Journal of Intelligent & Fuzzy Systems最新文献_第8页

Non-speech emotion recognition based on back propagation feed forward networks 基于反向传播前馈网络的非语音情感识别

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-12 DOI: 10.3233/jifs-238700

Xiwen Zhang, Hui Xiao

{"title":"Non-speech emotion recognition based on back propagation feed forward networks","authors":"Xiwen Zhang, Hui Xiao","doi":"10.3233/jifs-238700","DOIUrl":"https://doi.org/10.3233/jifs-238700","url":null,"abstract":"Non-speech emotion recognition involves identifying emotions conveyed through non-verbal vocalizations such as laughter, crying, and other sound signals, which play a crucial role in emotional expression and transmission. This paper employs a nine-category discrete emotion model encompassing happy, sad, angry, peaceful, fearful, loving, hateful, brave, and neutral. A proprietary non-speech dataset comprising 2337 instances was utilized, with 384-dimensional feature vectors extracted. The traditional Backpropagation Neural Network (BPNN) algorithm achieved a recognition rate of 87.7% on the non-speech dataset. In contrast, the proposed Whale Optimization Algorithm - Backpropagation Neural Network (WOA-BPNN) algorithm, applied to a self-made non-speech dataset, demonstrated a remarkable accuracy of 98.6% . Notably, even without facial emotional cues, non-speech sounds effectively convey dynamic information, and the proposed algorithm excels in their recognition. The study underscores the importance of non-speech emotional signals in communication, especially with the continuous advancement of artificial intelligence technology. The abstract thus encapsulates the paper’s focus on leveraging AI algorithms for high-precision non-speech emotion recognition.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"8 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140249380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Object pose estimation method for robotic arm grasping 用于机械臂抓取的物体姿态估计方法

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-12 DOI: 10.3233/jifs-234351

Cheng Huang, Shuyu Hou

引用次数: 0

Task allocation algorithm for distributed large data stream group computing in the era of digital intelligence 数字智能时代分布式大数据流群计算的任务分配算法

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-12 DOI: 10.3233/jifs-238427

Ling Sun, Rong Jiang, Wenbing Wan

{"title":"Task allocation algorithm for distributed large data stream group computing in the era of digital intelligence","authors":"Ling Sun, Rong Jiang, Wenbing Wan","doi":"10.3233/jifs-238427","DOIUrl":"https://doi.org/10.3233/jifs-238427","url":null,"abstract":"In the era of digital intelligence, this paper studies the task allocation algorithm of distributed large data stream group computing, and reasonably allocates the task of group computing to meet the needs of massive computing and analysis of distributed large data stream. According to the idea of swarm intelligence perception and crowdsourcing platform, the task allocation model of distributed large data stream group computing is constructed to realize the task allocation of group computing. A distributed large data stream group computing task model and a user model are constructed, user attributes are initialized by using the accuracy of the answers submitted by users, the possibility that users can participate in the group computing task is predicted by a logistic regression algorithm, so that user candidate sequences participating in the computing task can be obtained, and the accuracy of the user’s real topics and corresponding topics can be grasped by capturing the candidate users’ real topics and evaluating the accuracy algorithm. Select the users who meet the subject area, update the candidate user sequence, and filter the users again on the basis of fully considering the factors such as information gain, user integrity and cost, so as to get the final user sequence and complete the task allocation of group computing. Experiments show that this method can solve the problem of distributed large data flow group computing task allocation, achieve high accuracy, reduce the cost, and effectively improve the information gain.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140248787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive solar power generation forecasting using enhanced neural network with weather modulation 利用带天气调制的增强型神经网络进行自适应太阳能发电预测

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-12 DOI: 10.3233/jifs-235612

T. Sujeeth, C. Ramesh, Sushila Palwe, Gandikota Ramu, S. J. Basha, Deepak Upadhyay, K. Chanthirasekaran, K. Sivasankari, A. Rajaram

{"title":"Adaptive solar power generation forecasting using enhanced neural network with weather modulation","authors":"T. Sujeeth, C. Ramesh, Sushila Palwe, Gandikota Ramu, S. J. Basha, Deepak Upadhyay, K. Chanthirasekaran, K. Sivasankari, A. Rajaram","doi":"10.3233/jifs-235612","DOIUrl":"https://doi.org/10.3233/jifs-235612","url":null,"abstract":"Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26% . The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"12 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140251330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Economic optimization of fresh logistics pick-up routing problems with time windows based on gray prediction 基于灰色预测的有时间窗口的生鲜物流取货路由问题的经济优化

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-12 DOI: 10.3233/jifs-235260

Yonghong Liang, Xian-long Ge, Yuanzhi Jin, Zhong Zheng, Yating Zhang, Yunyun Jiang

{"title":"Economic optimization of fresh logistics pick-up routing problems with time windows based on gray prediction","authors":"Yonghong Liang, Xian-long Ge, Yuanzhi Jin, Zhong Zheng, Yating Zhang, Yunyun Jiang","doi":"10.3233/jifs-235260","DOIUrl":"https://doi.org/10.3233/jifs-235260","url":null,"abstract":"The rapid development of modern cold chain logistics technology has greatly expanded the sales market of agricultural products in rural areas. However, due to the uncertainty of agricultural product harvesting, relying on the experience values provided by farmers for vehicle scheduling can easily lead to low utilization of vehicle capacity during the pickup process and generate more transportation cost. Therefore, this article adopts a non-linear improved grey prediction method based on data transformation to estimate the pickup demand of fresh agricultural products, and then establishes a mathematical model that considers the fixed vehicle usage cost, the damage cost caused by non-linear fresh fruit and vegetable transportation damage and decay rate, the cooling cost generated by refrigerated transportation, and the time window penalty cost. In order to solve the model, a hybrid simulated annealing algorithm integrating genetic operators was designed to solve this problem. This hybrid algorithm combines local search strategies such as the selection operator without repeated strings and the crossover operator that preserves the best substring to improve the algorithm’s solving performance. Numerical experiments were conducted through a set of benchmark examples, and the results showed that the proposed algorithm can adapt to problem instances of different scales. In 50 customer examples, the difference between the algorithm and the standard value in this paper is 2.30%, which is 7.29% higher than C&S. Finally, the effectiveness of the grey prediction freight path optimization model was verified through a practical case simulation analysis, achieving a logistics cost savings of 9.73% .","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140249901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A connectivity index based on adjacent vertices in cubic fuzzy graph with an application 基于立方模糊图中相邻顶点的连通性指数及其应用

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-12 DOI: 10.3233/jifs-238021

Hao Guan, S. Sadati, A. Talebi, J. Shafi, Aysha Khan

引用次数: 0

A level set approach using adaptive local pre-fitting energy for image segmentation with intensity non-uniformity 利用自适应局部预拟合能量的水平集方法，用于强度不均匀的图像分割

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-12 DOI: 10.3233/jifs-237629

Pengqiang Ge, Yiyang Chen, Guina Wang, G. Weng, Hongtian Chen

{"title":"A level set approach using adaptive local pre-fitting energy for image segmentation with intensity non-uniformity","authors":"Pengqiang Ge, Yiyang Chen, Guina Wang, G. Weng, Hongtian Chen","doi":"10.3233/jifs-237629","DOIUrl":"https://doi.org/10.3233/jifs-237629","url":null,"abstract":"Active contour model (ACM) is considered as one of the most frequently employed models in image segmentation due to its effectiveness and efficiency. However, the segmentation results of images with intensity non-uniformity processed by the majority of existing ACMs are possibly inaccurate or even wrong in the forms of edge leakage, long convergence time and poor robustness. In addition, they usually become unstable with the existence of different initial contours and unevenly distributed intensity. To better solve these problems and improve segmentation results, this paper puts forward an ACM approach using adaptive local pre-fitting energy (ALPF) for image segmentation with intensity non-uniformity. Firstly, the pre-fitting functions generate fitted images inside and outside contour line ahead of iteration, which significantly reduces convergence time of level set function. Next, an adaptive regularization function is designed to normalize the energy range of data-driven term, which improves robustness and stability to different initial contours and intensity non-uniformity. Lastly, an improved length constraint term is utilized to continuously smooth and shorten zero level set, which reduces the chance of edge leakage and filters out irrelevant background noise. In contrast with newly constructed ACMs, ALPF model not only improves segmentation accuracy (Intersection over union(IOU)), but also significantly reduces computation cost (CPU operating time T), while handling three types of images. Experiments also indicate that it is not only more robust to different initial contours as well as different noise, but also more competent to process images with intensity non-uniformity.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"137 39","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140251463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MAO-DBN based membrane fouling prediction 基于 MAO-DBN 的膜污垢预测

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-12 DOI: 10.3233/jifs-233655

Zhiwen Wang, Yibin Zhao, Yaoke Shi, Guobi Ling

{"title":"MAO-DBN based membrane fouling prediction","authors":"Zhiwen Wang, Yibin Zhao, Yaoke Shi, Guobi Ling","doi":"10.3233/jifs-233655","DOIUrl":"https://doi.org/10.3233/jifs-233655","url":null,"abstract":"Due to the complexity of the factors influencing membrane fouling in membrane bioreactors (MBR), it is difficult to accurately predict membrane fouling. This paper proposes a multi-strategy of integration aquila optimizer deep belief network (MAO-DBN) based membrane fouling prediction method. The method is developed to improve the accuracy and efficiency of membrane fouling prediction. Firstly, partial least squares (PLS) are used to reduce the dimensionality of many membrane fouling factors to improve the algorithm’s generalization ability. Secondly, considering the drawbacks of deep belief network (DBN) such as long training time and easy overfitting, piecewise mapping is introduced in aquila optimizer (AO) to improve the uniformity of population distribution, while adaptive weighting is used to improve the convergence speed and prevent falling into local optimum. Finally, the prediction of membrane fouling is carried out by utilizing membrane fouling data as the research object. The experimental results show that the method proposed in this paper can achieve accurate prediction of membrane fluxes, with an 88.45% reduction in RMSE and 87.53% reduction in MAE compared with the DBN model before improvement. The experimental results show that the model proposed in this paper achieves a prediction accuracy of 98.61%, both higher than other comparative models, which can provide a theoretical basis for membrane fouling prediction in the practical operation of membrane water treatment.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"42 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140248126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect of animal behavior on EEG microstates in healthy children: An outdoor observation task 动物行为对健康儿童脑电图微观状态的影响：户外观察任务

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-11 DOI: 10.3233/jifs-235533

Xiaoting Ding, Jiuchuan Jiang, Mengting Wei, Yue Leng, Haixian Wang

{"title":"Effect of animal behavior on EEG microstates in healthy children: An outdoor observation task","authors":"Xiaoting Ding, Jiuchuan Jiang, Mengting Wei, Yue Leng, Haixian Wang","doi":"10.3233/jifs-235533","DOIUrl":"https://doi.org/10.3233/jifs-235533","url":null,"abstract":"Analyzing physiological signals in the brain under outdoor conditions, like observing animal behavior, forms the normative basis for the outdoor task and provides new insights into the cognitive neuronal mechanisms of children’s functional brain systems. Here we investigated EEG data from a cohort of seventeen children (6–7 years old, 30-channel EEG) in the resting state and animal-observation state, using the microstate method combined with source-localization analysis to identify the changes in network-level functional interactions. Our study suggested that: while observing animal behavior, the parameters (global explained variance, occurrence, coverage, and duration) of microstates showed a regular trend, and the dynamic reorganization patterns of children’s brains were associated with verbal input networks and higher-order cognitive networks; the activity of the brain network in the frontal and temporal lobes of children increased, while the activity of the insula brain area decreased after observing the behavioral activities of animals. This study may be essential to understand the effects of animal behavior on changes in healthy children’s emotions and have important implications for education.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"9 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140253956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy logical system for personalized vocal music instruction and psychological awareness in colleges using big data 利用大数据实现高校个性化声乐教学与心理认知的模糊逻辑系统

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-11 DOI: 10.3233/jifs-236248

Yu Wang

{"title":"Fuzzy logical system for personalized vocal music instruction and psychological awareness in colleges using big data","authors":"Yu Wang","doi":"10.3233/jifs-236248","DOIUrl":"https://doi.org/10.3233/jifs-236248","url":null,"abstract":"Traditional psychological awareness relating to vocal musical instruction often disregards the impact of earlier experiences on music learning could result in a gap in meeting the needs of individual students. Conventional learning techniques of music related to psychological awareness for each individual has been focused on and addressed in this research. Technological upgrades in Fuzzy Logic (FL) and Big Data (BD) related to Artificial Intelligence (AI) are provided as a solution for the existing challenges and provide enhancement in personalized music education. The combined approach of BD-assisted Radial Basis Function is added with the Takagi Sugeno (RBF-TS) inference system, able to give personalized vocal music instruction recommendations and indulge psychological awareness among students. Applying Mel-Frequency Cepstral Coefficients (MFCC) is beneficial in capturing variant vocal characteristics as a feature extraction technique. The BD-assisted RBF can identify the accuracy of pitch differences and quality of tone, understand choices from students, and stimulate psychological awareness. The uncertainties are addressed by using the TS fuzzy inference system and delivering personalized vocal training depending on different student preference factors. With the use of multimodal data, the proposed RBF-TS approach can establish a fuzzy rule base in accordance with the personalized emotional elements, enhancing self-awareness and psychological well-being. Validation of the proposed approach using an Instruction Resource Utilization Rate (IRUR) gives significant improvements in engaging students, analyzing the pitching accuracy, frequency distribution of vocal music instruction, and loss function called Mean Square Error(MSE). The proposed research algorithm pioneers a novel solution using advanced AI algorithms addressing the research challenges in existing personalized vocal music education. It promises better student outcomes in the field of music education.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"8 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140252638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Journal of Intelligent &amp; Fuzzy Systems最新文献

Journal of Intelligent & Fuzzy Systems最新文献