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Determining the energy potential of biomass is the first step in selecting the most suitable and efficient energy conversion technology based on regional characteristics. The approach to estimating and determining biomass potential generally uses geospatial technology related to collecting and processing data about mapping an area. Unfortunately, this method is inadequate for simulating the interaction between variables, nor can it provide accurate predictions for the biomass supply chain. As a result, the results obtained from this method tend to be biased and macro, particularly in regions experiencing rapid land-use development. In this paper, the author has developed a clustering methodology with a fuzzy c-means (FCM) algorithm to determine biomass energy potential based on regional characteristics to produce data clusters with high accuracy. Grouping the characteristics of clustering-based areas involves grouping physical or abstract objects into classes or similar objects. [ABSTRACT FROM AUTHOR]

Summary: Sensor‐enabled systems have been used successfully in agricultural, healthcare, commercial, and military application domains. Recently, there has been significant interest in the intelligent applications of sensor‐enabled technologies, particularly in the domains of smart grid, Internet of Vehicles (IoV), body area networks, and the Internet of Things (IoT). In recent research, various protocols and algorithm are developed for effective energy‐efficient routing and energy balancing. These existing models have some issues like high energy consumption and minimum network life time. In order to overcome these existing issues, a novel cluster head selection and routing mechanism in a wireless sensor network (WSN) environment is proposed. The clustering process has been formed by an enhanced Taylor kernel fuzzy C‐means algorithm (TKFC‐means). The cluster head in the group of sensor nodes has been identified based on energy and distance calculation. Finally, the routing has been performed by a novel energy‐efficient Chebyshev fire hawks optimization‐based routing protocol to route data to the edge server, which helps to balance the energy effectively. This protocol takes into account various factors, including distance, cost, residual energy, load, temperature, latency, and overall energy. The proposed model can obtain a throughput value of 82 Mbps for the sensor nodes at 500 and an end‐to‐end delay of 3.6 at 500 sensor nodes. The packet delivery ratio and loss ratio attain 96.4% and 2.7%, respectively, with 500 sensor nodes in the proposed approach. The proposed method consumes 0.45 mJ of energy with 500 nodes. From this analysis, the proposed model can obtain better results than the existing compared models. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoTs) has revolutionized cities, enabling them to become smarter. IoTs play an important role in monitoring the traffic cameras, roads, smart farming, connected vehicles, air quality, water level, humidity, and carbon dioxide pollution levels in city buildings. One of the major challenges of smart cities is the cyber threat to sensitive data. This paper presents an intelligent approach for detecting cyberattacks and mitigating malicious events in IoT-based smart systems. The proposed approach, known as FCM-SWA, hybridizes a fuzzy C-mean (FCM) with a sperm whale algorithm (SWA). In the first step, we use a novel SWA optimization algorithm to enhance the FCM performance and provide effective defenses against various types of smart city attacks. Next, we propose an adaptive threshold strategy to enhance the global search capability of SWA and prevent the algorithm from settling into local optima. Lastly, we present an efficient scaling approach that solves the clustering problem and finds the optimal cluster center, striking a balance between exploration and exploration in the search space. The proposed FCM-SWA model does better than related and state-of-the-art methods in terms of accuracy, detection rate, precision rate, and F1-scores, as shown by experiments on the NSL-KDD, AWID, and BoT-IoT datasets. [ABSTRACT FROM AUTHOR]

This work employs anonymization techniques to safeguard privacy. Data plays a vital role in corporate decision-making in the current information-centric landscape. Various sectors, like banking and healthcare, gather confidential information on a daily basis. This information is disseminated by multiple sources through numerous methods. Securing sensitive data is of paramount importance for any data mining application. This study safeguarded confidential information using an anonymization technique. Several machine learning methodologies have a deficiency in accuracy. The study seeks to generate superior and more precise results compared to alternative methodologies. For large datasets, numerous solutions exhibit increased time complexity and memory use. For huge datasets, numerous solutions require more time and memory. The enhanced fuzzy C-means (FCM) algorithm surpasses existing approaches in terms of both accuracy and information preservation. This study provides a comprehensive analysis of data anonymization utilizing the oppositional fruit fly approach, a technique that enhances privacy. The clustering method being presented utilizes an enhanced version of the FCM algorithm. The secrecy of the recommended oppositional fruit fly algorithm is effective. The comparison demonstrated that the proposed research enhanced both accuracy and privacy in comparison to two existing methods. The existing strategy outperforms data anonymization-based privacy preservation by 82.17%, while the suggested method surpasses it by 94.17%. [ABSTRACT FROM AUTHOR]

Corn is a major commodity after rice in supporting food self-sufficiency in Indonesia. However, due to leaf disease, the quality and quantity of corn plants are greatly reduced. The problem with detecting corn leaf diseases is that the detection method is still manual, making it inefficient and ineffective. Therefore, in this study, disease detection on corn leaves was performed using the Fuzzy C-Means (FCM) and Long Short-Term Memory (LSTM) methods. First, oversampling was carried out to ensure an equal amount of data in all classes, then the corn leaf images were pre-processed before being input into the LSTM algorithm. After completing clustering process in the FCM + LSTM algorithm, the next step involved extracting texture features using the Gray Level Co-occurrence Matrix (GLCM) technique, followed by classification using LSTM. To assess their performance, both algorithms underwent evaluation using the k-fold cross-validation method, and their accuracy and speed were compared. The results of the k-fold cross-validation demonstrated that the FCM + LSTM algorithm achieved an accuracy of 63.53%, whereas the LSTM algorithm achieved an accuracy of 80.24%. In terms of the time required for training and prediction, the LSTM algorithm took 13 min and 18 s for training on corn leaf disease images, while the prediction process only took 1.59 s. The training and prediction time required for the FCM + LSTM algorithm were 65 min and 24 s and 5 min and 44 s, respectively. The conclusion of this study is that the LSTM algorithm has better accuracy and time compared to FCM + LSTM on the dataset used in this study in terms of corn leaf disease detection. [ABSTRACT FROM AUTHOR]

The difficult jobs of investigating illegal activity and identifying offenses have fallen to the Law Enforcement Agencies (LEA). To help the LEA solve crimes, a number of criminal profiling systems have been created; however, the methods used in most systems do not allow for the clustering of criminals according to their behavioral traits. In order to get the best SVM classifier with the best kernel function that best fits our crime data set, this study hybridized the Fuzzy C-Means technique with the support vector machine algorithm in criminal profiling. To reduce the intra-cluster variances, the Fuzzy C-Means (FCM) method was altered by hybridizing it with Support Vector Machine (SVM). This was accomplished by substituting the SVM inner-product distance norm for the Euclidean distance used in the current FCM method to calculate the similarity and dissimilarity measure. Next, additional characteristics were added to the data along with the hybridized algorithm. MATLAB scripts were used to implement the developed strategies. The following criteria assessed the performance: execution time, sensitivity, precision, accuracy, and specificity. The result shows that the RBF kernel function performed best with both OAA and OAO classifiers. The OAA classifier performed best with 93.53% Specificity, 96.89% Precision, and 95.44% Accuracy over OAO and the pairwise classifier (BSVM). Therefore, the RBF kernel function using the OAA classifier is recommended to best suit our crime data set for criminal profiling, contributing to Sustainable Development Goal (SDG) 16. [ABSTRACT FROM AUTHOR]

In the field of animal husbandry, the process of animal identification and recognition is challenging, time-consuming, and costly. In Türkiye, the ear tagging method is widely used for animal identification. However, this traditional method has many significant disadvantages such as lost tags, the ability to copy and replicate tags, and negative impacts on animal welfare. Therefore, in some countries, biometric identification methods are being developed and used as alternatives to overcome the disadvantages of traditional methods. Retina vessel patterns are a biometric identifier with potential in biometric identification studies. Preprocessing steps and vessel segmentation emerge as crucial steps in image processing-based identification and recognition systems. In this study, conducted in the Kars region of Türkiye, a series of preprocessing steps were applied to retinal images collected from cattle. Fuzzy c-means, k-means, and level-set methods were utilized for vessel segmentation. The segmented vascular structures obtained with these methods were comparatively analyzed. As a result of the comparison, it was observed that all models successfully performed retinal main vessel structure segmentation, fine vessels were successfully identified with fuzzy c-means, and spots in retinal images were detected only by the level-set method. Evaluating the success of these methods in identification, recognition, or disease detection will facilitate the development of successful systems.

This study aims to compare the performance of three clustering algorithms, namely Fuzzy C-Means, K-Means, and DBSCAN, in grouping houses based on their specifications and prices. The data used includes features such as price, building area, land area, number of bedrooms, number of bathrooms, and availability of garages. The performance of these algorithms was evaluated using Silhouette Score and Davies-Bouldin Score to determine the quality of cluster separation. The results indicate that K-Means achieved the best performance with the highest Silhouette Score of 0.7702 for two clusters, followed by Fuzzy C-Means, which excelled in handling overlapping clusters. DBSCAN, while effective in detecting outliers, showed suboptimal performance for this housing dataset. These findings suggest that K-Means is the most suitable clustering method for housing data, while Fuzzy C-Means and DBSCAN can serve as alternatives depending on the data characteristics. This research is expected to assist in making the house searching and classification process more efficient and provide additional insights for developers in shaping housing market strategies.

Abstract Key frame extraction is very important in video summarization and content-based video analysis to address the problem of data redundancy in a video. Key frame extraction enables quick navigation and expert video arrangement in many applications. The visually impaired can benefit from the use of key frame extraction for rapid object recognition and tracking. Most key frame extraction techniques consider only a single visual feature instead of multiple features or full pictorial information of the video. This study proposes a key frame extraction method from a video that (i) first removes insignificant frames by pre-processing, (ii) second, four visual and structural feature differences among the consecutive frames are extracted and aggregated to identify informative frames, (iii) third, to cluster the obtained frames, a hybrid FCM-AHA method is proposed by combining Fuzzy C-means(FCM) with artificial hummingbird optimization algorithm (AHA) to circumvent the local minima trapping problem of FCM, and finally, from each cluster, the two frames having greatest Euclidean distance from all the other frames within a cluster is selected as key frames to remove redundant frames. Experimental results on the Open video and YouTube datasets show that the suggested method outperforms state-of-the-art methods both in terms of subjective qualitative analysis and objective quantitative evaluation, e.g., Precision, Recall, and F-score. Further, results are also taken on real video to demonstrate its applicability in real-life applications.

This study addresses the critical issue of building element defect management, focusing on prevalent concerns like cracks, dampness, and surface degradation. Recognizing the limitations introduced by human subjectivity in defect classification, the research proposes a novel, data-driven approach to automate the process. The methodology leverages extensive field data collection, encompassing 500 painted walls from 24 geographically dispersed buildings, to develop a robust fuzzy logic-based building element condition assessment model. The model categorizes element conditions (C1-C5) and classifies damage severity into five groups: no damage, slight damage, moderate damage, extensive damage, and complete damage, with nuanced precision. The efficacy of the fuzzy C-Means clustering is rigorously validated through the application of silhouette index and Davis-Bouldin index, ensuring optimal cluster formation and enhanced model accuracy. A real-world case study involving an office building exemplifies the model's practical application, showcasing its effectiveness in minimizing human error during defect identification and classification. This research contributes a sophisticated defect management framework informed by extensive field data and validated fuzzy logic, ultimately leading to demonstrably improved building quality and reduced operational costs within the construction industry.

For biodiversity conservation and the development of protected areas, it is essential to create strategic plans that ensure the preservation and sustainable use of natural resources. Biogeography plays a crucial role in supporting these efforts by identifying and categorizing geographic areas (regionalization) that represent different biotas, as well as recognizing patterns in biodiversity distribution. Another application of regionalization is in planning species sampling and inventories. Developing a species list is vital for monitoring and understanding diversity patterns. This study focuses on the Palearctic region, specifically the areas between Morocco, the Iberian Peninsula, and France. Its aim is to compare different clustering algorithms—such as K-means++, DBSCAN, PD-clustering, Infomap, and federated heuristic optimization based on fuzzy clustering—with a reference regionalization, using environmental and soil data. Various spatial contiguity approaches were applied, including the third-degree polynomial model and principal coordinates. The results demonstrated that the hybrid approach offers a robust solution in the construction of the regions and that K-means++ and PDC produced regions with strong spatial similarity to the reference regionalization, closely aligning with the expected number of regions, especially at the biome level. Our study shows that a purely statistical regionalization can approximate a global reference regionalization, making it reproducible. [ABSTRACT FROM AUTHOR]

In the manuscript, an automatic approach for analysis and detection of various stages of retinopathy defects in human eyes has been proposed. The approach consists of a robust preprocessing technique of the retina fundus image to mitigate the effects of noise and poor lightening in the image. To realize a compressive analysis of the defects, methods for extracting blood vessels and optic disc in the fundus image has also been developed. Adaptive Histogram Equalization (AHE), median filtering and Connected Component Analysis techniques were used in separating blood vessels and optic disc from each fundus image. The pre-processing utilizes canny edge detection and Morphological Closing on the fundus image. An interval type-2 fuzzy (IT2F) clustering is applied to segments an input image into four clusters. These four clusters from the fuzzy segmentation are further analyzed to extract various stages of retinopathy abnormalities (e.g., Hemorrhage, hard exudates etc.). The extracted blood vessels and optic disc are removed from the analysis to enhance the defects detection process. Experiments were conducted on DIARETDB1 database. The experimental results obtained are validated using the ground-truth images contained in DIARETDB1 database. Impressive results are recorded throughout the experiment. Hard-Exudates and Hemorrhage were detected from the fundus images and results from similarity indexes such as, accuracy (94.11%) sensitivity (93.03%) and specificity (98.45%) were recorded. [ABSTRACT FROM AUTHOR]

We introduce an unsupervised feature selection method based on regularized weighted Fuzzy C-Means (WRFCM) clustering. When the target task is clustering, our objective should be to select a subset of features that can generate the same/similar partition matrix to the partition matrix obtained from the original high dimensional data by a clustering algorithm. To achieve this we propose a novel objective function keeping in view the Fuzzy-C-Means (FCM) clustering algorithm. This approach realizes feature selection within the WRFCM framework, emphasizing features to maintain the FCM-based target partition. We evaluate our method using Normalized Mutual Information (NMI), Adjusted Rand Index (ARI) and Kuhn-Munkres index (KM-index). NMI, and ARI measure the agreement between clusters, i.e, the partition in the lower dimension and the partition of the original data. On the other hand, KM-index measures the disagreement between the two partitions. Experimental results on synthetic and real datasets showcase our method's efficacy in selecting informative features. This approach fills a crucial gap in unsupervised feature selection, making it valuable for real-world applications. The approach is very general in the sense that the target partition can be generated by any clustering algorithm or even by the actual class labels of the data, when they are available. [ABSTRACT FROM AUTHOR]

Key frame extraction is very important in video summarization and content-based video analysis to address the problem of data redundancy in a video. Key frame extraction enables quick navigation and expert video arrangement in many applications. The visually impaired can benefit from the use of key frame extraction for rapid object recognition and tracking. Most key frame extraction techniques consider only a single visual feature instead of multiple features or full pictorial information of the video. This study proposes a key frame extraction method from a video that (i) first removes insignificant frames by pre-processing, (ii) second, four visual and structural feature differences among the consecutive frames are extracted and aggregated to identify informative frames, (iii) third, to cluster the obtained frames, a hybrid FCM-AHA method is proposed by combining Fuzzy C-means(FCM) with artificial hummingbird optimization algorithm (AHA) to circumvent the local minima trapping problem of FCM, and finally, from each cluster, the two frames having greatest Euclidean distance from all the other frames within a cluster is selected as key frames to remove redundant frames. Experimental results on the Open video and YouTube datasets show that the suggested method outperforms state-of-the-art methods both in terms of subjective qualitative analysis and objective quantitative evaluation, e.g., Precision, Recall, and F-score. Further, results are also taken on real video to demonstrate its applicability in real-life applications. [ABSTRACT FROM AUTHOR]

This article presents an inventory classification method that provides more accurate results in the white goods factory, which will contribute to sustainability, sustainability economics, and supply chain management targets. A novel inventory classification application is presented with real-world data. Two different datasets are used, and these datasets are compared to each other. These larger dataset is Stock Keeping Unit (SKU)-based (6.032 SKUs), and the smaller one is product-group-based (270 product groups). In the first phase, Artificial Intelligence (AI) clustering methods that have not been used in the field of inventory classification, to our knowledge, are applied to these datasets; the results are obtained and compared using K-Means, Gaussian mixture, agglomerative clustering, and spectral clustering methods. In the second stage, an autoencoder is separately hybridized with the AI clustering methods to develop a novel approach to inventory classification. Fuzzy C-Means (FCM) is used in the third step to classify inventories. At the end of the study, these nine different methodologies ("K-Means, Gaussian mixture, agglomerative clustering, spectral clustering" with and without the autoencoder and Fuzzy C-Means) are compared using two different datasets. It is shown that the proposed new hybrid method gives much better results than classical AI methods. [ABSTRACT FROM AUTHOR]

Voice is perceived as a form of biometrics which communicates valuable and rich information pertinent to an individual, such as his or her identity, gender, accent, age and emotion. Speaker identification denotes the task of identifying speakers based on their intrinsic voice characteristics. This study proposes a text-independent speaker identification system based on principal component analysis (PCA), fuzzy C-means (FCM) along with deepForest called PCAFCM-deepForest. The proposed approach is evaluated under neutral and adverse talking environments. Given this approach, we assessed our proposed model architecture on five benchmark corpora, namely private Arabic Emirati-accented speech dataset, public English dataset; Crowd-sourced emotional multimodal actors dataset (CREMA), public German database; Berlin database of emotional speech (EmoDB), public Chinese and English; emotional speech database (ESD), and public French dataset; public Canadian French emotional (CaFE) speech dataset. Our analysis shows that the performance of speaker identification has been immensely increased (greatly improved) when fuzzy logic and PCA are both applied to the extracted mel-frequency cepstral coefficients (MFCC). Speaker identification performance achieved by the proposed PCAFCM-deepForest is superior to that obtained by deepForest alone, FCM-deepForest as well as convolutional neural network (CNN). Besides, it surpasses the following conventional models: Random forest and support vector machine (SVM). Our findings demonstrate that the attained average speaker identification accuracy is equivalent to 98.20% using the Emirati database; an average performance which outperforms the existing frameworks. Moreover, PCAFCM-deepForest is fine-tuned using the grid search algorithm, and the achieved complexity is much less than that of CNN. [ABSTRACT FROM AUTHOR]

In the era of vast information, individuals are immersed in choices when purchasing goods and services. Recommender systems (RS) have emerged as vital tools to navigate these excess options. However, these systems encounter challenges like data sparsity, impairing their effectiveness. This paper proposes a novel approach to address this issue and enhance RS performance. By integrating user demographic data, singular value decomposition (SVD) clustering, and semantic similarity in collaborative filtering (CF), we introduce the UDIS method. This method amalgamates four prediction types—user-based CF (U), demographic-similarity-based (D), item-based CF (I), and semantic-similarity-based (S). UDIS generates separate predictions for each category and evaluates four different merging techniques—the average, max, weighted sum, and Shambour methods—to integrate these predictions. Among these, the average method proved most effective, offering a balanced approach that significantly improved precision and accuracy on the MovieLens dataset compared to alternative methods. [ABSTRACT FROM AUTHOR]

The most significant issue today is electricity theft (ET) which causes much loss to electricity boards. The development of smart grids (SGs) is crucial for ET detection (ETD) because these systems produce enormous amounts of data, including information on customer consumption, which can be used to identify ET using machine learning and deep learning (DL) techniques. However, the existing models majorly suffers with lower prediction accuracy because of over-fitting and dataset imbalancing issues. Therefore, to overcome these shortcomings, this paper proposes a novel DL approach for ETD in the Internet of Things-based SGs using parameter-tuned bidirectional long short-term memory (PTBiLSTM) with pre-trained feature learning model. The proposed system mainly comprises '4' phases: preprocessing, dataset balancing, feature selection, and ETD. Initially, the consumers' electricity consumption data are collected from the theft detection dataset 2022 (TDD2022) dataset. Then, the data balancing is carried out by using Gaussian distribution, including fuzzy C-means approach to handle the imbalance data. Afterward, the meaningful features from the balanced dataset are extracted using the hard swish and dropout layer included residual neural network-50 (ResNet-50) model. Finally, the ETD is done, which utilizes a PTBiLSTM. The proposed models' performance is evaluated using different performance metrics like accuracy, precision, recall, f-measure, the area under the curve, and kappa. The outcomes proved the efficiency of the proposed method over other related schemes in the ETD of SGs. [ABSTRACT FROM AUTHOR]

The fuzzy C-means (FCM) clustering algorithm is a widely used unsupervised learning method known for its ability to identify natural groupings within datasets. While effective in many cases, FCM faces challenges such as sensitivity to initial cluster assignments, slow convergence, and difficulty in handling non-linear and overlapping clusters. Aimed at these limitations, this paper introduces a novel fractional fuzzy C-means (Frac-FCM) algorithm, which incorporates fractional derivatives into the FCM framework. By capturing non-local dependencies and long memory effects, fractional derivatives offer a more flexible and precise representation of data relationships, making the method more suitable for complex datasets. Additionally, a genetic algorithm (GA) is employed to optimize a new least-squares objective function that emphasizes the geometric properties of clusters, particularly focusing on the Fukuyama–Sugeno and Xie–Beni indices, thereby enhancing the balance between cluster compactness and separation. Furthermore, the Frac-FCM algorithm is evaluated on several benchmark datasets, including Iris, Seed, and Statlog, and compared against traditional methods like K-means, SOM, GMM, and FCM. The results indicate that Frac-FCM consistently outperforms these methods in terms of the Silhouette and Dunn indices. For instance, Frac-FCM achieves higher Silhouette scores of most cases, indicating more distinct and well-separated clusters. Dunn's index further shows that Frac-FCM generates clusters that are better separated, surpassing the performance of traditional methods. These findings highlight the robustness and superior clustering performance of Frac-FCM. The Friedman test was employed to enhance and validate the effectiveness of Frac-FCM. [ABSTRACT FROM AUTHOR]

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Structural and photometric anomalies in the brain magnetic resonance images (MRIs) affect the segmentation performance. Moreover, a sudden change in intensity between two boundaries of the brain tissues makes it prone to data uncertainty, resulting in the misclassification of the pixels lying near the cluster boundaries. The discrete cosine transform (DCT) domain-based filtering is an effective way to deal with structural and photometric anomalies, while the intuitionistic fuzzy C-means (IFCM) clustering can handle the uncertainty using the intuitionistic fuzzy set (IFS) theory. In this background, we propose two novel approaches, namely, the DCT-based intuitionistic fuzzy C-means (DCT-IFCM) and the DCT-based local information IFCM (DCT-LIFCM), which effectively deal with the Rician and Gaussian noises and also handle the data uncertainty problem to provide high segmentation accuracy. The DCT-IFCM approach performs the histogram-based segmentation, while the DCT-LIFCM uses the pixel-wise computation to include the spatial information. Although the DCT-LIFCM delivers slightly better performance than the DCT-IFCM, the latter is very fast in providing equally high segmentation accuracy. An exhaustive performance analysis is provided to demonstrate the superior performance of the proposed algorithms compared with the state-of-the-art algorithms, including those based on the DCT-based filtering approach and the IFS theory. [ABSTRACT FROM AUTHOR]

In this study, we redefine FCM algorithm by integrating fuzzy set theory, fuzzy metrics, and Sugeno negation principles. This innovative approach overcomes the limitations inherent in conventional machine learning models, especially in situations characterized by uncertainty, noise, and ambiguity. Our model utilizes the membership degrees from fuzzy set theory, and transforms the concept of proximity defined by fuzzy metrics into a minimization problem. This transformation is achieved using a linguistic negation operator, which is crucial for optimizing FCM algorithm's objective function. A significant innovation in our research is the use of GA for optimizing parameters within the contexts of fuzzy metrics and Sugeno negation. The precise optimization capabilities of GA greatly enhance the sensitivity and adaptability of FCM algorithm, thereby improving overall performance. By leveraging the meticulous parameter adjustments provided by GA, our approach has shown superior results in practical applications, such as brain MRI image segmentation, surpassing traditional methods. Experimental results highlight the considerable enhancements our proposed FCM algorithms bring over existing methods across various performance metrics. In conclusion, this study makes a valuable addition to the field of fuzzy-based machine learning methodologies. It combines the optimization strength of GA with the flexible classification capabilities of fuzzy logic. The integration of Sugeno negation and fuzzy metrics not only improves the accuracy and precision of FCM algorithm but also provides significant benefits in handling complex and ambiguous datasets. This research signifies a major advance in machine learning and fuzzy logic, setting the stage for future applications and studies. [ABSTRACT FROM AUTHOR]

The classification of galaxies has significantly advanced using machine learning techniques, offering deeper insights into the universe. This study focuses on the typology of galaxies using data from the Galaxy Zoo project, where classifications are based on the opinions of non-expert volunteers, introducing a degree of uncertainty. The objective of this study is to integrate Fuzzy C-Means (FCM) clustering with explainability methods to achieve a precise and interpretable model for galaxy classification. We applied FCM to manage this uncertainty and group galaxies based on their morphological characteristics. Additionally, we used explainability methods, specifically SHAP (SHapley Additive exPlanations) values and LIME (Local Interpretable Model-Agnostic Explanations), to interpret and explain the key factors influencing the classification. The results show that using FCM allows for accurate classification while managing data uncertainty, with high precision values that meet the expectations of the study. Additionally, SHAP values and LIME provide a clear understanding of the most influential features in each cluster. This method enhances our classification and understanding of galaxies and is extendable to environmental studies on Earth, offering tools for environmental management and protection. The presented methodology highlights the importance of integrating FCM and XAI techniques to address complex problems with uncertain data. [ABSTRACT FROM AUTHOR]

Modelling and controlling an electrical Power Generation System (PGS), which consists of an Internal Combustion Engine (ICE) linked to an electric generator, poses a significant challenge due to various factors. These include the non-linear characteristics of the system's components, thermal effects, mechanical vibrations, electrical noise, and the dynamic and transient impacts of electrical loads. In this study, we introduce a fuzzy modelling identification approach utilizing the Takagi–Sugeno (T–S) structure, wherein model and control parameters are optimized. This methodology circumvents the need for deriving a mathematical model through energy balance considerations involving thermodynamics and the non-linear representation of the electric generator. Initially, a non-linear mathematical model for the electrical power system is obtained through the fuzzy c-means algorithm, which handles both premises and consequents in state space, utilizing input–output experimental data. Subsequently, the Particle Swarm Algorithm (PSO) is employed for optimizing the fuzzy parameter m of the c-means algorithm during the modelling phase. Additionally, in the design of the Parallel Distributed Compensation Controller (PDC), the optimization of parameters pertaining to the poles of the closed-loop response is conducted also by using the PSO method. Ultimately, numerical simulations are conducted, adjusting the power consumption of an inductive load. [ABSTRACT FROM AUTHOR]

Fuzzy C-Means is a clustering algorithm widely used in many applications. However, its computational complexity is very large, which prevents its use for large problem instances. Therefore, a hybrid improvement is proposed for the algorithm, which considerably reduces the number of iterations and, in many cases, improves the solution quality, expressed as the value of the objective function. This improvement integrates two heuristics, one in the initialization phase and the other in the convergence phase or the convergence criterion. This improvement was called HPFCM. A set of experiments was designed to validate this proposal; to this end, four sets of real data were solved from a prestigious repository. The solutions obtained by HPFCM were compared against those of the Fuzzy C-Means algorithm. In the best case, reductions of an average of 97.65% in the number of required iterations and an improvement in quality solution of 82.42% were observed when solving the SPAM dataset. Finally, we consider that the proposed heuristics may inspire improvements in other specific purpose variants of Fuzzy C-Means. [ABSTRACT FROM AUTHOR]

Metal dichalcogenides are a kind of chemical substance that consists of a metal atom paired with chalcogen elements such as selenium and sulphur. These materials have distinctive electrical and optical characteristics, making them fascinating for a variety of applications, including electronics and optoelectronics. Growth examination of metal dichalcogenide thin films entails analyzing their controlled deposition and crystallization. Understanding growth processes, substrate interactions and controlling parameters like as temperature and precursor concentration are critical for producing high-quality films with the appropriate characteristics, establishing the way for developments in nanotechnology and device manufacturing. Throughout this research, we employed the Machine learning (ML) enabled Reflection High-Energy Electron Diffraction (RHEED) analytical approach to examine the development of two-dimensional (2D thin layers of dichalcogenides (ReSe2) made of transition metals on graphene substrates using Molecular Beam Epitaxy (MBE). Independent Component Analysis (ICA) and the Fuzzy C-Means approach were implemented to determine different patterns and represent the pattern growths. To decrease the original dataset's dimensionality, we employed 20 Independent Components (ICs) and each RHEED image was distributed to the closest centroid, which resulted in the dataset being clustered using Fuzzy C-Means. [ABSTRACT FROM AUTHOR]

Studying user electricity consumption behavior is crucial for understanding their power usage patterns. However, the traditional clustering methods fail to identify emerging types of electricity consumption behavior. To address this issue, this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage patterns. The fuzzy C-means clustering algorithm is then used to analyze 6 months of electricity consumption data in 2017 from energy storage equipment, agricultural drainage irrigation, port shore power, and electric vehicles. Finally, the proposed method is validated through experiments, where the Davies-Bouldin index and profile coefficient are calculated and compared. Experiments showed that the optimal number of clusters is 4. This study demonstrates the potential of using a fuzzy C-means clustering algorithm in identifying emerging types of electricity consumption behavior, which can help power system operators and policymakers to make informed decisions and improve energy efficiency. [ABSTRACT FROM AUTHOR]

Oil palm fruit, a high-demand and economically valuable crop, faces grading challenges in Malaysia due to labour-intensive methods, resulting in unharvested ripe fruits and yield losses. Extensive research has been conducted on outdoor palm fruit classifiers, but most high-accuracy research in this field relies heavily on large image datasets. This creates a trade-off between employing more sophisticated deep-learning approaches that require extensive data and utilising traditional techniques that have lower data requirements. Therefore, the aim is to propose an outdoor image-based fresh fruit bunch (FFB) classification system, emphasising pre-processing technique and feature extraction techniques suitable for limited datasets. To achieve this, the FFB was localised within the original images using the YOLOv4-Tiny algorithm. Subsequently, a salient segmentation method utilising superpixel-based Fast Fuzzy C-means (FFCM) clustering is employed to remove the background from the images. Next, colour moment and opposite colour local binary pattern (OCLBP) features are extracted from the segmented images to capture important information for classification. Finally, the extracted features are fed into a Multilayer Perceptron (MLP) classifier, which enables the system to predict five classes: damaged, empty, unripe, ripe, and overripe. The developed system demonstrated a commendable performance in accurately classifying fruit bunches, achieving an accuracy of 93.68 %. In conclusion, the proposed system effectively addresses the issue of unripe fruit harvesting and contributes to the advancement of state-of-the-art methods in classifying outdoor FFB images.

Stunting, or short stature, is a growth disorder usually caused by chronic dietary deficiencies from the prenatal stage to early childhood, typically becoming evident in children after the age of 2. Stunting cases in Yogyakarta Province experienced a decline in 2020. With this development, the government aims to achieve zero stunting in Yogyakarta Province by 2024. To support this goal, a research study was conducted in 2021 to analyze family factors associated with stunting risks in Yogyakarta Province. The study aimed to assist the government in addressing the issue and achieving the target. In this research, a hierarchical clustering algorithm using the Ward technique and a non-hierarchical clustering algorithm using the Fuzzy C-Means (FCM) approach were applied. The optimal number of clusters was determined using the average distance and figure of merit approach. Stability validation, which also used the average distance and figure of merit approach, demonstrated that the best results were achieved by the non-hierarchical clustering algorithm employing FCM. As a result, six clusters were identified: cluster 1 with 5 sub-districts, cluster 2 with 18 sub-districts, cluster 3 with 21 sub-districts, cluster 4 with 17 sub-districts, cluster 5 with 14 sub-districts, and cluster 6 with 3 sub-districts.

Abstract The long-term loss of distribution network in the process of distribution network development is caused by the backward management mode of distribution network. The traditional analysis and calculation methods of distribution network loss can not adapt to the current development environment of distribution network. To improve the accuracy of filling missing values in power load data, particle swarm optimization algorithm is proposed to optimize the clustering center of the clustering algorithm. Furthermore, the original isolated forest anomaly recognition algorithm can be used to detect outliers in the load data, and the coefficient of variation of the load data is used to improve the recognition accuracy of the algorithm. Finally, this paper introduces a breadth-first-based method for calculating line loss in the context of big data. An example is provided using the distribution network system of Yuxi City in Yunnan Province, and a simulation experiment is carried out. And the findings revealed that the error of the enhanced fuzzy C-mean clustering algorithm was on average − 6.35, with a standard deviation of 4.015 in the situation of partially missing data. The area under the characteristic curve of the improved isolated forest algorithm subjects in the case of the abnormal sample fuzzy situation was 0.8586, with the smallest decrease, based on the coefficient of variation, and through the refinement of the analysis, it was discovered that the feeder line loss rate is 7.62%. It is confirmed that the suggested technique can carry out distribution network line loss analysis fast and accurately and can serve as a guide for managing distribution network line loss.

In-vehicle communication has been optimized day to day to keep updated of the technologies. Control area network (CAN) is used as a standard communication method because of its efficient and reliable connection. However, CAN is prone to several network level attacks because of its lack in security mechanisms. Various methods have been introduced to incorporate this in CAN. We proposed an unsupervised method of intrusion detection for in-vehicle communication networks by combining the optimal feature extracting ability of autoencoders and more precise clustering using fuzzy C-means (FCM). The proposed method is light weight and requires less computation time. We performed an extensive experiment and achieved an accuracy of 75.51 % with the ML350 in-vehicle intrusion dataset. By experimental result, the proposed method also works better for other intrusion detection problems like wireless intrusion detection datasets such as WNS-DS with accuracy of 84.05 % and network intrusion detection datasets such as KDDCup with accuracy 60.63 % , UNSW_NB15 with accuracy 73.62 % and Information Security Center of Excellence (ISCX) with accuracy 74.83 %. Overall, the proposed method outperforms the existing methods and avoids labeled datasets when training an in-vehicle intrusion detection model. The results of the experiment of our proposed method performed on various intrusion detection datasets indicate that the proposed approach is generalized and robust in detecting intrusions and can be effectively deployed in real time to monitor CAN traffic in vehicles and proactively alert during attacks.

Big Data (BD) is a concept that deals with enormous amounts of data storage, processing, and analysis. With the exponential advancement in the evolution of cloud computing domains in healthcare (HC), the security and confidentiality of medical records have evolved into a primary consideration for HC services and applications. There needs to be more than the present-day cryptosystems to address these troubles. Therefore, this paper introduces a novel Three-Factor Authentication (3FA) and optimal Map-Reduce (MR) framework for secure BD transmission over the cloud with Secure Hashing Authentication XOR-ed Elliptical Curve Cryptography (SHAXECC). The authentication procedure is initially carried out with the SHA-512 algorithm, which protects the network from unauthorized access. Next, data deduplication is done using the SHA-512 algorithm to eliminate duplicate files. After that, an optimal MR design is introduced to handle a large amount of BD. In an optimal MR, the mapper uses the Modified Fuzzy C-means (MFCM) clustering approach to initially form the BD clusters. Then, the reducer uses the Levy Flight and Scoring Mutation-based Chimp Optimization Algorithm (LSCOA) to form final BD clusters. Finally, the SHAXECC is used to transmit the data securely. Experiments are performed to compare the superiority of the proposed technique with the existing techniques in terms of some performance measures. The proposed approach outperformed other existing models concerning clustering and security measures. So, the proposed model is the best for data protection and privacy in cloud-enabled HC data. [ABSTRACT FROM AUTHOR]

Possibilistic fuzzy c-means (PFCM) clustering is a kind of hybrid clustering method based on fuzzy c-means (FCM) and possibilistic c-means (PCM), which not only has the stability of FCM but also partly inherits the robustness of PCM. However, as an extension of FCM on the objective function, PFCM tends to find a suboptimal local minimum, which affects its performance. In this paper, we rederive PFCM using the majorization-minimization (MM) method, which is a new derivation approach not seen in other studies. In addition, we propose an effective optimization method to solve the above problem, called MMPFCM. Firstly, by eliminating the variable V ∈ R p × c , the original optimization problem is transformed into a simplified model with fewer variables but a proportional term. Therefore, we introduce a new intermediate variable s ∈ R c to convert the model with the proportional term into an easily solvable equivalent form. Subsequently, we design an iterative sub-problem using the MM method. The complexity analysis indicates that MMPFCM and PFCM share the same computational complexity. However, MMPFCM requires less memory per iteration. Extensive experiments, including objective function value comparison and clustering performance comparison, demonstrate that MMPFCM converges to a better local minimum compared to PFCM. [ABSTRACT FROM AUTHOR]

This article presents a classification of Acoustic Emission (AE) signals from AlSi10Mg specimens produced via Selective Laser Melting (SLM). Tensile tests characterized the mechanical properties of specimens printed in different orientations (X, Y, Z, 45°). Initially, a study quantified damage modes based on the stress-strain curve and cumulative AE energy. AE signals for each specimen (X, Y, 45°, Z), across deformation stages (elastic and plastic), and damage modes were analyzed using continuous wavelet transform to extract time-frequency features. A novel convolutional neural network, based on artificial bee colonies and fuzzy C-means, was developed for scalogram classification. Data augmentation with Gaussian white noise enhanced the approach. Cross-validation ensured robustness against overfitting and suboptimal local maxima. Evaluation metrics, including the confusion matrix, precision-recall curve, and F1 score, demonstrated the algorithm's high accuracy of 92.6%, precision-recall curve of 92.5%, and F1 score of 92.5% for AE signals based on printing direction (X, Y, 45°, Z). The study highlighted the potential for improving AE signal classification related to elastic and plastic deformation stages with 100% accuracy. For damage modes, the algorithm achieved a confusion matrix accuracy of 90.6%, a precision-recall curve of 90.4%, and an F1 score of 90.5%. This approach demonstrates high accuracy in classifying AE signals across different printing orientations, deformation stages, and damage modes of AlSi10Mg specimens manufactured through SLM. [ABSTRACT FROM AUTHOR]

Hypertensive disorders in pregnancy, which include preeclampsia, eclampsia, and chronic hypertension, complicate approximately 10% of all pregnancies in the world, constituting one of the most serious causes of mortality and morbidity in gestation. To help predict the occurrence of hypertensive disorders, a study based on algorithms that help model this health problem using mathematical tools is proposed. This study proposes a fuzzy c-means (FCM) model based on the Takagi–Sugeno (T-S) type of fuzzy rule to predict hypertensive disorders in pregnancy. To test different modeling methodologies, cross-validation comparisons were made between random forest, decision tree, support vector machine, and T-S and FCM methods, which achieved 80.00%, 66.25%, 70.00%, and 90.00%, respectively. The evaluation consisted of calculating the true positive rate (TPR) over the true negative rate (TNR), with equal error rate (EER) curves achieving a percentage of 20%. The learning dataset consisted of a total of 371 pregnant women, of which 13.2% were diagnosed with a condition related to gestational hypertension. The dataset for this study was obtained from the Secretaría de Salud del Estado de Hidalgo (SSEH), México. A random sub-sampling technique was used to adjust the class distribution of the data set, and to eliminate the problem of unbalanced classes. The models were trained using a total of 98 samples. The modeling results indicate that the T-S and FCM method has a higher predictive ability than the other three models in this research. [ABSTRACT FROM AUTHOR]

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In order to reduce network energy consumption and prolong the network lifetime in wireless sensor networks, a data fusion algorithm named CFLDF is proposed. Firstly, upon completion of the arrangement of network nodes, network clustering is achieved using fuzzy c-means optimized by the improved butterfly optimization algorithm, and a data fusion model is established on the clustering structure. Then, reliable data is sent to the cluster head by the nodes with the assistance of a fuzzy logic controller, and data fusion is performed by the cluster head using a fuzzy logic algorithm. Finally, cluster heads transmit the fused data to the base station. Finally, the fused data is transmitted to the base station by the cluster heads. Simulation experiments are conducted to evaluate the CFLDF algorithm against the LEACH, LEACH-C, and SEECP algorithms. The results demonstrate that network energy consumption is effectively reduced and the network lifetime is extended by the CFLDF algorithm. [ABSTRACT FROM AUTHOR]

River water quality could be determined by understanding the capacity of pollutants in a water body. Fuzzy C-Means (FCM) is one of the fuzzy clustering methods for determining river water quality by measuring water quality parameters, that is, dissolved oxygen (DO) and total dissolved solids (TDS). The FCM algorithm is an effective fuzzy clustering algorithm for grouping data but often produces local and inconsistent optimal solutions due to the partition matrix's random initialisation process. Therefore, this study proposes to modify the FCM algorithm to be precise in the partition matrix initialisation process using several distance concepts. The purpose of the proposed algorithm modification is to get more consistent FCM clustering results and minimise stop iterations. The validation process for the clustering results uses the FCM algorithm, and the FCM modification algorithm uses three parameters, namely the Partition Coefficient Index (PCI), Partition Entropy Index (PEI) and Silhouette Score (SS). The experiments were conducted with three replications and using various distance concepts. The results showed that the number of iterations stopped in the FCM algorithm has different values for PCI, PEI, SS, and stop iterations and objective functions in each trial. On the contrary, the FCM modification algorithm has consistent PCI, PEI, and SS values, and the number of iterations stops with fewer iterations. Therefore, the modified algorithm for initialising the partition matrix can be used in the fuzzy C-means clustering algorithm. [ABSTRACT FROM AUTHOR]

In clinical research, it is crucial to segment the magnetic resonance (MR) brain image for studying the internal tissues of the brain. To address this challenge in a sustainable manner, a novel approach has been proposed leveraging the power of unsupervised clustering while integrating conditional spatial properties of the image into intuitionistic clustering technique for segmenting MRI images of brain scans. In the proposed technique, an Intuitionistic-based clustering approach incorporates a nuanced understanding of uncertainty inherent in the image data. The measure of uncertainty is achieved through calculation of hesitation degree. The approach introduces a conditional spatial function alongside the intuitionistic membership matrix, enabling the consideration of spatial relationships within the image. Furthermore, by calculating weighted intuitionistic membership matrix, the algorithm gains the ability to adapt its smoothing behavior based on the local context. The main advantages are enhanced robustness with homogenous segments, lower sensitivity to noise, intensity inhomogeneity and accommodation of degree of hesitation or uncertainty that may exist in the real-world datasets. A comparative analysis of synthetic and real datasets of MR brain images proves the efficiency of the suggested approach over different algorithms. The paper investigates how the suggested research methodology performs in medical industry under different circumstances including both qualitative and quantitative parameters such as segmentation accuracy, similarity index, true positive ratio, false positive ratio. The experimental outcomes demonstrate that the suggested algorithm outperforms in retaining image details and achieving segmentation accuracy. [ABSTRACT FROM AUTHOR]

The prevalence of Recommender Systems (RS) has surged due to the widespread growth of the Internet and its related technologies. The efficiency of the RS is dependent on the quality of the underlying datasets used for training. Sparse datasets present a challenge for training such systems and negatively impacts their efficiency. This research paper presents novel synergistic approaches that combine Fuzzy C-Means (FCM), Restricted Boltzmann Machines (RBM), and Extreme Learning Machines (ELM), to enhance Collaborative Filtering (CF) in RS. The study introduces two clustering-based approaches, FCM-ELM and FCM-RBM-ELM, to address the issue of non-optimal hidden biases and input weights in ELM caused by random assignment in order to overcome the data sparsity effect in RS. The efficiency of proposed approaches against different clustering methods has been evaluated using two benchmark datasets and four metrics. The empirical results indicate that the proposed methods can generate better recommendation results compared to other traditional techniques. [ABSTRACT FROM AUTHOR]

The Consillium Academica initiative, spearheaded by the academic vicedeanship of the Faculty of Sciences at Universidad Nacional de Colombia in Bogotá, is based on a comprehensive clustering analysis of undergraduate students. This study leverages data spanning from the 2012-1S to 2022-2S academic terms to semi-automatically identify a group of students consistently exhibiting academic underperformance each semester with a potential high risk of academic dropout. The methodology employed in this initiative serves as a proactive measure to identify and support students at risk, to improve the effectiveness of the intervention strategies of the tutor-teacher program, facilitating direct contact between mentors and identified students to provide personalized guidance and academic advisement. This article presents the methodology, key findings, and implications of the Consillium Academica initiative, shedding light on its potential to fortify academic support systems and contribute to the overall success and retention of undergraduate students. [ABSTRACT FROM AUTHOR]