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129 results for “Information Extraction”
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Research ArticleOpen accessIn the abstractAnimal Health Prediction Using Hybrid CNN Based BiLSTM Classification Model: A Deep Learning ApproachJ. Rathi, A. SumathiAccurate prediction of animal diseases is vital in veterinary medicine, as it can substantially enhance animal health outcomes and mitigate economic losses, making it a critical task that warrants attention and innovative solutions. This paper proposes a novel approach to animal condition classification, leveraging an Auto-encoder-based feature selection process and an Improved Hybrid Convolutional Neural Networks (CNN) with Bidirectional (Bi-LSTM) … …Journal of Computer Science, Vol. 22, Issue 5 · 20 May 2026 · 1,481 accessesView PDFCite
Research ArticleOpen accessIn the abstractBoosting Arabic Named Entity Recognition with K-Fold Cross Validation on LSTM and Bi-LSTM ModelsHamid Sadeq Mahdi Alsultani, Ahmed H. AliwyNamed-Entity-Recognition(NER) is one of the most important Information-Extraction (IE) use cases, whichis used to improve the performance of Natural Languages Processing (NLP) tasks,such as Relation-Extraction (RE), Question-Answering (QA). Recently, Arabic NER is tackled in differentways by researchers. In this study, we assess the performance of two widelyused models, namely, LSTM and Bi-LSTM on the NER task in the Arabic languageand … …Journal of Computer Science, Vol. 18, Issue 9 · 6 Sep 2022 · 6,317 accessesView PDFCite
Research ArticleOpen accessIn the abstractUltrasound-Assisted Enzymatic Extraction of Polysaccharides from Waste Corn Bract: Process Optimization, Characterization, Antioxidant and Anti-Diabetic PotentialsYihui Liu, Yayi Li, Na Niu et al.Corn bract is the main agricultural waste of corn production and processing. Little information is focused on its value-added utilization. In the present study, the Ultrasound-Assisted Enzymatic Extraction (UAEE) was used for the first time to extract Corn Bract Polysaccharides (CBPs). The optimum conditions of UAEE for CBPs extraction were: Cellulase amount of 0.7%, enzymolysis for 58 min, and ultrasound … …American Journal of Biochemistry and Biotechnology, Vol. 18, Issue 2 · 28 Jun 2022 · 6,126 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Recommendation-Based Contextual Model for Talent AcquisitionChannabasamma A., Yeresime SureshIt is important to assist the job seekers in selecting the perfect jobs, which suit candidates' current skills and career objectives. Given the job description and resumes in the unstructured form, choosing the best job manually is a tedious task, so there is a need for an automated system to deal with raw data. The extraction of structured information from … …Journal of Computer Science, Vol. 18, Issue 7 · 20 Jul 2022 · 6,122 accessesView PDFCite
Research ArticleOpen accessIn the abstractHarmonic Mean Projection Shape Transform for Leaf ClassificationSophia Jamila Zahra, Riza Sulaiman, Seyed M.M. Kahaki, Anton Satria PrabuwonoShape feature extraction has emerged as an important part of computer vision and image processing applications. Identification of shape objects in huge data based on shape similarity still remains a challenging problem due to the similarity of the shapes and existing of classes with similar contour information. This paper presents to investigate the shape analysis for plant leaf classification by … …Journal of Computer Science, Vol. 16, Issue 9 · 13 Sep 2020 · 6,854 accessesView PDFCite
Research ArticleOpen accessIn the abstractMultipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU AccelerationToto Haryanto, Adib Pratama, Heru Suhartanto et al.Cancer is one of the leading causes of death in the world. It is the main reason why research in this field becomes challenging. Not only for the pathologist but also from the view of a computer scientist. Hematoxylin and Eosin (H&E) images are the most common modalities used by the pathologist for cancer detection. The status of cancer with … …Journal of Computer Science, Vol. 16, Issue 3 · 5 Mar 2020 · 9,215 accessesView PDFCite
Research ArticleOpen accessIn the abstractData Warehouse Design to Support Social Media Analysis in a Big Data EnvironmentCarlos Roberto Valêncio, Luis Marcello Moraes Silva, William Tenório et al.The volume of generated and stored data from social media has increased in the last decade. Therefore, analyzing and understanding this kind of data can offer relevant information in different contexts and can assist researchers and companies in the decision-making process. However, the data are scattered in a large volume, come from different sources, with different formats and are rapidly … …Journal of Computer Science, Vol. 16, Issue 2 · 20 Feb 2020 · 8,223 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Transformer Based Model to Detect Environmental Related Distress on Online Social MediaUtkarsh Sharma, Prateek Pandey, Shishir KumarOnline social media has been recently employed as the source of data for the detection of traffic-related activities and to control the flow of traffic in smart cities all over the world. Till now an ample amount of research work has been done in identification of traffic-related incidents with the help of online social media platforms (mostly Twitter). Along with … …Journal of Computer Science, Vol. 22, Issue 11 · 9 Oct 2026 · 462 accessesView PDFCite
Research ArticleOpen accessIn the abstractUMPM-Net: A Multimodal Deep Learning Framework for Colorectal Cancer PrognosisK. Muthuchamy, S K. Piramu PreethikaColorectal cancer (CRC) is one of the most common cancers worldwide, for which early prediction of patient outcomes is important for personalised treatment planning and to improve survival. However, current prediction systems are limited in the fact that they are based on single-modality data and cannot capture the complex interactions between clinical, imaging, and genomic factors. To overcome these shortcomings, … …Journal of Computer Science, Vol. 22, Issue 9 · 20 Sep 2026 · 613 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid CNN-Based Transformer Pipeline With Radiomic Fusion for Multi-Class Lung Cancer DetectionAanchal Vij, Kuldeep Singh Kaswan, Anand NayyarEarly detection of lung cancer remains challenging due to high intra-class variation and inter-class similarity in Computed Tomography (CT) images. In this paper, we propose a hybrid deep learning model that combines convolutional, attention-based, and transformer-guided representations to address these challenges in multi-class lung cancer classification. For deep feature extraction, we use the EfficientNetV2-S architecture augmented with a Convolutional Block … …Journal of Computer Science, Vol. 22, Issue 6 · 3 Jun 2026 · 1,528 accessesView PDFCite