonal division and a periodic attention mechanism (PAM) for PV power prediction is proposed. First, the dataset is divided into three components of trend, period, and residual under fuzzy c-means clustering (FCM) and the seasonal decomposition (SD) method according to four seasons. Three independent bidirectional long short-term memory (BiLTSM) networks are constructed for these subsequences. Then, the network is optimized using the improved Newton–Raphson genetic algorithm (NRGA), and the innovative PAM is added to focus on the periodic characteristics of the data. Finally, the results of each component are summarized to obtain the final prediction results. A case study of the Australian DKASC Alice Spring PV power plant dataset demonstrates the performance of the proposed approach. Compared with other paper models, the MAE, RMSE, and MAPE performance evaluation indexes show that the proposed approach has excellent performance in predicting output power accuracy and stability. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Diseases and pests in plants/crops are major causes of significant agricultural losses with economic, social and ecological impacts. Therefore, there is a need for early identification of plant diseases and pests through automated systems. Recently, machine learning-based methods have become popular in solving agricultural problems such as plant diseases faced by technically-noob farmers. This work proposes a novel method based on stacking ensemble machine learning to detect plant diseases in Uradbean precisely. Two classifiers: support vector machine (SVM), random forest (RF) are trained on a dataset consists of Uradbean infected and healthy leaf images. These classifiers are stacked with logistic regression (LR) classifier. In the diverse ensemble, LR classifier is used as a meta-learner which enhanced the precision of the disease classification. The fuzzy C-Means clustering with particle swarm optimization is used for image segmentation. Haralick, Hu Moments and color histogram methods are used in feature extraction. During the tests, the proposed model is also compared with pretrained networks: DenseNet-201, ResNet-50, and VGG19. It achieved an impressive classification accuracy of 96.82 % which is higher than the individual classifiers and pre-trained networks. To validate model performance, it is evaluated on a benchmark public dataset consists of A pple leaf images and achieved 98.30% accuracy. It is observed that ensemble method reflects an advantage over individual models in increasing the classification rates and reducing the computational overhead in comparison to pre-trained networks which struggle due to the issues such as irrelevant features, generation of pertinent characteristics, and noise. [ABSTRACT FROM AUTHOR]

Objectives: To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical biochemical markers of skeletal health. Methods: This study presents an unsupervised learning approach for the automatic detection of first arrival time and estimation of ultrasonic velocity from axial transmission waveforms, which potentially indicates bone quality. The proposed method combines the ReliefF algorithm and fuzzy C‐means clustering. It was first validated using an in vitro dataset measured from a Sawbones phantom. It was subsequently applied on in vivo signals collected from 40 infants, comprising 21 males and 19 females. The extracted neonatal ultrasonic velocity was subjected to statistical analysis to explore correlations with the infants' anthropometric features and biochemical indicators. Results: The results of in vivo data analysis revealed significant correlations between the extracted ultrasonic velocity and the neonatal anthropometric measurements and biochemical markers. The velocity of first arrival signals showed good associations with body weight (ρ = 0.583, P value <.001), body length (ρ = 0.583, P value <.001), and gestational age (ρ = 0.557, P value <.001). Conclusion: These findings suggest that fuzzy C‐means clustering is highly effective in extracting ultrasonic propagating velocity in bone and reliably applicable in in vivo measurement. This work is a preliminary study that holds promise in advancing the development of a standardized ultrasonic tool for assessing neonatal bone health. Such advancements are crucial in the accurate diagnosis of bone growth disorders. [ABSTRACT FROM AUTHOR]

Precision agriculture involves extensive agricultural landscapes with varying terrains and crop types. An energy-efficient routing protocol ensures that data is efficiently transmitted across the entire agricultural area. However, the ability of clustering routing protocol is based on the cluster formation as well as cluster head selection processes. Traditional methods are impractical for such large-scale deployments. In order to conquer the above-mentioned challenges, this paper proposed a novel Ensemble Fuzzy Crossover based Golden Jackal (EFC-GJ) method for enhancing the formation of cluster and cluster heads selection. In the proposed method, the crossover-based Golden Jackal Optimization, Fuzzy c-means Clustering Method, and Ensemble Q-learning are utilized for cluster center initialization, cluster formation, and cluster head selection respectively. The performance evaluation measures such as throughput, jitter, latency, energy consumption, and network lifetime are utilized for the evaluation of the proposed EFC-GJ method and these results are compared with existing methods. The EFC-GJ method attained a PDR of 0.98, throughput of 0.97 Mbps, end-to-end delay of 1.3 s, network lifetime of 5620 rounds, energy consumption of 0.2 mJ, jitter of 0.36 ms, and latency of 1.7 s. The experimental results illustrate the EFC-GJ method's effectiveness in forming cluster and selecting cluster head. [ABSTRACT FROM AUTHOR]

Accurate short-term photovoltaic (PV) power forecasting can reduce the un- certainty of PV power generation, which is crucial for grid operation as well as energy dispatch. Considering the influence of seasonal and meteorological factors on short-term PV power prediction, a short-term PV power predic- tion method based on meteorological similarity day and sparrow search algo- rithm and bi-directional long and short-term memory network combination (SSA-BiLSTM) is proposed. Firstly, the correlation between meteorological factors and PV power generation is calculated by using Pearson coefficients, getting the strongly correlated meteorological factors affecting PV power generation; afterwards,the historical data of the strongly correlated meteorological factors are clustered by fuzzy C-means clustering to achieve meteorological similar day clustering; then, the best similar day is selected from the meteorological similar day according to the test day seasonal features and meteorological data, and Forming a training set with historical data, and training the original BiLSTM network. the SSA algorithm was used to find the optimal BiLSTM network parameters. Finally, Using the optimal parameters construct BiLSTM network to achieve short-term PV power prediction. The experiments were conducted with historical data from a PV power plant in Xinjiang, and also compared with existing prediction algorithms.The results show that the accuracy of PV power prediction in different weather conditions is 33.1 %, 31.9 % and 24.1 % higher than that under the same intelligent optimization algorithm and different neural networks, the accuracy of PV power prediction in different weather conditions is 27.9 %, 24.7 % and 18.0 % higher than that under the different intelligent algorithms and same neural network. Therefore, the algorithm in this paper has better accuracy in short-term PV power prediction under different seasons and different weather conditions.

Radiologists employ mammograms for the detection of breast cancer in patients, particularly as breast cancer exhibits higher incidence rates in women. Early identification of breast cancer significantly reduces the risk of mortality. Mammograms serve as a valuable imaging technique for the early detection of breast cancer. However, accurately characterizing breast cancer images poses a considerable challenge. A recent research study introduced an innovative algorithm for Mammogram Pectoral Muscle Removal, leveraging Entropy-Based Fuzzy C-means clustering and Classification using RMCNN (Root Mean Squares Convolutional Neural Network). The process involves extracting and pre-processing the input breast image from the dataset through Gaussian filtering. Subsequently, pectoral muscle removal is achieved via entropy-based fuzzy C-Means clustering, followed by DCT (Discrete Cosine Transform) and DWT (Discrete wavelet transforms) feature extraction. The Root Mean Square Value-based Convolutional Neural Network classifier effectively clusters mammography images into normal, malignant, and benign classes, achieving an impressive 99.45% accuracy far better than existing methods with accuracies of 93%, 89%, and 60%. [ABSTRACT FROM AUTHOR]

Routes discovery that can provide reliable data transmission in Mobile Ad-hoc Networks is challenging due to its wireless channel characteristics and dynamic transmission environment. Ad-hoc networks frequently experience link failure because nodes are mobile and their positions are not fixed. The cellular ad hoc community's dynamic nature makes it possible to analyze multi-route routing protocols specifically. One of the most trustworthy and environment-friendly routing methods is the Zone Routing Protocol used in Mobile Ad Hoc Networks. Offering timely and trustworthy communication services, however, depends critically on maintaining the Quality of Service, electricity performance, and outstanding resource management. In this research, we suggested an energy-efficient routing method for zone-based MANETs called enthalpy and net routing. First, a completely fuzzy quarter clustering based on an Energy guide is used to perform the clustering. With the help of fuzzy memberships, the AFCM set of rules permits the input of statistics for each elegance. In the second, the best route is selected using Enthalpy Ant Net Routing (EANR) while taking the following factors into account: community disconnection, channel error, buffer overflow, and contention at the link layer. The results of the experiment show that the collection of rules performs better than many existing algorithms in terms of community longevity, electricity consumption, and other metrics. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Scientific information on the spatial variability of soil properties is critical for sustainable production and designing appropriate measures for efficient soil–crop management. The growing urban areas in fertile landscapes are a major concern experiencing a huge anthropogenic onslaught and lack of information on spatial variability of soil properties. Therefore, the present study was carried out to delineate the spatial distribution of some selected soil properties from Balh Valley and its catchment area (Suketi basin) in lower Himachal Himalaya, India. A total of 468 geo-referenced surface soil samples were collected and analyzed for soil pH, EC, OC; primary nutrients (N, P, K); secondary nutrients (Ca and Mg); and DTPA-extractable micronutrients (Zn, Fe, Mn and Cu)following standard procedures. The results showed a significant variation in soil pH (acidic to alkaline), EC (0.08–0.70 dS/m), OC (3–26 g/kg), major nutrients N (41.96–208.03 mg/kg), P (5.80–18.75 mg/kg), and K (53.57–163.64 mg/kg). Among micronutrients, Zn was found below the critical limit toward the extreme fringes of the basin. The data were analyzed with descriptive statistics and geostatistical approach. The spatial maps were prepared with ordinary kriging (OK) technique after semivariogram modeling and cross-validation approach. The five principal components (PCs) chosen depicted a moderate correlation between the calculated soil attributes. The two management zones (MZs) were derived by performing the fuzzy c-means clustering analysis based on fuzzy performance index (FPI) and normalized classification entropy (NCE) analysis. The spatial maps represent the distribution of soil properties in the valley and its catchment area. The information generated provides baseline data for site-specific fertilizer recommendations for precision agriculture and minimize downstream adverse environmental impact in the Himalayan ecosystem. [ABSTRACT FROM AUTHOR]

Understanding regional climatic trends is crucial for taking appropriate actions to mitigate the impacts of climate change and managing water resources effectively. This study aims to investigate the dissimilarities and similarities among various climate stations in Bangladesh from 1981 to 2021. Fuzzy C-means (FCM) and K-means clustering techniques were employed to identify regions with comparable rainfall patterns. Moreover, the innovative trend analysis (ITA) and the Mann-Kendall (MK) test family were utilized to analyze rainfall trends. The results indicate that both K-means and FCM methods successfully detected two rainfall regions in Bangladesh with distinct patterns. The ITA curve analysis revealed that out of the 29 stations, 13 had a non-monotonic increasing trend having no monotonic increasing trend, 8 had a non-monotonic decreasing trend, and 8 exhibited a monotonic decreasing trend. Additionally, the MK tests employed in the study showed predominantly negative trends across Bangladesh. The majority of stations (65.51%) fell into Cluster 1, while the remaining 34.48% were in Cluster 2. In terms of ITA analysis, 17.24% of stations exhibited a monotonic decrease, while there were no stations with a monotonic increase. However, 37.93% of stations showed a non-monotonic increase, and 44.83% displayed a non-monotonic decrease. These identified regions can provide valuable insights for water resource management, disaster risk reduction, and agricultural planning. Moreover, detailed rainfall analysis can help policymakers and scientists develop sustainable and effective regional-scale policies for managing the country's flood and drought situations, ultimately supporting agricultural development and environmental planning.

Privacy is a significant issue that requires consideration in all applications. Data collected from various individuals and organizations must be disclosed to the public or private parties for analysis and research purposes. The collected data are studied and analyzed digitally for the extraction of various useful patterns for decision-making research purposes. Privacy-preserving data publishing is significant as privacy violations in the patient’s data may have an adverse effect on the individual positive reputation. An efficient Cluster Based anonymity model has been proposed to anonymizes the 1:1 dataset with a single sensitive attribute through the introduction of a concept named “Semi-sensitive attribute.” Based on correlation, the attributes are categorized as quasi-identifier and semi-sensitive attributes. The k-anonymity is implemented on the quasi-identifier with the semi-sensitive attribute table and Fuzzy c-means clustering has been implemented to fix a range of values for anonymizing the semi-sensitive attributes. The disease is considered a sensitive attribute as the research work focuses on the medical dataset. The proposed model is demonstrated to resist the three privacy attacks such as, i)Identity Disclosure, ii) Attribute Disclosure, and iii) Membership Disclosure. The utility loss is calculated for each row and utility loss of each record are aggregated and considered as the total information loss for each attribute. Cluster Based anonymity model measured the utility loss for all the attributes and the average utility loss for the anonymized patient dataset is 3.78%.

Skin cancer is one of the most deadly types of cancer that makes people less aware of the signs and symptoms. The skin cells are destroyed in people affected by skin cancer, a typical occurrence worldwide. As a result, accurate skin cancer detection earlier is crucial to reduce the risk of the disease spreading and raising the chances of survival. In recent years, medical applications have grown more interested in image processing and machine learning approaches. However, the model's performance is still vulnerable to image occlusions and inaccurate detection. Hence, a deep learning based skin cancer detection mechanism is introduced in this research. For the input image, the artefacts are removed using the pre-processing technique. Then, the essential region of interest (ROI) is segmented using the Dictionary Learning based Fuzzy C-Means (DicL-FCM) clustering technique. Then, the optimal best features are chosen using the proposed Chebyshev based Chaotic Genetic Optimization (C-CGO) algorithm from the extracted features. Finally, skin cancer detection is devised using the proposed Twin Attention based Convolutional Gated Recurrent Network (TA_CGRNet) model. The performance of the proposed Optimized TA_CGRNet is analyzed based on various assessment measures like Accuracy, Specificity, Precision, Recall, and F-Measure accomplished the values of 98.91%, 94.67%, 96.92%, 96.23%, and 96.54%, respectively. [ABSTRACT FROM AUTHOR]

Technological progress has brought about modifications in the educational process. This transformation has led to numerous technological innovations for supporting student learning, with smartphones being one of the prominent tools. Despite the widespread use, students have primarily used smartphones for online gaming and social media, leading to a decline in the effectiveness for educational purposes. Therefore, this study aimed to explore how students in electronics engineering education responded to a novel technology, augmented reality (AR), when integrated into the fundamental learning process, in order to maximize the utility of smartphones. A cross-sectional survey approach with a quantitative methodology comprising 101 students in the field of electronics engineering education in higher education institutions in Indonesia was adopted. Data were collected through a questionnaire, which was subsequently analyzed using the Structural Equation Model (SEM) method with SmartPLS 3 software. Additionally, this study used Fuzzy C-Means (FCM) clustering method to examine the influence of each cluster values on the others. The analytical results showed that Computational Thinking Skill (CTS) of students significantly impacted Actual System Use (ASU) of AR. Furthermore, perceived usefulness (PU) and perceived ease of use (PEU) of this technology played crucial roles as mediators in ASU of AR. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a common type of neurodegenerative condition characterized by progressive neural deterioration. The anatomical changes associated with individuals affected by Alzheimer's disease include the loss of tissue in various areas of the brain. Magnetic Resonance Imaging (MRI) is commonly used as a noninvasive tool to assess the neural structure of the brain for diagnosing Alzheimer's disease. In this study, an integrated Improved Fuzzy C-means method with improved watershed segmentation was employed to segment the brain tissue components affected by this disease. These segmented features were fed into a hybrid technique for classification. Specifically, a hybrid Convolutional Neural Network–Long Short-Term Memory classifier with 14 layers was developed in this study. The evaluation results revealed that the proposed method achieved an accuracy of 98.13% in classifying segmented brain images according to different disease severities. [ABSTRACT FROM AUTHOR]

Various wind turbines have been manufactured for converting wind power into electric energy. They are fixed speed concepts with squirrel cage induction generators, limited variable speed concepts with wound rotor induction generators, variable speed concepts with double fed induction generators, direct-drive concepts with electrically excited synchronous generators and gearbox-free concepts with permanent magnet induction technologies. The composed components and the power curve of these technologies are different and to select an appropriate wind turbine for a wind site, in addition to the economic parameter, reliability criterion must be considered. To address this, a reliability model is developed in this paper that considers both component failure and the unpredictable nature of wind speed for different types of wind turbines. The optimal state number of reliability presentations is determined using XB index calculation and fuzzy c-means clustering method to create multi-state presentations for wind turbines. The proposed approach can be used to determine the most reliable wind turbine for a given wind site by assessing the adequacy of the electric network containing various types of wind turbines. The approach's effectiveness is demonstrated through adequacy assessments of the RBTS and IEEE-RTS, which contain various types of wind turbines. [ABSTRACT FROM AUTHOR]

Assessing soil liquefaction potential is a crucial consideration in the seismic design of structures and their seismic stability. The complex nonlinear behavior of the liquefiable soils and the non-deterministic nature of earthquakes make the liquefaction analysis vague. Accordingly, researchers have progressively focused on employing machine learning and mathematical algorithms to address the complexities and uncertainties of evaluating soil liquefaction potential. This paper investigates the performance of fuzzy c-means clustering of incomplete data for assessing liquefaction potential based on cone penetration test (CPT) and shear wave velocity (Vs) field data. The research was conducted using two approaches: (1) whole data strategy; (2) partial distance strategy. The used database contains 786 CPT and 846 Vs records, with specified liquefaction conditions in past earthquake events. We compared the effectiveness and success of this method with traditional deterministic and probabilistic liquefaction evaluation approaches. It was found that the fuzzy c-means clustering model had a comparable predictive ability with other methods and would be reliable when assessing the liquefaction possibility. [ABSTRACT FROM AUTHOR]

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The urge to unveil the secrets of digital visual enhancement has always been a dream for mankind. It has always been an expanding realm of research that has never failed to surprise humanity. In this paper, we have proposed a modified Clustering technique in Fuzzy C-Means named Narrow Fuzzy C-Means Clustering. This clustering method is implemented and fused with U-Net Convolution. The proposed segmentation algorithm uses this unique technique which assists in providing elevated and enhanced outcomes. The suggested approach helps to precisely segment the area of interest from the provided input images. The novel proposal provides an accuracy of 96.5% with a Dice Similarity Co-Efficient (DSC) of 0.94 which tends to determine the exact segmentation of the area of interest with a low false positive rate. [ABSTRACT FROM AUTHOR]

The current state of economic, social ideas, and the advancement of cutting-edge technology are determined by the primary subjects of the contemporary information era, big data. People are immersed in a world of information, guided by the abundance of data that penetrates every element of their surroundings. Smart gadgets, the IoT, and other technologies are responsible for the data's explosive expansion. Organisations have struggled to store data effectively throughout the past few decades. This disadvantage is related to outdated, expensive, and inadequately large storage technology. In the meanwhile, large data demands innovative storage techniques supported by strong technology. This paper proposes the bigdata clustering and classification model with improved fuzzy-based Deep Architecture under the Map Reduce framework. At first, the pre-processing phase involves data partitioning from the big dataset utilizing an improved C-Means clustering procedure. The pre-processed big data is then handled by the Map Reduce framework, which involves the mapper and reducer phases. In the mapper phase. Data normalization takes place, followed by the feature fusion approach that combines the extracted features like entropy-based features and correlation-based features. In the reduction phase, all the mappers are combined to produce an acceptable feature. Finally, a deep hybrid model, which is the combination of a DCNN and Bi-GRU is used for the classification process. The Improved score level fusion procedure is used in this case to obtain the final classification result. Moreover, the analysis of the proposed work has proved to be efficient in terms of classification accuracy, precision, recall, FNR, FPR, and other performance metrics. [ABSTRACT FROM AUTHOR]

The Fuzzy C-means (FCM) algorithm is one of the most widely used algorithms in unsupervised pattern recognition. As the intensity of observation noise increases, FCM tends to produce large center deviations and even overlap clustering problems. The relative entropy fuzzy C-means algorithm (REFCM) adds relative entropy as a regularization function to the fuzzy C-means algorithm, which has a good ability for noise detection and membership assignment to observed values. However, REFCM still tends to generate overlapping clusters as the size of the cluster increases and becomes imbalanced. Moreover, the convergence speed of this algorithm is slow. To solve this problem, modified suppressed relative entropy fuzzy c-means clustering (MSREFCM) is proposed. Specifically, the MSREFCM algorithm improves the convergence speed of the algorithm while maintaining the accuracy and anti-noise capability of the REFCM algorithm by adding a suppression strategy based on the intra-class average distance measurement. In addition, to further improve the clustering performance of MSREFCM for multidimensional imbalanced data, the center overlapping problem and the center offset problem of elliptical data are solved by replacing the Euclidean distance in REFCM with the Mahalanobis distance. Experiments on several synthetic and UCI datasets indicate that MSREFCM can improve the convergence speed and classification performance of the REFCM for spherical and ellipsoidal datasets with imbalanced sizes. In particular, for the Statlog dataset, the running time of MSREFCM is nearly one second less than that of REFCM, and the accuracy of MSREFCM is 0.034 higher than that of REFCM. [ABSTRACT FROM AUTHOR]

Accurate and automatic first-arrival picking is one of the most crucial steps in microseismic monitoring. We propose a method based on fuzzy c-means clustering (FCC) to accurately divide microseismic data into useful waveform and noise sections. The microseismic recordings' polarization linearity, variance, and energy are employed as inputs for the fuzzy clustering algorithm. The FCC produces a membership degree matrix that calculates the membership degree of each feature belonging to each cluster. The data section with the higher membership degree is identified as the useful waveform section, whose first point is determined as the first arrival. The extracted polarization linearity improves the classification performance of the fuzzy clustering algorithm, thereby enhancing the accuracy of first-arrival picking. Comparison tests using synthetic data with different signal-to-noise ratios (SNRs) demonstrate that the proposed method ensures that 94.3% of the first arrivals picked have an error within 2 ms when SNR = −5 dB, surpassing the residual U-Net, Akaike information criterion, and short/long time average ratio approaches. In addition, the proposed method achieves a picking accuracy of over 95% in the real dataset tests without requiring labelled data. [ABSTRACT FROM AUTHOR]

Background and Objectives: scouring is a phenomenon that occurs due to the passage of water flow at the boundaries of contact with other objects in hydraulic structures. The basis of this phenomenon is the creation of a vacuum at the contact boundaries of two environments, due to the water velocity gradient. In general, scouring occurs in the vicinity of various structures such as bridge foundations, submerged spillways, downstream of spillways. With the increase in the dimensions of the scour hole, there is a possibility of overturning and destroying these structures. So that every year significant costs are spent on the repair and reconstruction of various structures, including bridge foundations that have been destroyed due to erosion. Estimation and prediction of scouring around the piers play a significant role to design these structures since with increasing dimensions of scour hole, stability of the pier is threatened; as a result, the structure may be destructed. In this study, scour hole in the vicinity of twin and three piers was estimated by using fuzzy c-means clustering of ANFIS (ANFIS-FCM) network technique. Methodology: Firstly, the parameters affecting scour hole around twin and three piers including Froude number (Fr), the ratio of the pier diameter to the flow depth (D/h), and the ratio of the distance between the piers to the flow depth (d/h) were detected. Subsequently, seven ANFIS-FCM models were defined by means of these dimensional input parameters. It should be stated that 70% of the experimental data were utilized to training the models and 30% of the rest were applied to testing. Then, the superior ANFIS-FCM model and the most important input parameter were introduced by implementing a sensitivity analysis. The premium model as a function of all input parameters simulated the scour values with a reasonable accuracy. Findings: For instance, the correlation coefficient (R), the scatter index (SI), and the Nash-Sutcliff efficiency coefficient (NSC) are respectively computed to be 0.988, 0.106, and 0.976. Furthermore, the Froude number was considered as the most important input parameter. Finally, a computer code was introduced to simulate the scour hole around the twin and three piers. Conclusion: In this study, a neuro-fuzzy technique of the new artificial intelligence method called ANFIS Network Fuzzy C-Mean Classification (ANFIS-FCM) was used to simulate the scour depth in the vicinity of paired and triple bridge foundations. To validate the results of the simulations, 70% of the observed values were used to train the artificial intelligence model and the remaining 30% were used to test it. Then, using the input parameters, seven ANFIS-FCM models were defined, and by analyzing the results Modeling, the best model and the most effective input parameters were introduced. The superior model (ANFIS-FCM 1) simulated scour values with acceptable accuracy. This model estimated scour values according to all input parameters and Fr and D/h parameters were identified as the most effective input parameters. For example, the values of RMSE, MAE and VAF were calculated as 0.025, 0.019 and 97.507 respectively for the test conditions of ANFIS-FCM 1 model (superior model). It should be mentioned that in order to estimate scour depth in the vicinity of double and triple bridge foundations, a computer code was proposed for use in practical work for engineers. One of scour hole, stability of the pier is threatened; as a result, the structure may be destructed. In this study, scour hole in the vicinity of twin and three piers was estimated by using fuzzy c-means clustering of ANFIS (ANFIS-FCM) network technique. Firstly, the parameters affecting scour hole around twin and three piers including Froude number (Fr), the ratio of the pier diameter to the flow depth (D/h), and the ratio of the distance between the piers to the flow depth (d/h) were detected. Subsequently, seven ANFIS-FCM models were defined by means of these dimensional input parameters. It should be stated that 70% of the experimental data were utilized to training the models and 30% of the rest were applied to testing. Then, the superior ANFISFCM model and the most important input parameter were introduced by implementing a sensitivity analysis. The premium model as a function of all input parameters simulated the scour values with a reasonable accuracy. For instance, the correlation coefficient (R), the scatter index (SI), and the Nash-Sutcliff efficiency coefficient (NSC) are respectively computed to be 0.988, 0.106, and 0.976. Furthermore, the Froude number was considered as the most important input parameter. Finally, a computer code was introduced to simulate the scour hole around the twin and three piers. [ABSTRACT FROM AUTHOR]

Diseases and pests in plants/crops are major causes of significant agricultural losses with economic, social and ecological impacts. Therefore, there is a need for early identification of plant diseases and pests through automated systems. Recently, machine learning-based methods have become popular in solving agricultural problems such as plant diseases faced by technically-noob farmers. This work proposes a novel method based on stacking ensemble machine learning to detect plant diseases in Uradbean precisely. Two classifiers: support vector machine (SVM), random forest (RF) are trained on a dataset consists of Uradbean infected and healthy leaf images. These classifiers are stacked with logistic regression (LR) classifier. In the diverse ensemble, LR classifier is used as a meta-learner which enhanced the precision of the disease classification. The fuzzy C-Means clustering with particle swarm optimization is used for image segmentation. Haralick, Hu Moments and color histogram methods are used in feature extraction. During the tests, the proposed model is also compared with pre-trained networks: DenseNet-201, ResNet-50, and VGG19. It achieved an impressive classification accuracy of 96.82 % which is higher than the individual classifiers and pre-trained networks. To validate model performance, it is evaluated on a benchmark public dataset consists of Apple leaf images and achieved 98.30% accuracy. It is observed that ensemble method reflects an advantage over individual models in increasing the classification rates and reducing the computational overhead in comparison to pre-trained networks which struggle due to the issues such as irrelevant features, generation of pertinent characteristics, and noise