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排序方式: 共有186条查询结果,搜索用时 15 毫秒
1.
Rauf Tavakoli Bakhshi Puyan Mirarab Meysam Shahbazi Khalil 《Theoretical Foundations of Chemical Engineering》2020,54(5):995-1004
Theoretical Foundations of Chemical Engineering - In this work, with the aim of accurate prediction of water content, H2S and CO2 absorption capacity of diethanolamine (DEA) solvent in Khangiran... 相似文献
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Meraj Talha Rauf Hafiz Tayyab Zahoor Saliha Hassan Arslan Lali M. IkramUllah Ali Liaqat Bukhari Syed Ahmad Chan Shoaib Umar 《Neural computing & applications》2021,33(17):10737-10750
Neural Computing and Applications - Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of... 相似文献
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J. Mustafa M. S. Ahmad Jr. A. Rauf S. M. Osman 《Journal of the American Oil Chemists' Society》1990,67(12):937-939
Branched-chain thioethers have been prepared from methyl 4-oxo-trans-2-hexadecenoate and 9,12-dioxo-trans-10-octadecenoic acid. The reagents involved in these preparations were mercaptoacetic and mercaptopropionic acids. The yields
of these thioethers are almost quantitative. 相似文献
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Jamal Mustafa M. S. Ahmad A. Rauf S. M. Osman 《Journal of the American Oil Chemists' Society》1984,61(3):555-558
Methyl 4-oxo-trans-2-octadecenoate (II), when treated with excess hydrazoic acid in the presence of BF3-etherate, produced 66% methyl 5-aza-nonadec-trans-2-enoate (4,5-d)-tetrazole (III), 10% methyl 5-aza-nonadec-4-oxo-trans-2-enoate (IV) and 7% pentadecamide (V). Individual products were characterized by spectral and elemental methods. 相似文献
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Razzaq Saad Shah Babar Iqbal Farkhund Ilyas Muhammad Maqbool Fahad Rocha Alvaro 《Neural computing & applications》2023,35(11):8017-8026
Neural Computing and Applications - A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart... 相似文献
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R. Bhaskaran S. Saravanan M. Kavitha C. Jeyalakshmi Seifedine Kadry Hafiz Tayyab Rauf Reem Alkhammash 《计算机系统科学与工程》2023,44(1):235-247
Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover, TF-IDF vectorizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments. Furthermore, optimal parameter tuning is done for ELMSAE model using MRDA technique. A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced efficiency of MRDA-ELMSAE technique against other recent techniques. 相似文献
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This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents. 相似文献