FCM Model Research Articles

Research on the influencing factors and countermeasures of the development of prefabricated buildings under the background of "double carbon"

As a representative of green and low-carbon buildings, prefabricated buildings have gained widespread attention from the government and industry due to their significant advantages in energy conservation and emission reduction, time saving, labor saving, and high construction efficiency. However, currently, prefabricated buildings have a low market share in China and their development is slow. This article summarizes the influencing factors of the development of prefabricated buildings and constructs a causal model (FCM model) consisting of five conceptual nodes for inference and simulation analysis. The predictive analysis results show that the correlation strength between the four major influencing factors and the development of prefabricated buildings is: F2(cost environment）> F4（policy system）> F3（technology system）> F1(market foundation). This article proposes reasonable measures and suggestions based on the analysis results, providing reference for promoting the development of prefabricated buildings.

A Study of Problems Faced by Engineering Students in Mathematical Subjects using Fuzzy Cognitive Mappings

Introduction: One of the main concerns and issues for educators, particularly for engineering students, has been the caliber of mathematics instruction and learning. There are many different concepts and skills that make up mathematics. The improvement of problem-solving skills is one of mathematics' key functions. Numerous colleges struggle with the issue of students quitting because of mathematics because it is frequently thought of as a topic that students find difficult to master. Objectives: The study's goal is to examine the reasons why engineering students struggle with math. In this research, using fuzzy cognitive map techniques (FCM), we analyze the challenges engineering students encounter when trying to earn grades in math-related courses. FCM is a blend of some fuzzy logic and neural network ideas. FCM is made up of collections of concepts and all the different causal relationships that connect them. Methods: We created a model of the issues and causal relationships that engineering students faced when trying to succeed in mathematics classes. Both statically and dynamically, utilizing concepts from graph theory and simulations, the model is examined. Also we applied DEMATEL one of the Multi Criteria Decision Making method. Results: The analysis reveals that the main issue is not just the students' lack of mathematical background knowledge, but also their attitude toward learning, psychological response to the first failure, and lack of effort to complete additional tasks or attend tutorials. Conclusions: Using the FCM method, a model was created for the case of problems faced by engineering college students in scoring marks in mathematical related subjects based on the opinion of a domain expert. The model was first examined statically. The model was then simulated in a dynamic and it was predicted that there should be an attitude change in teachers and as well as students. Through the above static and dynamic studies of the FCM model was identified as important and useful decision support system, since it capable to provide support to decision makers, by making predictions on various scenarios that are imposed on the FCM model.

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier.

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study's commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.

Multimodal network path optimization based on a two‐stage algorithm in the perspective of sustainable transport development

AbstractThe environmental issues brought on by carbon emissions from transport have risen to prominence in recent years. More and more academics are using the multi‐objective path optimization method to solve the multimodal optimization problem from the standpoint of sustainable development in order to address the environmental issues brought on by the transport process. The research proposes a two‐stage method to handle multi‐objective optimization convergence and simplify multimodal transport path optimization. In the first stage, a fuzzy C clustering model is established, and based on the clustering results, the multimodal transport network nodes are identified. In the second stage, a multimodal transport multi‐objective path optimization model is established, and the optimal path is solved using a genetic algorithm. The research method was applied in the Bohai Rim region. Results indicated that the fuzzy C‐clustering method and the genetic method were able to select the optimal node city, thus solving the actual site selection problem of multimodal transportation networks. Using the FCM model, the 86 city nodes were categorized into four types, leading to the establishment of the most proficient multimodal transportation network in the Bohai Rim region. Using a genetic algorithm for optimization, a stable state is reached after 25 iterations. In the validation experiment on path optimization, the cost was reduced by 47.12% compared to the minimum single objective time, and transportation carbon emissions saw a reduction of 28.23%. Similarly, compared to the lowest target for transportation carbon emissions, the cost was reduced by 39.48% and the time was reduced by 38.12%. Compared to the lowest target for transportation carbon emissions, the time was reduced by 32.02% and the carbon emissions were reduced by 19.23%. Notably, the transportation multi‐objective path optimization model showed significant improvement compared to the single‐target model. The research method has been proven to be superior, and can offer the most optimal transportation route guidance for participants in multimodal transportation. Furthermore, it can effectively tackle the issue of node selection convergence and multi‐objective optimization, while also serving as a valuable source of data to support the theoretical advancement of multimodal transportation network path optimization.

Improved Fuzzy Based Segmentation with Hybrid Classification for Skin Disease Detection

In this paper, we present a novel approach for the early detection of skin disorders, aiming to improve the prediction accuracy and precision. Our motivation lies in addressing the challenges posed by the uncertainty in skin lesion features. To achieve this, we employ fuzzy-based segmentation techniques to enhance the quality of skin disorder image processing. Additionally, we explore the integration of intensity data to further refine the segmented images. Our methodology consists of three main steps: segmentation, feature extraction, and classification. We begin by inputting the image into the segmentation phase, followed by the application of a fuzzy c-means clustering process to improve the segmentation output. From the resulting segmented image, we extract features using local binary pattern and local Gabor XOR pattern techniques. These features are then subjected to classification, employing a hybrid classifier that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The use of this hybrid classifier is particularly significant, as it leverages the strengths of both CNN and LSTM to enhance the predictive power of our model. Through rigorous experimentation, we have achieved promising results. Our proposed method exhibits an impressive accuracy of 94.6% and precision of 95.5% in comparison to the Conventional FCM Model, which only achieves an accuracy of 87% and precision of 82.5%.

Exploring the Role of Blockchain Technology in Improving Sustainable Supply Chain Performance: A System-Analysis-Based Approach

Due to the complexity of SSC practices, BT can be adopted as an innovative tool for addressing socioenvironmental issues. However, BT adoption has not been receiving growing attention because of organizational challenges (e.g., financial constraints). This study develops a system-analysis-based approach to investigate the impact of BT adoption on the improvement of SSC performance. This approach is proposed based on the FCM to model CRs between SSC-performance-related targets and enablers of BT adoption. These enablers include inherent features of BT identified in the context of sustainability (e.g., social responsibility and environmental sustainability). After developing an FCM model for the BT adoption problem, the impact of enablers on target concepts is investigated by implementing a hybrid FCM learning algorithm according to the extracted CRs. Based on the outputs of the FCM learning algorithm, this study also identifies the most effective enablers for improving SSC performance using the combined compromise solution method. The proposed system-analysis-based approach demonstrates that BT can significantly affect various dimensions of SSC networks’ performance. The results imply that the appropriate BT adoption supports SSC performance by improving environmental sustainability, creating smart contracts, and increasing traceability.

Viability of two adaptive fuzzy systems based on fuzzy c means and subtractive clustering methods for modeling Cadmium in groundwater resources

The Adaptive Neuro-Fuzzy Inference System (ANFIS) combines the strengths of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) into a single framework. By doing so, it allows for quicker learning and adaptable interpretation capabilities, which are useful for modeling complex patterns and identifying nonlinear relationships. One significant challenge in assessing water quality is the difficulty and time-consuming nature of determining the various factors that impact it. Given this situation, predicting heavy metal levels in groundwater resources, both urban and rural, is essential. This paper investigates two methods, ANFIS-FCM and ANFIS-SUB, to determine their effectiveness in modeling Cadmium (Cd) in groundwater resources. ‏The parameters to be considered are: dissolved solids (TDS), electroconductivity (EC), turbidity (TU), and pH were assumed to be the independent variables. A total of 51 sampling location were used with in the groundwater resource were used to develop the fuzzy models. For evaluating the performance of ANFIS-FCM and ANFIS-SUB models, three different performance criteria including the correlation coefficient, root mean square error, and sum square error were used for comparing the model outputs with actual outputs‏.‏ Based on the obtained results from scatter plots of actual and predicted value by ANFIS-SUB and ANFIS- FCM models, the determination coefficient (R2) value for total data, test and train sets is equal to 0.978, 0.982, 0.993 and to 0.983, 0.999 and 0.998 respectively. This result proved the Cd predictions of the implemented ANFIS-FCM model was significantly close to the measured all experimental data with R2 of 0.983. The performance of the implemented ANFIS-FCM model was compared with the ANFIS-SUB model and it is found that the ANFIS-FCM provided slightly higher accuracy than the ANFIS-SUB model. Also, the results obtained from the comparison between the predicted and the actual data indicated that the ANFIS-FCM and ANFIS-SUB have a strong potential in estimating the heavy metals in the groundwater with a high degree of accuracy.

A FCM Runoff Model for Small Rivers with Rainfall Recharge. 1. The Concept and Algorithms

The flood cycle model FCM is a lumped conceptual water-balance model designed to simulate rain runoff at a scale of a small river basin. The development of the FCM model includes the description of the dynamics of the major components of the total moisture reserves of a basin and reproduces the effect of spatial expansion of the drainage network through temporary surface and subsurface streams during extraordinary floods. The accepted conceptual assumptions of the model, which are in agreement with the rational hydrological considerations, lead to three runoff-formation regimes, referred to as intravolume, surface and breakthrough. The concept and algorithms of FCM are given in detail.

A FCM Runoff Model for Small Rivers with Rainfall Recharge. 2. Parameterization and Verification

The specific features of the parameterization and verification of FCM model were analyzed, and the data published before, as well as new results of the authors’ field observations, are systematized. The parameterization algorithms and the results of model testing in different regions with the predominance of rain floods in river regime are described. The basis and empirical confirmation of some principal hypotheses of FCM are discussed and the so-called effect of basin buffer storage is considered. This most vivid manifestation of the nonlinearity of the extreme runoff formation is, though a rare, but principally important phenomenon in the formation of extraordinary rain floods.

Enhanced adaptive neuro-fuzzy inference system using genetic algorithm: a case study in predicting electricity consumption

Energy forecasting is crucial for efficient energy management and planning for future energy needs. Previous studies have employed hybrid modeling techniques, but insufficient attention has been given to hyper-parameter tuning and parameter selection. In this study, we present a hybrid model, which combines fuzzy c-means clustered adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA), named GA–ANFIS–FCM, to model electricity consumption in Lagos districts, Nigeria. The model is simulated using the algorithms’ control settings, and the best model is identified after assessing their performance using renowned statistical indicators. To further narrow down the best viable model, the impact of the core parameter of the GA on the GA–ANFIS–FCM optimal model is examined by varying the crossover percentage in the range of 0.2–0.6. Firstly, the results reveal the better performance of the hybridized ANFIS model than the standalone ANFIS model. Additionally, the best model is obtained with the GA–ANFIS–FCM model with four clusters at a crossover percentage of 0.4, with mean absolute percentage error (MAPE), mean absolute error (MAE), coefficient of root mean square error (CVRMSE), root mean square error (RMSE) values of 7.6345 (signifying a forecast accuracy of 92.4%), 706.0547, 9.4913, and 918.6518 during the testing phase, respectively. The study demonstrates the potential of the proposed model as a reliable tool for energy forecasting.

Sustainable Systems Engineering Using Life Cycle Assessment: Application of Artificial Intelligence for Predicting Agro-Environmental Footprint

The increase in population has increased the need for agricultural and food products, and thus agricultural production should be increased. This goal may cause increases in emissions and environmental impacts by increasing the consumption of agricultural inputs. The prediction of environmental impacts plays an important role in evaluating pollutant emissions in crop production. This study employed two artificial intelligence (AI) methods: the adaptive neuro-fuzzy inference system–fuzzy c-means (ANFIS–FCM) algorithm as a novel computational method, and an artificial neural network (ANN) as a conventional computational method to predict the environmental impacts of soybean production in different scenarios (i.e., soybean cultivation after rapeseed (R-S), wheat (W-S), and fallow (F-S)). The life cycle of soybean production was assessed in terms of environmental impacts through the IMPACT2002+ method in SimaPro. In the present study, the production of one ton of soybeans was considered the functional unit, and the boundary of the system was considered the gate of the field. According to the results, the production of each ton of soybean in the defined scenarios resulted in 0.0009 to 0.0016 DALY, 5476.18 to 8799.80 MJ primary, 1033.68 to 1840.70 PDF × m2 × yr, and 563.55 to 880.61 kg CO2-eq damage to human health, resources, ecosystem quality, and climate change, respectively. Moreover, the weighted analysis indicated that various soybean production scenarios led to 293.87–503.73 mPt damage to the environment, in which the R-S scenario had the best environmental performance. According to the results, the ANFIS–FCM algorithm acted as the best prediction model of environmental indicators for soybean cultivation in all cases related to the ANN. The range of calculated R2 for the ANFIS-FCM and ANN models were between 0.9967 to 0.9989 and 0.9269 to 0.9870, respectively. It can be concluded that the proposed ANFIS–FCM model is an efficient technique for obtaining accurate environmental prediction parameters of soybean cultivation.

On the transition to dripping of an inverted liquid film

The transition to dripping in the gravity-driven flow of a liquid film under an inclined plate is investigated at zero Reynolds number. Computations are carried out on a periodic domain assuming either a fixed fluid volume or a fixed flow rate for a hierarchy of models: two lubrication models with either linearised curvature or full curvature (the LCM and FCM, respectively), and the full equations of Stokes flow. Of particular interest is the breakdown of travelling-wave solutions as the plate inclination angle is increased. For any fixed volume, the LCM reaches the horizontal state where it attains a cosine-shaped profile. For sufficiently small volume, the FCM and Stokes solutions attain a weak Young–Laplace equilibrium profile, the approach to which is described by an asymptotic analysis generalising that of Kalliadasis & Chang (J. Fluid Mech., vol. 261, 1994, pp. 135–168) for the LCM. For large volumes, the bifurcation curves for the FCM and Stokes model have a turning point so that the fully inverted state is never reached. For fixed flow rate, the LCM blows up at a critical angle that is well predicted by asymptotic analysis. The bifurcation curve for the FCM either has a turning point or else reaches a point at which the surface profile has an infinite slope singularity, indicating the onset of multi-valuedness. The latter is confirmed by the Stokes model, which can be continued to obtain overturning surface profiles. Overall, the thin-film models either provide an accurate prediction for dripping onset or else supply an upper bound on the critical inclination angle.

An individual tree-based model for estimating regional and temporal carbon storage of Abies chensiensis forest ecosystem in the Qinling Mountains, China

Accurate estimation of carbon storage of forest ecosystem has significant implication for mitigating global warming. In order to get accurate estimation of carbon storage of forest ecosystem at the regional scale and temporal dynamics in the Qinling Mountains, this study established a dynamic estimation model (FCM model) for forest carbon storage at tree stand scale according to the data of forest growth and degradation characteristics of Aibes chensiensis, and constructed a regional and temporal estimation model (geo-FCM model) based on FCM model and Aibes chensiensis spatial distribution data from 3S technology. The results showed that carbon density of Aibes chensiensis forest at tree age of 80 years from FCM model were 245.56 t ha−1, which was similar to the measured carbon densities from field investigation. The simulation process of the FCM model could well reflect the growth characteristic of Aibes chensiensis. The simulated result of geo-FCM model showed carbon storage of Abies chensiensis forest in study region were 5.23 × 105 t in year 2018 with the average carbon density of 222.26 t ha−1 and increased to 5.60 × 105 t in year 2028 along with tree growth. Its carbon storage increased 7.11% compared to the state of year 2018. The constructed model provided a robust method to estimate forest carbon storage of different tree species at different age stages at the regional scale, which can provide an important reference for forest management and selection of plantation tree species.

Modeling of Fuzzy Cognitive Maps with a Metaheuristics-Based Rainfall Prediction System

Rainfall prediction remains a hot research topic in smart city environments. Precise rainfall prediction in smart cities becomes essential for planning security measures before construction and transportation activities, flight operations, water reservoir systems, and agricultural tasks. Precise rainfall forecasting now becomes more complex than before because of extreme climatic changes. Machine learning (ML) approaches can forecast rainfall by deriving hidden patterns from historic meteorological datasets. Selecting a suitable classification method for forecasting has become a tough job. This article introduces the Fuzzy Cognitive Maps with a Metaheuristics-based Rainfall Prediction System (FCMM-RPS) technique. The intention of the FCMM-RPS technique is to predict rainfall automatically and efficiently. To accomplish this, the presented FCMM-RPS technique primarily pre-processes the rainfall data to make it compatible. In addition, the presented FCMM-RPS technique predicts rainfall using the FCM model. To enhance the rainfall prediction outcomes of the FCM model, the parameter optimization process is performed using a modified butterfly optimization algorithm (MBOA). The performance assessment of the FCMM-RPS technique is tested on a rainfall dataset. A widespread comparison study highlights the improvements of the FCMM-RPS technique in the rainfall forecasting process compared to existing techniques with a maximum accuracy of 94.22%.

Fuzzy Data Mining and Bioinformatics Analysis in Methylation Analysis of M6A Gene Promoter Region in Esophageal Cancer

This work was aimed at analyzing the correlation between the methylation level of the M6A gene in esophageal cancer (EC) and the prognosis of patients based on bioinformatics technology and evaluating the prognostic predictive values of different data mining models. 80 EC patients and 80 healthy people were selected, and the serum of the patients was collected to detect the level of DNA methyltransferase. During the radical resection of EC, tumor tissues and adjacent normal tissues were collected from patients to detect the methylation level of the M6A gene. COX regression analysis was employed to analyze the independent risk factors (IRFs) of M6A gene methylation and other treatments affecting the prognosis of EC patients. The particle swarm optimization (PSO) algorithm was introduced to improve the fuzzy C -means clustering (FCM) algorithm. The differences in the prognostic prediction efficiency of logistic regression analysis (LRA), decision tree (DT) C5.0, artificial neural network (ANN), support vector machine (SVM), and improved FCM (IFCM) models were compared. The levels of DNA methyltransferase and human histone deacetylase 1 (HSD-1) in EC patients were increased greatly ( P < 0.05 ). The methylation rates and methylation levels of M6A methylation regulators (ALKBH5, HNRNPC, METTL3, WTAP, RBM15, YTHDC1, YTHDF1, and FTO) in EC tissues were obviously higher ( P < 0.05 ). The survival time of high-risk EC patients was much shorter than that of low-risk patients ( P < 0.05 ). Univariate and multivariate COX regression analysis showed that gender, tumor grade, TNM grade, degree of infiltration, and methylation of ALKBH5, HNRNPC, and METTL3 genes were IRFs for the prognosis of EC patients ( P < 0.05 ). The areas under the ROC curve (AUCs) of LRA, DT C5.0, ANN, SVM, and IFCM algorithms for predicting the prognosis of patients were 0.813, 0.857, 0.895, 0.926, and 0.958, respectively, and the IFCM model had the best diagnostic effect. In conclusion, the detection of bioinformatics technology showed no obvious DNA methylation in EC patients, and the elevated levels of M6A methylation regulators in patients were an IRF affecting the prognosis of patients. In addition, the fuzzy data mining model can be undertaken as the preferred method for prognosis prediction of EC patients.

Gait Recognition Based on the Plantar Pressure Data of Ice and Snow Athletes.

The study of plantar pressure has become a research consensus in the field of biomechanics. The purpose of this paper is to study some lower limb movements in the daily activities of ice and snow athletes to obtain relevant data so as to carry out gait recognition analysis research. This paper selects the average foot pressure, forefoot foot pressure, front and rear foot pressure, foot pressure, toe pressure, 2–5 toe pressure, standing with eyes closed, x- and y-axes speed, foot length, foot width, and other actions of ice and snow athletes. Therefore, correlation analysis, work analysis, and curve fitting analysis were carried out on the joint motion in a single gait cycle. The collection and application of foot pressure and foot posture information are also analyzed. According to the plantar structure, the sole is divided into four parts. The maximum pressure point and coordinates of each part, the pressure center point, the ratio of the width and height of the sole of the foot, and so on are extracted as the haptic features of the gait. The experimental data shows that it can be seen that if the plantar area is divided in advance and the weight of each area is marked, whether standing, walking, or standing with one leg closed eyes can achieve better recognition results, and the accuracy rate is all more than 90 percent. The average recognition accuracy rate using the method of dividing four regions is only about 80%, and the accuracy rate of recognition using the method of dividing eight regions is 82%. It can be seen that the features extracted by the FCM model proposed in this paper contain more information of the plantar pressure image, and the accuracy rate is higher in the classification and recognition.

Factor analysis and countermeasure simulation on the socio-ecological environment risks during the development of coalbed methane: based on the DEMATEL and FCM models

The socio-ecological environmental problem caused by the development of coalbed methane (CBM) is becoming more serious, so it is very significant for accelerating the development of CBM industrialization to identify the main influential factors of socioecological environmental risks and take effective risk prevention measures. Based on the triangular fuzzy number method, this paper firstly establishes a comprehensive correlation matrix. Then, the affected factors of socio-ecological environmental risks are analyzed by the DEMATEL method during the development of CBM. Then according to the aspects of centrality and causality, the key indicators are determined. Furthermore, the scenario simulation based on the FCM, the steady-state value and the comprehensive importance of key indicators, and the core indicators are selected. Finally, the scenario simulation of risk management countermeasure is done. The results show that: ① The index weights are calculated by the triangular fuzzy numbers, and the correlation matrix in DEMATEL is obtained by the weight ratio, which reduces the shortcomings and the subjectivity of traditional DEMATEL method. ② The correlation matrix in FCM is established based on the comprehensive correlation matrix in the above improved DEMATEL, which not only makes the FCM model more comprehensive and reduces the difficulty that the FCM correlation matrix is obtained considering the indirect influence relationship between indicators, but also makes up for the disadvantages that DEMATEL excludes during the selection of the core indexes, which are the considerations of the dynamic change of the importance of the indicators. ③ The rate of forestry and grass coverage, the ratio of investment in prevention and construction, the pollution index of soil Merlot, the reliability of machinery and equipment, the standard evaluation index of major pollution factors of underground water, and the ability of personal risk prevention are the key indicators, in which the rate of forestry and grass coverage, the ratio of investment in prevention and construction, and the reliability of machinery and equipment are the core indicators and are extremely important for the management of socio-ecological environmental risks during the development of coalbed methane. ④ The results of the scenario simulation of risk management countermeasure show that the core variables affect the socio-ecological environmental risks through such indicators as the degree of production mechanization, the degree of demolition and resettlement, and the ratio of economic loss by the influenced mechanism analysis; the ratio of investment in prevention and construction has the greatest impact on the socio-ecological environment risks in the early stage of CMB development, while the rate of forestry and grass coverage is the most important in the middle and late stages by the influenced result analysis. Therefore, this paper puts forward some corresponding stage suggestions on the prevention and control of socio-ecological environmental risks during the CMB development. In the early stage of CMB development, the ratio of investment in prevention and construction should be increased. In the middle stage, it is very important to reduce the damage of vegetation regions and to improve the reliability of machinery and equipment; in the late stage, the investment in prevention and construction should be increased steadily and the recovery of damaged vegetation areas should be focused on.

Brain tumour segmentation from magnetic resonance images using improved FCM and active contour model

Brain tumour segmentation from magnetic resonance images using improved FCM and active contour model

Brain tumour segmentation from magnetic resonance images using improved FCM and active contour model

Brain tumour segmentation from magnetic resonance images using improved FCM and active contour model

Stock Price Prediction Methods based on FCM and DNN Algorithms

With the rapid economic development and the continuous expansion of investment scale, the stock market has produced increasing amounts of transaction data and market public opinion information, making it further difficult for investors to distinguish effective investment information. With the continuous enrichment of artificial intelligence achievements, the status and influence of artificial intelligence researchers in academia and society have been greatly improved. Expert system, as an important part of artificial intelligence, has made breakthrough progress at this stage. Expert system is based on a large amount of professional knowledge and experience for a specific field. Computers of this system can be used to simulate the decision-making process of experts to provide a decision-making basis for solving some complex problems. This research mainly discusses stock price prediction methods on the basis of artificial intelligence (AI) algorithms. Fuzzy clustering is a data mining tool that has been developed in recent years and is widely used. Using this method to process super large-scale databases with various data attributes has the characteristics of high efficiency and small amount of information loss. Theoretically speaking, the use of fuzzy clustering technology and related index method can effectively reduce the massive financial fundamentals of listed companies. By analyzing the influencing factors of stock value investment, we specifically select from the financial statements of listed companies the five aspects that can reflect their profitability, development ability, shareholder profitability, solvency, and operating ability. The full text runs through a variety of AI methods that is the characteristic of the research method used in this article, which pays special attention to verifying the theoretical method model. Doing so ensures its effectiveness in practical applications. In stock value portfolio research, a portfolio optimization model, which integrates the dual objectives of portfolio risk and returns into the risk-adjusted return of capital single objective constraints and solves the portfolio, is established. The accuracy and recall of the FCM model are relatively stable, with accuracies of 0.884 and 0.001, respectively. This research can help improve the number and quality of listed companies.