Adaptive Clustering Techniques for Software Components and Architecture

Wireless Sensor Networks are vital for applications like environmental monitoring, smart cities, and industrial Internet of Things, yet their efficiency is often limited by sensor node energy constraints. To address this, we propose a novel hybrid routing algorithm namely, Fuzzy C-Means - Fuzzy-Based Spotted Hyena Optimization - Quantum Annealing, which enhances energy efficiency and network longevity. The three-phase approach begins with adaptive fuzzy clustering, allowing nodes to belong to multiple clusters for improved load balancing. Next, the Fuzzy-Based Spotted Hyena Optimization algorithm selects initial routes using parameters such as residual energy, distance to the base station, and link quality. Finally, Quantum Annealing refines routing paths to ensure global optimization and avoid local optima. This hybrid method adapts dynamically to changing network conditions and energy levels, delivering superior scalability and performance. Experimental results show a 30% reduction in total energy consumption and a 25% increase in effective data transmission. The algorithm achieves a packet delivery ratio of 98.87%, throughput of 65 kbps, and latency of 115 ms, outperforming conventional techniques. This work contributes a robust and adaptive routing solution, supporting sustainable and efficient WSN deployments in dynamic environments.

This paper presents a self-optimal clustering (SOC) technique which is an advanced version of improved mountain clustering (IMC) technique. The proposed clustering technique is equipped with major changes and modifications in its previous versions of algorithm. SOC is compared with some of the widely used clustering techniques such as K-means, fuzzy C-means, Expectation and Maximization, and K-medoid. Also, the comparison of the proposed technique is shown with IMC and its last updated version. The quantitative and qualitative performances of all these well-known clustering techniques are presented and compared with the aid of case studies and examples on various benchmarked validation indices. SOC has been evaluated via cluster compactness within itself and separation with other clusters. The optimizing factor in the threshold function is computed via interpolation and found to be effective in forming better quality clusters as verified by visual assessment and various standard validation indices like the global silhouette index, partition index, separation index, and Dunn index.

Mining event logs for knowledge discovery based on adaptive efficient fuzzy Kohonen clustering network

Mapping adaptive fuzzy Kohonen clustering network onto distributed image processing system

A fuzzy self-tuning controller based on fuzzy clustering algorithmis proposed in the paper. First of all, the adaptive fuzzy clustering algorithm is presented to confirm the structure of fuzzy model. The Kalman filtering method is used to confirm parameters of fuzzy model. On the basis of the proposed method, the parameters of the controller can be automatically adjusted. To demonstrate the performance of the proposed method, simulation on the nonlinear system is performed. The results show that this method provides effective and accurate control.

The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k-NN algorithm, support vector machine, and hybrid data mining methods in accuracy.

Sales volume forecasting is becoming a crucial requirement for many sellers, enabling them to modify their advertising strategies and strike the ideal balance between sales volume and profit. A higher budget for promotion and a lower profit are the outcomes of overestimating the sales volume, which results in lower actual sales than predicted. Therefore, accurate sales volume forecasting is essential for sellers to choose the best methods of advertisement. The quantity of sales predicting approach in this case is one that predicts future sales based on historical data for the particular supplier, as well as the advertising techniques to be used on the relevant future days, sales volume from the past, dynamic influencing elements like past marketing strategies and weather data, and the static characteristics of the specific vendor are all examples of historical information. As a result, merchants use the expected forthcoming gross revenue under various stuff to choose the deal strategy to be employed. This work suggests a clustering-based forecasting methodology for predicting sales by fusing clustering and machine-learning techniques. The suggested method first divided the training data into groups using the clustering technique, which groups data with comparable features or patterns. The forecasting models for each group are then, using machine learning techniques, it was trained. Once, the cluster's trained forecasting model was chosen for sales forecasting. It was determined which cluster's data patterns matched the test data the most closely. Because sales data reflect similar data patterns or features over time, the presented clustering-based forecasting methodology can enhance forecast accuracy. Three machine learning techniques are used in this paper: Generalized Linear Regression, Decision Tree, and Gradient Boosted Tree, as well as two clustering techniques: Self Organizing Map (SOM) and K-mean clustering. A predictive model that is most suited for predicting the sales trend is presented based on a performance evaluation. Discussion of the data is done in terms of the precision and accuracy of good prediction systems. The best-fit model is chosen to be the Gradient Boosted Tree Algorithm and it has the highest level of forecasting and sales prediction accuracy. The sales forecast, or which product will be sold in the future and in what quantity, is carried out using Gradient Boosted Tree.

Any data in the real world can be organized properly by Clustering the data using some clustering techniques and in these clustering techniques Fuzzy C-Means (FCM) is a very recent and better technique that can mold the data with good logic and in a highly accurate manner. FCM is same as K-Means clustering technique but FCM is developed with some Fuzzy means. FCM is associated with some constraints as FCM is more responsive as on the order how the clusters were arranged in the beginning of applying the technique. Even though it has its own defects when dealing with large data, it cannot get the finest solution as the data need some optimization? So to get some better accuracy, In our proposed paper we are applying an enhanced FCM for clustering and for obtaining precise results we are involving GSA based on gravitation laws and perception of masses. This optimizing technique (GSA) can be used to improve the limitations in order to gain a well-defined system performance; GSA can efficiently deal with many propositions of large data. Here we are going to develop a Map-Reduce mechanism to effectively deal with large data. The expected result can be achieved by choosing the better finest candidates and grouping them in the reduction mode to get the finest solution. Evaluation of practical results will be optimizing when compared to other existing similar techniques.

The designs of solar energy systems depend mainly on the solar radiation that reaches the earth's surface, as it is difficult to determine the amount of solar radiation precisely due to several climatic, geographical and temporal factors. Therefore, forecasting of solar radiation is necessary before using solar energy systems. In this paper, the researchers present an accuracy MAMDANI fuzzy inference system for solar radiation prediction with meteorological data. This system is based with a two-stage method for Fuzzy Rules Learning through Clustering (FRLC). In the first stage, the subtractive clustering is used to extract the fuzzy rules, the second stage is a linguistic approximation and a refinement of the learned solutions with linguistic hedges. FRLC is compared to multilayer feed-forward neural network and support vector regression. The results of the experiments show the efficacy of linguistic fuzzy rules in the forecasting of solar radiation. In parallel with the prediction, the model provides a good balance between interpretability and accuracy.

Clustering by fast search and find of density peaks (CFSFDP) is proposed to cluster the data by finding of density peaks. CFSFDP is based on two assumptions that: a cluster center is a high dense data point as compared to its surrounding neighbors, and it lies at a large distance from other cluster centers. Based on these assumptions, CFSFDP supports a heuristic approach, known as decision graph to manually select cluster centers. Manual selection of cluster centers is a big limitation of CFSFDP in intelligent data analysis. In this paper, we proposed a fuzzy-CFSFDP method for adaptively selecting the cluster centers, effectively. It uses the fuzzy rules, based on aforementioned assumption for the selection of cluster centers. We performed a number of experiments on nine synthetic clustering datasets and compared the resulting clusters with the state-of-the-art methods. Clustering results and the comparisons of synthetic data validate the robustness and effectiveness of proposed fuzzy-CFSFDP method.

An improved detection of blind image forgery using hybrid deep belief network and adaptive fuzzy clustering

Regional flood frequency analysis by combining self-organizing feature map and fuzzy clustering

Anomaly detection is very important for the mining industry. If anomalies in mining equipment can be correctly detected to predict machine breakdown, mining companies will be able to reduce the cost of maintaining their machines. However, anomaly detection in this context is quite difficult considering the large volume of sensor data involved and unlabelled nature of the data. Clustering techniques have therefore been applied to analyse this problem, by dividing the data into normal and abnormal clusters. In this paper, we investigate the influence of using different numbers of clusters in clustering models, which include k-means, fuzzy c-means and the self-organising map, to obtain useful data patterns and classify the data into normal and abnormal types. Our aim here is to reduce the trigger of false alarm in the anomaly detection process. The data used in this study is based on real-world grease cycle data from a mining company in Australia. Our experimental results show that with more clusters, the number of anomalies detected tends to decrease for the clustering models considered. This means false alarms can be reduced by increasing the number of clusters used.

Use of a fuzzy granulation–degranulation criterion for assessing cluster validity

It is well known that the training of medical students is a long and arduous process. Students master many areas of knowledge in a relatively short amount of time in order to become experts in their chosen field. The Socratic Method used in the latter stages of medical education, where a physician directly monitors a group of students, is inherently restrictive due to the limited number of cases and length of the students’ rotations. Innovative Intelligent Tutoring techniques offer a solution to this problem. This paper outlines the overall structure and design of Shufti, an Intelligent Tutoring System (ITS) focused on mammography and medical imaging. Shufti's aim is to provide medical students with an improved learning environment, exposing them to a broad range of examples supported by customized feedback and hints driven by an adaptive Reinforcement Learning system and Clustering Techniques.