Emma Kieny^1,2,3, Kévin Candelier^2,3,*, Louis Milhe¹, Yannick Estevez⁴, Cyrielle Sophie⁴, Romain Lehnebach¹, Jérémie Damay^2,3, Daniela Florez¹, Emeline Houël⁵, Marie-France Thévenon^2,3, Julie Bossu⁴

Journal of Renewable Materials, Vol.13, No.1, pp. 79-100, 2025, DOI:10.32604/jrm.2024.056731 - 20 January 2025

Abstract The valorization of Amazonian wood residues into active chemical compounds could be an eco-friendly, cost-effective and valuable way to develop wood preservative formulations to enhance the decay and termite resistance of low-durable wood species. Wacapou (Vouacapoua americana., Fabaceae) is a well-known Guianese wood species commonly used in local wood construction due to its outstanding natural durability, which results from the presence of a large panel of extractives compounds. In addition, its industrial processing generates large amounts of residues. Wacapou residues were extracted by maceration using four different solvents (water/ethanol, ethyl acetate, hexane and dichloromethane/methanol), separately and… More > Graphic Abstract

Alexandr Arshanitsa^*, Matiss Pals, Daniela Godina, Oskars Bikovens

Journal of Renewable Materials, Vol.12, No.11, pp. 1927-1948, 2024, DOI:10.32604/jrm.2024.056466 - 22 November 2024

Abstract The isolated hydrophilic black alder (Alnus glutinosa) bark extractives were characterized in terms of component and functional composition and converted at 150°C–170°C into liquid green polyols using solvent-free and low-toxic base-catalyzed modification with propylene carbonate (PC). FTIR spectroscopy, HP-LC, GC, GPC, and wet chemistry methods were used to characterize the starting constituents, intermediate and final products of the reaction and to monitor the different pathways of PC conversion. The reaction of extractives as well as the model compounds, including catechol, xylose, PEG 400, and benzoic acid, with PC indicated the ability of OH groups of different… More > Graphic Abstract

Sohail Muhammad¹, Muzammil Khan², Sarwar Shah Khan^2,3,*

Journal on Artificial Intelligence, Vol.6, pp. 193-209, 2024, DOI:10.32604/jai.2024.052099 - 07 August 2024

Abstract Retrieving information from evolving digital data collection using a user’s query is always essential and needs efficient retrieval mechanisms that help reduce the required time from such massive collections. Large-scale time consumption is certain to scan and analyze to retrieve the most relevant textual data item from all the documents required a sophisticated technique for a query against the document collection. It is always challenging to retrieve a more accurate and fast retrieval from a large collection. Text summarization is a dominant research field in information retrieval and text processing to locate the most appropriate… More >

P. Sharmila^1,*, C. Deisy¹, S. Parthasarathy²

Computer Systems Science and Engineering, Vol.45, No.1, pp. 377-393, 2023, DOI:10.32604/csse.2023.027481 - 16 August 2022

Abstract With the continuous growth of online news articles, there arises the necessity for an efficient abstractive summarization technique for the problem of information overloading. Abstractive summarization is highly complex and requires a deeper understanding and proper reasoning to come up with its own summary outline. Abstractive summarization task is framed as seq2seq modeling. Existing seq2seq methods perform better on short sequences; however, for long sequences, the performance degrades due to high computation and hence a two-phase self-normalized deep neural document summarization model consisting of improvised extractive cosine normalization and seq2seq abstractive phases has been proposed… More >

Abeer Abdulaziz AlArfaj, Hanan Ahmed Hosni Mahmoud^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4231-4244, 2022, DOI:10.32604/cmc.2022.030090 - 16 June 2022

Abstract In recent research, deep learning algorithms have presented effective representation learning models for natural languages. The deep learning-based models create better data representation than classical models. They are capable of automated extraction of distributed representation of texts. In this research, we introduce a new tree Extractive text summarization that is characterized by fitting the text structure representation in knowledge base training module, and also addresses memory issues that were not addresses before. The proposed model employs a tree structured mechanism to generate the phrase and text embedding. The proposed architecture mimics the tree configuration of… More >

Wissem Sahmim, Georges Eid, Febrina Dellarose Boer, Hubert Chapuis, Philippe Gérardin, Christine Gérardin-Charbonnier^*

Journal of Renewable Materials, Vol.10, No.6, pp. 1491-1503, 2022, DOI:10.32604/jrm.2022.016364 - 20 January 2022

Abstract This study evaluated the methods of grafting commercial catechin with fatty acids, namely capric acid (C10), lauric acid (C12), and myristic acid (C14) through esterification. Specimens of beech wood (Fagus sylvatica L.) were impregnated with catechin and modified catechin-fatty acids, separately, at a 5% concentration diluted in ethanol using vacuum pressure treatment and subjected to leaching. The weight percentage gain before leaching (WPG), after leaching (WPGAL), and weight loss due to leaching (PL) were investigated. Both leached and unleached samples were tested against white-rot fungi (Trametes versicolor) in Petri-dishes for twelve weeks. Results show that samples treated More > Graphic Abstract

Surbhi Bhatia^*, Mohammed AlOjail

Computer Systems Science and Engineering, Vol.42, No.2, pp. 779-794, 2022, DOI:10.32604/csse.2022.022579 - 04 January 2022

Abstract Opinion summarization recapitulates the opinions about a common topic automatically. The primary motive of summarization is to preserve the properties of the text and is shortened in a way with no loss in the semantics of the text. The need of automatic summarization efficiently resulted in increased interest among communities of Natural Language Processing and Text Mining. This paper emphasis on building an extractive summarization system combining the features of principal component analysis for dimensionality reduction and bidirectional Recurrent Neural Networks and Long Short-Term Memory (RNN-LSTM) deep learning model for short and exact synopsis using… More >

Sarah S. Alrumiah^*, Amal A. Al-Shargabi

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6205-6221, 2022, DOI:10.32604/cmc.2022.021780 - 11 October 2021

Abstract Nowadays, people use online resources such as educational videos and courses. However, such videos and courses are mostly long and thus, summarizing them will be valuable. The video contents (visual, audio, and subtitles) could be analyzed to generate textual summaries, i.e., notes. Videos’ subtitles contain significant information. Therefore, summarizing subtitles is effective to concentrate on the necessary details. Most of the existing studies used Term Frequency–Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) models to create lectures’ summaries. This study takes another approach and applies Latent Dirichlet Allocation (LDA), which proved its effectiveness in document… More >

Ebrahim Heidary¹, Hamïd Parvïn^2,3,4,*, Samad Nejatian^5,6, Karamollah Bagherifard^1,6, Vahideh Rezaie^6,7

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2845-2861, 2021, DOI:10.32604/cmc.2021.014361 - 24 August 2021

Abstract With the remarkable growth of textual data sources in recent years, easy, fast, and accurate text processing has become a challenge with significant payoffs. Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents, which must be done without losing important features and information. This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure. The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the More >

Neeraj Kumar Sirohi^1,*, Mamta Bansal¹, S. N. Rajan²

Journal on Big Data, Vol.3, No.1, pp. 35-47, 2021, DOI:10.32604/jbd.2021.015954 - 25 January 2021

Abstract Nowadays, data is very rapidly increasing in every domain such as social media, news, education, banking, etc. Most of the data and information is in the form of text. Most of the text contains little invaluable information and knowledge with lots of unwanted contents. To fetch this valuable information out of the huge text document, we need summarizer which is capable to extract data automatically and at the same time capable to summarize the document, particularly textual text in novel document, without losing its any vital information. The summarization could be in the form of… More >