Journal of Intelligent & Fuzzy Systems - Volume 40, issue 4

Authors: Poonguzhali, S, | Chakravarthi, Rekha

Article Type: Research Article

Abstract: Diabetes is one of the chronic metabolic disorder. Under diabetic condition, blood glucose level should be properly maintained in order to avoid various major diseases. The condition will be worse when it is not controlled at an earlier stage. Even massive heart attack cannot be identified when the patient has been affected by diabetes. Early diagnosis is required for preventing fatal diseases like cardiac problem, asthma, heart attack etc. In the proposed system measurement of glucose level and Prediction/ diagnosis of diabetes is based on the real time low complexity neural network implemented on a wearable device. A larger network …is required for the diagnosis which needs to be present far-off in cloud and initiated for diagnosis and classification process of diabetes whenever it is essential. People can be able to manage and monitor the required basic parameters like heart rate, glucose level, lung condition, pressure of blood using the corresponding light weight biosensors in the wearable device designed through telemedicine technology. The quality of the disease diagnosis and Prediction is improved in this way. Using neural network feed forward prediction model in conjugation with back propagation algorithm and given training data, the system predicts whether the patient is prone to diabetes or not. The proposed work was evaluated using physic sensor data from physio net data base and also tested for real time functioning. The Proposed system found to be efficient in accuracy, sensitivity and fast operative. Show more

Keywords: Continuous glucose monitoring, diabetic condition prediction, early medical diagnosis, ANN, remote health assistance

DOI: 10.3233/JIFS-189477

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6365-6374, 2021

Authors: Ajitha, P. | Sivasangari, A. | Immanuel Rajkumar, R. | Poonguzhali, S.

Article Type: Research Article

Abstract: Text Sentiment Analysis is a system where text feeling polarity is positive or negative or neutral from a series of texts or documents or public opinions on a particular product or general subject. Using machine learning and natural language processing techniques, the current work aims to gain insight into sentiment mining on tweets. Text classification is accomplished using Machine Learning Algorithm-based fusion technique. This research suggested a system for grading feelings based on a lexicon. Bag-of-words (BOW) or lexicon-based methodology is currently the main standard way of modeling text for machine learning in sentiment analysis approaches. Marketers can use sentiment …analysis to analyze their business and services, public opinion, or to evaluate customer satisfaction. Organizations can even use this analysis to gather significant feedback on issues related to newly released products. The main objective of this is to resolve the data overload problem. Show more

Keywords: Sentiment analysis, natural language processing, lexicon method, naive bayesian algorithm

DOI: 10.3233/JIFS-189478

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6375-6383, 2021

Authors: Ma, Hongjiang | Luo, Xu

Article Type: Research Article

Abstract: The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its …supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective. Show more

Keywords: Neural network, improved algorithm, logistics demand, forecasting model

DOI: 10.3233/JIFS-189479

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6385-6395, 2021

Authors: Xie, Ying

Article Type: Research Article

Abstract: From the beginning to the end, monetary policy has focused too much on the control of the supply side. At present, the single supply-based monetary policy is ineffective. Therefore, it is urgent to change the current single direct supply-side regulation and control policy and replace it with a non-single and indirect control policy that combines supply and demand. Based on machine learning algorithms, this paper constructs a monetary policy analysis model based on dynamic stochastic general equilibrium methods to analyze the interactive effects of monetary policy and other policies. Moreover, this paper uses the dynamic stochastic general equilibrium model to …simulate and analyze the economic effects of fiscal policy. In addition, this paper compares the economic effects of monetary policy and other policies and conducts verification and analysis through actual data. The obtained results show that the model constructed in this paper achieves the expected effect. Show more

Keywords: Dynamic stochastic, general equilibrium model, monetary policy, policy effect

DOI: 10.3233/JIFS-189480

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6397-6408, 2021

Authors: Wang, Linuo

Article Type: Research Article

Abstract: Injuries and hidden dangers in training have a greater impact on athletes ’careers. In particular, the brain function that controls the motor function area has a greater impact on the athlete ’s competitive ability. Based on this, it is necessary to adopt scientific methods to recognize brain functions. In this paper, we study the structure of motor brain-computer and improve it based on traditional methods. Moreover, supported by machine learning and SVM technology, this study uses a DSP filter to convert the preprocessed EEG signal X into a time series, and adjusts the distance between the time series to classify …the data. In order to solve the inconsistency of DSP algorithms, a multi-layer joint learning framework based on logistic regression model is proposed, and a brain-machine interface system of sports based on machine learning and SVM is constructed. In addition, this study designed a control experiment to improve the performance of the method proposed by this study. The research results show that the method in this paper has a certain practical effect and can be applied to sports. Show more

Keywords: Machine learning, SVM, sports, brain-computer interface

DOI: 10.3233/JIFS-189481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6409-6420, 2021

Authors: Hongjin, Sun

Article Type: Research Article

Abstract: The financial supply chain is affected by many factors, so an artificial intelligence model is needed to identify supply chain risk factors. This article combines the actual situation of the financial supply chain, improves the traditional machine learning algorithm, and takes the actual company as an example to build a corresponding risk factor recognition model. From the perspective of optimizing the supply chain financial model, this paper combines the functions of the Internet of Things technology and the characteristics of the supply chain financial inventory pledge financing model to design a new type of inventory pledge financing model. The new …model makes up for the defects of the original model through the functions of intelligent identification, visual tracking and cloud computing big data processing of the Internet of Things technology. In addition, this study verifies the performance of the system, uses a large amount of data in Internet finance as an object, and obtains the corresponding results through mathematical statistical analysis. The research results show that the model proposed in this paper has a certain effect on the identification and analysis of financial supply chain risk factors. Show more

Keywords: Machine learning, finance risk, supply chain, risk factors

DOI: 10.3233/JIFS-189482

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6421-6431, 2021

Authors: Li, Zhou

Article Type: Research Article

Abstract: The accurate mastery of demand information enables retailers to better respond to consumers and effectively manage inventory. However, the precise connection and interaction between this information collection and inventory management is more difficult to measure. In view of this, this paper proposes a research on inventory model based on consumer web search. Moreover, centering on the two main actors in the online search environment, consumers and retailers, this paper fully considers their characteristics and situations to construct an inventory model in the online search environment, and analyzes the ordering strategy. Moreover, based on the digital traces left by consumers in …the decision-making process, this paper uses general search indicators and specific search indicators to measure consumer web search to explore the relationship between consumer web search indicators and the demand conversion rate proposed in the model. Moreover, this paper analyzes the model with examples. The research results are in line with model construction expectations. Show more

Keywords: Machine learning, consumer, behavior analysis, improvement model

DOI: 10.3233/JIFS-189483

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6433-6443, 2021

Authors: Yan, Tang | Pengfei, Li

Article Type: Research Article

Abstract: In marketing, problems such as the increase in customer data, the increase in the difficulty of data extraction and access, the lack of reliability and accuracy of data analysis, the slow efficiency of data processing, and the inability to effectively transform massive amounts of data into valuable information have become increasingly prominent. In order to study the effect of customer response, based on machine learning algorithms, this paper constructs a marketing customer response scoring model based on machine learning data analysis. In the context of supplier customer relationship management, this article analyzes the supplier’s precision marketing status and existing problems …and uses its own development and management characteristics to improve marketing strategies. Moreover, this article uses a combination of database and statistical modeling and analysis to try to establish a customer response scoring model suitable for supplier precision marketing. In addition, this article conducts research and analysis with examples. From the research results, it can be seen that the performance of the model constructed in this article is good. Show more

Keywords: Machine learning, data analysis, marketing, customer response, scoring model

DOI: 10.3233/JIFS-189484

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6445-6455, 2021

Authors: Han, Ying

Article Type: Research Article

Abstract: When choosing stock investment, there are many stock companies, and the stock varieties are also complicated. At present, there are various systems for evaluating stock performance in the market, but there is no uniform standard, so investors often cannot effectively invest in stocks. Simultaneously, stock management companies also have their own characteristics, and there are differences in shareholding structure and internal management structure. Based on this, based on multiple regression models and artificial intelligence models, this paper constructs a stock return influencing factor analysis model to statistically describe the sample data and factor data, and tests the applicability of the …five-factor model for performance evaluation of mixed stocks. In addition, this article combines the actual situation to carry out data simulation analysis and uses a five-factor analysis model to carry out quantitative research on stock returns. Through data simulation analysis, we can see that the model constructed in this paper has a certain effect in the analysis of factors affecting stock returns. Show more

Keywords: Multiple regression, artificial intelligence, stock returns, influencing factors

DOI: 10.3233/JIFS-189485

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6457-6467, 2021

Authors: Yaxu, Yang

Article Type: Research Article

Abstract: The loose logistics market, the weak value-added service capabilities of enterprises, and the backward construction and operation of logistics networks have led to high logistics costs and low efficiency in some enterprises. In order to improve the comprehensive evaluation effect of enterprise logistics enterprise competitiveness, this paper builds a comprehensive evaluation model of logistics enterprise competitiveness based on SEM model based on machine learning technology. Moreover, in order to more accurately grasp the law of customer logistics mode selection behavior, this paper adds the adaptive value of the latent variables of the logistics mode service characteristics obtained through the SEM …model to the utility function of the logistics mode to obtain the SEM-NL integrated model. In addition, starting from the analysis of the key factors affecting the competitiveness of enterprise logistics, this paper constructs an evaluation model of enterprise logistics competitiveness, and analyzes and studies the comprehensive competitiveness of enterprise logistics through two aspects of logistics actual competitiveness and logistics future development potential. The research results show that the model constructed in this paper is suitable for the comprehensive evaluation of the competitiveness of logistics enterprises. Show more

Keywords: SEM model, logistics enterprise, competitiveness, comprehensive evaluation

DOI: 10.3233/JIFS-189486

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6469-6479, 2021