Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Gao, Yu | Pan, Yinsong | Huang, Hong | Mohamed, Ehab R. | Aly, Zahraa M.I.

Article Type: Research Article

Abstract: Hyperspectral remote sensing combines spectrum, ground space and images organically to provide humans with unprecedented rich information. However, a prominent problem faced in the extraction and identification of hyperspectral remote sensing information is mixed pixels, and the method to solve mixed pixels is mixed pixel decomposition. The purpose of this paper is to study the swarm intelligence algorithm of spatial-spectral feature extraction and mixed pixel decomposition of hyperspectral remote sensing images. This paper first introduces two different methods for extracting spatial spectrum features, then studies linear and non-linear spectral hybrid models, and then studies end element extraction methods based on …quantum particle swarm optimization. The degree inversion method, the experimental part is based on the accuracy of the quantum particle swarm optimization-based end-element extraction method and two spatial-spectrum feature extraction methods. The experimental results show that the algorithm proposed in this paper improves the effect of group pixel decomposition based on the swarm intelligence algorithm. The classification accuracy of the 3DLBP spatial spectrum feature proposed in this paper is 94.22%. Show more

Keywords: Hyperspectral remote sensing image, spatial spectral feature extraction, mixed pixel decomposition, swarm intelligence algorithm, abundance inversion

DOI: 10.3233/JIFS-179990

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2020

Authors: Gong, Zhanxue | Li, Xiyuan | Ismail, Mahmoud

Article Type: Research Article

Abstract: The current automatic attendance system does not have automatic recognition effect. Especially in face recognition capture, the recognition effect is poor. In order to improve the automatic recognition efficiency of the attendance system, this study applies image processing technology to the recognition of attendance to study the automatic attendance system that can realize face recognition for working people. At the same time, this study aims at face recognition for mobile people, adopts migration feature algorithm for face recognition, and combines the actual needs of artificial intelligence attendance to construct an attendance system suitable for automatic face capture. In addition, in …order to study the performance of the algorithm, this paper designs a comparison test to compare the recognition rate parameters. It can be seen from the research that the algorithm proposed in this study has a high recognition rate and can be applied to practice and can provide theoretical reference for subsequent related research. Show more

Keywords: Image processing, feature recognition, image recognition, human resource management, attendance

DOI: 10.3233/JIFS-179971

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2020

Authors: Li, Meifang | Ruan, Binlin | Yuan, Caixing | Song, Zhishuang | Dai, Chongchong | Fu, Binghua | Qiu, Jianxing

Article Type: Research Article

Abstract: The early hidden characteristics of breast tumors make their features difficult to be effectively identified. In order to improve the detection accuracy of breast tumors, this study combined with computer-aided diagnosis techniques such as machine learning and computer vision and used X-ray analysis to study breast tumor diagnosis techniques. Moreover, this study combines breast tumor diagnostic images to determine various parameters of the image. At the same time, through experimental research and analysis of the region segmentation method and preprocessing method of breast detection images, the best diagnostic images are obtained, and the influence of background and other noise on …the image diagnosis results is effectively proposed. In addition, this study proposes a method for detecting the distortion of the mammogram image structure, which accurately detects the structural distortion and reduces the interference of various influencing factors. Finally, this paper designs experiments to study the effects of the diagnostic method of this paper. Through comparative analysis, it can be seen that the results of this study have certain advantages in accuracy and image clarity, and have certain clinical significance, and can provide theoretical reference for subsequent related research. Show more

Keywords: X-ray analysis, breast neoplasms, diagnosis, image, machine learning

DOI: 10.3233/JIFS-179967

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2020

Authors: Wang, Lei | Sun, Jinhai | Li, Tuojian

Article Type: Research Article

Abstract: Feature extraction is the basis of texture analysis. How to obtain texture features with small feature dimension, simple calculation and comprehensive representation of images is a hot spot and a difficult point in feature extraction. The traditional image texture feature extraction method is to process the image in the spatial domain. However, due to its high computational complexity, its practical application is restricted. Based on this, this study studies the extraction method of texture features, and deeply analyzes the principle of non-subsampled Contourlet transform. Moreover, this study uses NSCT to transform the image from the spatial domain to the frequency …domain and extracts the texture features of the decomposed low frequency sub-band, intermediate frequency sub-band and high frequency sub-band image respectively. In addition, this study selects the appropriate parameters to establish the support vector machine model and applies the extracted texture features into the support vector machine for recognition and applies it to the sports feature recognition. Finally, this study designed a controlled experiment to analyze the performance of the algorithm. The results show that the proposed method has certain effects. Show more

Keywords: Support vector machine, sports feature, feature recognition, improved algorithm

DOI: 10.3233/JIFS-179970

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2020

Authors: He, Han | Yi, Si | Liu, Weiwei

Article Type: Research Article

Abstract: It is of great research value and practical significance to use new technology to improve the accuracy of English speech recognition and apply the system to mobile platforms for users to use. The main content of this paper is the long-term and short-term memory, and the current decoding part is applied to the Android platform, and the performance of the program is analyzed. Neural networks converge slowly, making learning long-term memory difficult. In the experiment, the BPTT algorithm is used to analyze the problem of error elimination in traditional recursive networks. Combining BPTT algorithm in LSTM network to solve the …problem of traditional error elimination and improve speech recognition rate. In addition, this paper uses a new LSTM recurrent neural network to study the implementation of LSTM network on Android platform. Finally, this paper designs a comparative experiment to analyze the efficiency of oral English recognition. The results show that the research algorithm of this paper has certain effects. Show more

Keywords: Long short-term memory (LSTM), backpropagation through time (BPTT), financial spoken English, intelligent learning

DOI: 10.3233/JIFS-179969

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2020

Authors: Baojian, Wei | Yanmei, Lang | Chunyu, Li

Article Type: Research Article

Abstract: Most of the current plans for Alzheimer’s interventions to improve nursing interventions for patients are designed by clinical nurses themselves, which lack a theoretical basis and are not professional enough. Moreover, cognitive training only addresses a single aspect of rehabilitation for patients with cognitive dysfunction, so it lacks integrity. This study combines MRI and image recognition segmentation technology, adopts multi-party combined interventions for nursing rehabilitation, and uses image recognition technology to conduct experimental research. In addition, this study uses a team of doctors, nurses, and rehabilitators to form a team therapy model, which actively echoes the concept of multidisciplinary cooperation …and has a solid medical and theoretical basis. The results show that occupational therapy has a significant effect on slowing the deterioration of patients’ cognitive function, improving their daily living ability, and ultimately improving the quality of life of patients. Show more

Keywords: Joint intervention, neuro-cognition, alzheimer’s disease, improvement

DOI: 10.3233/JIFS-179976

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2020

Authors: He, Han | Yan, Hongcui | Liu, Weiwei

Article Type: Research Article

Abstract: In the evaluation of traditional college talents’ teaching ability, the importance of evaluation indicators lacks evaluation, and the evaluation results are relatively random. In order to improve the evaluation efficiency of university scientific research talents, this study combines BP neural network and fuzzy mathematical theory to build an evaluation model. Combining the talent training process and ability requirements of colleges and universities, a secondary index system is proposed, and the weight of the evaluation index is determined by combining data collection. This paper first normalizes the samples, determines the training and test samples, and then uses trial and error to …determine the number of hidden layer neurons. Then use fuzzy mathematics theory to construct fuzzy similarity matrix to describe the fuzzy relationship between factor domain and judgement domain. Calculate membership to get comprehensive evaluation results. Finally, this paper uses statistical methods to draw the results into statistical charts and combines the simulation results to obtain performance comparison results. The feasibility of the model is verified by experimental research, and the model can be applied to practice, and can provide theoretical reference for subsequent related research. Show more

Keywords: BP neural network, fuzzy mathematical, evaluation model, college talent, scientific research

DOI: 10.3233/JIFS-179977

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2020

Authors: Ren, Yangjun

Article Type: Research Article

Abstract: BP neural network method is provided by the outstanding characteristics of self-learning and non-linearity, and can obtain relatively satisfactory prediction results, which also can be used to forecast innovation output. The neural network toolbox function of Matlab can build a neural network prediction model to predict the innovation output from 2008 to 2017. Second, the dynamic SDM is used to empirically test the role of industrial cluster on the innovation efficiency and its space spillover effect by using of the panel data of Chinese cities. The results show the error comparison between the predicted value and real value of innovation …efficiency, which explains the accuracy of BP neural network is higher. There is a spatial distribution pattern in which the innovation efficiency decreases from the east, the middle, and the west, which also has the characteristic of time inertia and positive spatial correlation. The producer service agglomeration has significantly improved the innovation efficiency in this city but has no significant role on the innovation efficiency in neighboring cities. The manufacturing cluster has a significant negative effect on the innovation efficiency in this city in the long and short term but produces a significant positive effect on innovation efficiency in neighboring cities in the long and short term. Show more

Keywords: Industrial agglomeration, innovation efficiency, BP neural network, spatial econometric model

DOI: 10.3233/JIFS-179974

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2020

Authors: Shahsavari-Pour, Nasser | Mohammadi-Andargoli, Hamed | Bahram-Pour, Najmeh

Article Type: Research Article

Abstract: The purpose of this paper is to introduce a new meta-heuristic algorithm and apply this for solving a multi-objective flexible job-shop scheduling problem. The name of this algorithm is Cosmogony (CA). This algorithm has inspired by the ecosystem process of creatures and their environment. For a better understanding, we make an effort to apply the concepts of the meta-heuristic algorithms up to a possible extent. This algorithm identifies local optimal points during the self-search process of problem-solving. Initial creatures have been generated randomly in a certain number. This algorithm incorporates many features of the other algorithms in itself. So that …to prove the ability and efficiency of CA, a flexible job-shop scheduling problem has surveyed. This problem is in a Non-resumable situation with maintenance activity constraints in a two-time fixed and non-fixed state. The algorithm performance is evaluated by numerical experiments. The result has shown the proposed approach is more efficient and appropriate than the other methods. It also has high power in the searching process in the feasible region of the multi-objective flexible job-shop scheduling problem and high converge power. Show more

Keywords: Cosmogony algorithm, meta-heuristic algorithms, flexible job shop scheduling problem, multi-objective optimization

DOI: 10.3233/JIFS-191839

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-27, 2020

Authors: Zhu, Hongmei

Article Type: Research Article

Abstract: English speech recognition system is affected by a variety of interference factors. Associating the algorithm with the support of modern computer technology can increase the model effect of speech recognition system. Based on the study of the current mainstream controlled natural language thesaurus, this paper proposes a controlled natural language vocabulary classification type. Moreover, this paper defines the domain thesaurus according to the WordNet knowledge description framework, and uses WordNet’s synonym, antisense, upper and lower, etc. In this way, the controlled natural language system can use the semantic relationship of WordNet to identify the words of the non-domain thesaurus input …by the user and map the non-domain definition words to the words in the domain thesaurus, thereby improving the ease of use of controlled natural language systems. In addition, this paper designed a controlled experiment to analyze the performance of this system. The research results show that the model constructed in this paper has certain significant effects. Show more

Keywords: Machine learning, spoken English, language, recognition system, intelligent analysis

DOI: 10.3233/JIFS-179975

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2020