Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classifica...
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Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.
Purpose: Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially for 3D non-Cartesian acquisitions. On...
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Human cognitive processes remain an area of strong interest and ongoing research. One tool to gain greater insight into this process is neuronal modeling. The following features are desirable in a neuronal modeling to...
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Sample preparation is essential for nucleic acid assays, affecting their sensitivity and reliability. However, this process often results in a significant loss or dilution of the analyte, which becomes a bottleneck th...
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Radar-based fall detection is helping seniors to live independently. In this paper, a new method for fall detection from human activities is proposed using data augmentation and class imbalance handling. Data collecte...
Radar-based fall detection is helping seniors to live independently. In this paper, a new method for fall detection from human activities is proposed using data augmentation and class imbalance handling. Data collected from a radar signal is processed and a time series is obtained by aggregating the individual time series in the fast-time of the radar returns. This time series is used as input to several classifiers to distinguish fall from non-fall activities. To this end, we augment the radar time series data using a Mu-Sigma method. To handle the imbalanced data, we apply the synthetic minority over-sampling technique and class weighting strategy. A comprehensive study is performed to build a supervised learning method with or without data augmentation and imbalanced data handling. The performance of the proposed method is compared with some of the other existing methods using different classifiers including k-nearest neighbors, decision trees, naive Bayes, support vector machine and multi-layer perceptron. The results demonstrate that the proposed fall detection method outperforms the other methods in terms of providing higher accuracy, precision, sensitivity and specificity values.
Sb2Se3 is used to switch between broadband transparency and enhanced index contrast in two device types leveraging Bragg gratings for tunable stop- and pass-band functionalities. Experimental results highlight fabrica...
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Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortalit...
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The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can s...
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Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conven...
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Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conventionally. Supposedly with the advancement of technology and the use of the internet everywhere, learning such as e-learning can be important, especially in the health sector. Until this research was conducted, only 514 academic documents about e-learning in health sciences were found for 20 years from 2001 to 2020, obtained in searching on the Scopus database. This study presents a comprehensive overview of studies related to E-learning in the Health sciences sector. This study uses bibliometric analysis and indexed digital methods to map scientific publications throughout the world. This research employs the Scopus database to gather information, as well as the Scopus online analysis tool and Vosviewer to show the bibliometric network. The method consists with five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the most published and indexed articles by Scopus, papers published by researchers in the United States have the highest number of publications (80), followed by United Kingdom (63) and Australia with 45 academic publications. The processed data shows the pattern and trend of increasing the number of international publications in E-learning in Health sciences field, which Scopus index.
Deep Neural Networks (DNNs) are prone to learning spurious features that correlate with the label during training but are irrelevant to the learning problem. This hurts model generalization and poses problems when dep...
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