J. Intell. Fuzzy Syst.最新文献

Nesterov-accelerated Adaptive Moment Estimation NADAM-LSTM based text summarization1 基于内斯特罗夫加速自适应矩估计 NADAM-LSTM 的文本摘要1

J. Intell. Fuzzy Syst. Pub Date : 2024-01-31 DOI: 10.3233/jifs-224299

P. Radhakrishnan, G. S. Kumar

{"title":"Nesterov-accelerated Adaptive Moment Estimation NADAM-LSTM based text summarization1","authors":"P. Radhakrishnan, G. S. Kumar","doi":"10.3233/jifs-224299","DOIUrl":"https://doi.org/10.3233/jifs-224299","url":null,"abstract":"Automatic text summarization is the task of creating concise and fluent summaries without human intervention while preserving the meaning of the original text document. To increase the readability of the languages, a summary should be generated. In this paper, a novel Nesterov-accelerated Adaptive Moment Estimation Optimization based on Long Short-Term Memory [NADAM-LSTM] has been proposed to summarize the text. The proposed NADAM-LSTM model involves three stages namely pre-processing, summary generation, and parameter tuning. Initially, the Giga word Corpus dataset is pre-processed using Tokenization, Word Removal, Stemming, Lemmatization, and Normalization for removing irrelevant data. In the summary generation phase, the text is converted to the word-to-vector method. Further, the text is fed to LSTM to summarize the text. The parameter of the LSTM is then tuned using NADAM Optimization. The performance analysis of the proposed NADAM-LSTM is calculated based on parameters like accuracy, specificity, Recall, Precision, and F1 score. The suggested NADAM-LSTM achieves an accuracy range of 99.5%. The result illustrates that the proposed NADAM-LSTM enhances the overall accuracy better than 12%, 2.5%, and 1.5% in BERT, CNN-LSTM, and RNN respectively.","PeriodicalId":518977,"journal":{"name":"J. Intell. Fuzzy Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529472","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

Three-way concepts in the interval-valued formal contexts 区间值形式语境中的三向概念

J. Intell. Fuzzy Syst. Pub Date : 2024-01-31 DOI: 10.3233/jifs-236146

Rongle Zhao, Xiao Tang

引用次数: 0

Temperature prediction and scheduling of data center based on segmented neural network 基于分段神经网络的数据中心温度预测与调度

J. Intell. Fuzzy Syst. Pub Date : 2024-01-31 DOI: 10.3233/jifs-231320

Simin Wang, Yifei Kang, Yixuan Xu, Chunmiao Ma, Jinyu Wang, Weiguo Wu

{"title":"Temperature prediction and scheduling of data center based on segmented neural network","authors":"Simin Wang, Yifei Kang, Yixuan Xu, Chunmiao Ma, Jinyu Wang, Weiguo Wu","doi":"10.3233/jifs-231320","DOIUrl":"https://doi.org/10.3233/jifs-231320","url":null,"abstract":"Task scheduling based on temperature perception is beneficial for avoiding hotspots and optimizing the internal temperature distribution of data centers. However, the accuracy of task scheduling largely depends on the accuracy of temperature prediction. There are many features that affect the accuracy of temperature prediction in data centers, and the variation periods of these features vary greatly. Traditional machine learning models are difficult to accurately fit them. Therefore, this article proposes a step-by-step temperature prediction algorithm based on Gated Recurrent Unit (GRU). This algorithm establishes prediction models for important parameters such as CPU utilization and air conditioning temperature that affect temperature prediction, and uses the outputs of these two models as inputs for the server temperature prediction model to better fit the changes of feature values. The model combines the principle of thermal locality and integrates the temperature of upper and lower servers for joint modeling. Experiments show that our prediction model can accurately predict the inlet temperature evolution of the server with dynamic workload. RSME reaches 0.278 and the average prediction temperature difference is 0.633, which is much higher than the traditional model. In addition, this article also propose a minimum temperature difference scheduling algorithm based on temperature prediction model, which can effectively reduce the number of servers running at high temperature and low temperature in the data center, make the temperature of the data center more balanced and achieve better energy-saving compared with other baseline algorithms.","PeriodicalId":518977,"journal":{"name":"J. Intell. Fuzzy Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529221","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

Elimination of heart sound from respiratory sound using adaptive variational mode decomposition for pulmonary diseases diagnosis 利用自适应变异模式分解从呼吸音中消除心音，用于肺部疾病诊断

J. Intell. Fuzzy Syst. Pub Date : 2024-01-30 DOI: 10.3233/jifs-231127

K. S. Yamuna, S. Thirunavukkarasu, B. Manjunatha, B. Karthikeyan

{"title":"Elimination of heart sound from respiratory sound using adaptive variational mode decomposition for pulmonary diseases diagnosis","authors":"K. S. Yamuna, S. Thirunavukkarasu, B. Manjunatha, B. Karthikeyan","doi":"10.3233/jifs-231127","DOIUrl":"https://doi.org/10.3233/jifs-231127","url":null,"abstract":"Lung sound (LS) signals are a vital source of information for the identification of pulmonary disorders. Heart sound (HS) is the most common contaminant of lung sounds during auscultation from the chest walls. This directly affects the efficiency of lung sound processing in diagnosing lung diseases. In this work, Adaptive Variational Mode Decomposition (AVMD) technique is proposed to remove heart sound contaminants from lung sounds. The proposed AVMD method initially breakdown the noisy lung sound signal into a collective of bandlimited modes called variational mode functions (VMF). Then, based on the frequency spectrum, the HS is filtered out from the LS. The real time lung sound data is collected from 95 participants and the performance of VMD technique is evaluated using the statistical metrics measures. Thus, the proposed topology exhibits Higher SNR (29.6587dB, lowest Root Mean Square (RMSE) of 0.0102, lowest normalized Mean Absolute Error (nMAE) of 0.0336, and highest percentage in correlation coefficient Factor (CCF) of 99.79% respectively. These experimental results are found to be superior and outperform all other recently proposed techniques.","PeriodicalId":518977,"journal":{"name":"J. Intell. Fuzzy Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529143","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

Grey markov land pattern analysis and forecasting model incorporating social factors 包含社会因素的灰色马尔可夫土地模式分析和预测模型

J. Intell. Fuzzy Syst. Pub Date : 2024-01-29 DOI: 10.3233/jifs-235965

Zhifei Zhang, Shenmin Wang

{"title":"Grey markov land pattern analysis and forecasting model incorporating social factors","authors":"Zhifei Zhang, Shenmin Wang","doi":"10.3233/jifs-235965","DOIUrl":"https://doi.org/10.3233/jifs-235965","url":null,"abstract":"The focus of attention has shifted to land use and land cover changes as a result of the world’s fast urbanisation, and logical planning of urban land resources depends greatly on the forecast and analysis of these changes. In order to more precisely forecast and assess patterns of land use change, the study suggests a grey Markov land pattern analysis and prediction model that incorporates social aspects. The study builds a land pattern analysis and prediction model using a major city as the research object. The outcomes demonstrated the high accuracy and reliability of the grey Markov land pattern analysis and prediction model incorporating social factors, which can more accurately reflect and predict the land use pattern of the study area, with an average relative error of less than 0.01, an accuracy of more than 98%, and an overall fit that has increased by more than 3% . The overall pattern of change is very consistent with the reality. The model predicts that the main trend of future land use in the study area is the continued expansion of urban land such as industrial land, land for transport facilities and land for settlements, while non-construction land such as agricultural land and forest land will continue to decrease. The optimized land pattern analysis and prediction model of the study has a good application environment.","PeriodicalId":518977,"journal":{"name":"J. Intell. Fuzzy Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529606","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

An efficient hybrid bert model for brain tumor classification 用于脑肿瘤分类的高效混合伯特模型

J. Intell. Fuzzy Syst. Pub Date : 2024-01-03 DOI: 10.3233/jifs-237653

S. S. P. Kumar, C. A. Kumar, Anita Venugopal, Aditi Sharma

{"title":"An efficient hybrid bert model for brain tumor classification","authors":"S. S. P. Kumar, C. A. Kumar, Anita Venugopal, Aditi Sharma","doi":"10.3233/jifs-237653","DOIUrl":"https://doi.org/10.3233/jifs-237653","url":null,"abstract":"The central nervous system can develop complex and deadly neoplastic growths called brain tumors. Despite being relatively uncommon in comparison to other cancers, brain tumors pose particular challenges because of their delicate anatomical placement and interactions with critical brain regions. The data are taken from TCIA (The Cancer Image Archive) and Kaggle Datasets. Images are first pre-processed using amplified median filter techniques. The pre-processed images are then segmented using the Grabcut method. Feature extraction is extracted using the Shape, ABCD rule, and GLCM are the features were retrieved. The MRI images are then classified into several classes using the Bi-directional Encoder Representations from Transformers-Bidirectional Long Short Term Memory (BERT-Bi-LSTM) model. Kaggle and TICA datasets are used to simulate the proposed approach, and the results are evaluated in terms of F1-score, recall, precision and accuracy. The proposed model shows improved brain tumour identification and classification. To evaluate the expected technique’s efficacy, a thorough comparison of the current techniques with preceding methods is made. The trial results showed that an efficient hybrid bert model for brain tumor classification suggested strategy provided precision of 98.65%, F1-score of 98.25%, recall of 99.25%, and accuracy of 99.75% .","PeriodicalId":518977,"journal":{"name":"J. Intell. Fuzzy Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532983","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