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Abstractive Text Summarization Models Using Machine Learning Algorithms

Abstractive Text Summarization Models Using Machine Learning...

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international conference on Computing Communication Control and Automation (ICCUBEA)

作者： Yogita P. Narwadkar Anant M. Bagade Department of Computer Engineering SCTR's Pune Institute of Computer Technology Pune India Department of Information Technology SCTR's Pune Institute of Computer Technology Pune India

In today's era of cyberspace and intermedia, the number of e-documents have been enlarged enormously. the speedy growth of large data and documents in the field of Data Mining and emerging domain such as information Retrieval (IR) and the demanding area of Natural language processing (NLP) needs Automated Text Summarization. Text Summarization is a process that decreases size of the source document while keeping its important information with actual meaning. It is burdensome and laborious for Homo sapiens to physically do the summarization of huge documents of text. thus it is necessary to revise old methods and techniques of abstractive text summarization models and modernize them to be efficient and fulfill the requirements of users as per the need by resolving issues of continuously increased data. Now this paper presents a survey of the current futuristic models and the various algorithms and techniques used in Abstractive Text Summarization systems liable to be subjected to an Encoder-Decoder framework. this research can help to gain the overall concepts of recent machine learning algorithms and encoder-decoder architecture resting on abstractive text summarization prototypes with a review of the challenges, issues, and future scope of these models. this research can help solve the research community's problems, newsletters, email overload, and media monitoring.

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Auto-Generating Business Process Model from Heterogeneous Documents: A Comprehensive Literature Survey 9

Auto-Generating Business Process Model from Heterogeneous Do...

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9th international conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022

作者： Indahyanti, Uce Djunaidy, Arif Siahaan, Daniel Institut Teknologi Sepuluh Nopember Dept. of Informatics Surabaya Indonesia Universitas Muhammadiyah Sidoarjo Faculty of Science and Technology Indonesia Institut Teknologi Sepuluh Nopember Dept. of Information Systems Surabaya Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

Automatically generated business processes can be sourced from documents in the form of structured text or natural language. Although there have been several studies discussing generating business processes, there is still no research using systematically reviews as the source documents. this study presents a systematic literature review on document sources, methods, and challenges in generating business processes. We conducted a systematic literature review published from 2017 to early 2022 and identified 24 main studies discussing the sources of documents in generating business processes. We formulated and applied inclusion and exclusion criteria in two stages to determine the most relevant studies for our research goal. this literature review found that the most frequently used document sources were textual business rules (structured rules), using case diagrams (14 main studies), event logs (7 studies), and natural language text (3 studies) including customer feedback. In the aspect of the method, the most widely used method is in the field is natural language processing (NLP), followed by other methods such as semantic knowledge engineering (SKE), fuzzy, graph-based, deep learning, and the combination of NLP with deep learning. Meanwhile, the challenges faced in generating business processes include text preprocessing or document extraction, integration of business rules with business processes, and challenges in the form of time interval constraints, activity sequences, dummy activities, or invisible tasks that are generally found in event logs. © 2022 Institute of advanced Engineering and Science (IAES).

关键词： auto-generating business process document sources type systematic review

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Boundary Alignment Loss-Guided Cross-Modal Attention Network for Referring Image Segmentation

Boundary Alignment Loss-Guided Cross-Modal Attention Network...

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Machine Learning and Natural language processing (MLNLP), international conference on

作者： Lingyun Zhao Huifang Sun Longfei Li Qihang Sun Wang Nan Bing Gu Shandong University of Science and Technology TaiAn China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

Referring image segmentation is an advanced computer vision task that aims to accurately segment specific objects in computer vision images using algorithms to understand natural language descriptions. Previous methods rely on explicit human commands to recognize target objects or categories, and lack the ability to actively reason the thin object boundaries, degrading segmentation performance. To solve this problem, this paper proposes a boundary alignment loss-guided cross-modal attention network for referring image segmentation, namely BACNet. Technologically, we firstly introduce a cross-modal attention coupling module (CACM) to realize the bidirectional interaction between language and visual features, effectively exploring the fine-grained information across different modalities. then, we design a boundary alignment loss (BAL) to refine segmentation boundaries through a differentiable direction vector prediction within an end-to-end framework, significantly enhancing the accuracy of prediction map in aligning with true object boundaries. Specifically, by guiding the Predicted Boundary Descriptors (PDBs) towards the Ground Truth Boundaries (GTBs), BAL dynamically adapts during training, eliminating the need for explicit boundary detection and progressively improving the model performance. Experiments demonstrate the effectiveness of the proposed method on challenging referring image segmentation datasets, exhibiting higher segmentation quality.

关键词： Couplings Training Image segmentation Computer vision Visualization Accuracy Target recognition Predictive models Vectors Natural language processing

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Global hybrid multi-core-GPUs-resources platform (GHRP) in hard real time system

Global hybrid multi-core-GPUs-resources platform (GHRP) in h...

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international conference on BioMedical Engineering and Informatics (BMEI)

作者： Furkan Rabee Yong Liao Maolin Yang Jian Liu Ge Zhu Computer Science Department University of Kufa Najaf Iraq School of Information and Software Engineering University of Electronic Science & Technology of China (UESTC) Cheng Du China

Graphics processing Units (GPU) technologies have been adopted in many real-time applications, due to the developments in the architectures of GPU fit to be used in general purpose operation. Interesting with that we present a platform for hybrid multi-core-GPUs with global/local shared resources, where this platform divided into three parts; first, cluster the multi-core-GPUs-shared resources, where CPUs and GPUs grouped in one cluster with local shared resources and grouped the global shared resources by unique global cluster where the shared resources governed by global and local semaphore. Second, present an innovative mechanism for scheduling GPUs with different shared resources in multi-core system. third, drive a precise blocking time. the GPU has been tested with many operations by OpenCL language and used the GPU performance with multi-core system depending on local and distributed semaphores evaluations and compare it with MPCP protocol.

关键词： Graphics processing units Protocols Real-time systems Kernel Multicore processing Mathematical model

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Deep Hybrid Model with Trained Weights for Multimodal Sarcasm Detection 7th

Deep Hybrid Model with Trained Weights for Multimodal Sarcas...

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7th international conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： Bavkar, Dnyaneshwar Kashyap, Ramgopal Khairnar, Vaishali Department of Computer Science and Engineering Amity University Chhattisgarh Raipur India Department of Information Technology Terna Engineering College Nerul Navi Mumbai India

ISBN: (纸本)9789819951659

Sarcasm detection is one the most challenging task in natural language processing. though sentiment semantics are necessary to improve sarcasm detection performance, existing DL-based sarcasm detection models do not fully incorporate them. this research suggested the Hybrid RNN and Optimized LSTM for Multimodal Sarcasm Detection (HROMSD) model. the model is processed under the four stages: preprocessing, feature extraction, feature level fusion, and classification. the initial stage of this proposed technique is preprocessing, here input of the multimodal data, which comprises of text, video, and audio are preprocessed. Here, the text will be preprocessed under tokenization and stemming, the video will be preprocessed under face detection and the audio will be preprocessed under filtering technique. then, the stage of feature extraction takes place, where the features from preprocessed text, video, and audio are extracted, here, n-grams, TF-IDF, improved Bag of Visual Words, and emojis are extracted as the text features;then CLM and improved SLBT based video features are extracted from the video features, and chroma, MFCC, jitter and special features are extracted from the audio features. the resultant extracted features set are subjected for feature level fusion stage, which makes use of an improved multilevel CCA fusion technique. the classification is carried out using Hybrid RNN and Optimized LSTM for detection purpose, where Improved BES (IBES) method utilized to increase the detection system’s performance. When compared to earlier research, the proposed work is more accurate. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Word Sense Determination using WordNet and Sense Co-occurrence

Word Sense Determination using WordNet and Sense Co-occurren...

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international conference on advanced information Networking and Applications (AINA)

作者： Che-Yu Yang J.C. Hung Department of Computer Science and Information Engineering Tamkang University Taipei Taiwan Department of Information Management Northern Taiwan Institute of Science and Technology Taipei Taiwan

this paper presents a method of word sense disambiguation that assigns a target word the sense that is most related to the senses of its neighbor words. We explore the use of measures of relatedness between word senses based on a novel hybrid approach. First, we investigate how to "literally" and "regularly" express a ''concept". We apply set algebra to Wordnet's synsets cooperating with Wordnet's word ontology. In this way we establish regular rules for constructing various representations (lexical notations) of a concept using Boolean operators and various word forms in synset(s). then we construct a formal mechanism for quantifying and estimating the semantic relatedness between concepts-we facilitate "concept distribution statistics" to determine the degree of semantic relatedness between two lexically expressed concepts. then we applied the measure of semantic relatedness to the WSD task. the experimental results showed good performance on Semcor, a subset of Brown corpus. We observe that measures of semantic relatedness are useful sources of information for word sense disambiguation

关键词： Natural language processing Artificial intelligence Dictionaries Computer science information management Algebra Ontologies information resources web sites History

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Automatic Generation of CEP Rules using Data Analysis Techniques and Model-Driven Engineering

Automatic Generation of CEP Rules using Data Analysis Techni...

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international conference on Internet of things and Applications (IoT)

作者： Mohsen Gholami Bahman Zamani Behrouz Shahgholi Ghahfarokhi Department of Software Engineering MDSE Research Group University of Isfahan Isfahan Iran Department of Information Technology Engineering University of Isfahan Isfahan Iran

the widespread use of Internet of things (IoT) systems results in a large volume of data that needs to be analyzed in real time for efficient usage. Complex Event processing (CEP) is a technique that enables organizations to analyze data and identify complex and meaningful events. CEP uses patterns written in an Event Pattern language (EPL) to describe an event. However, producing patterns and rules using EPL remains a significant *** research benefits from data analysis and model-driven engineering to automatically produce CEP rules that allow domain experts to extract and define patterns automatically, even if unfamiliar with data analysis or CEP rule design. At the end of the paper, a case problem is solved, resulting in a coverage coefficient of 60%, an average accuracy of 82.5%, and an average precision of 82%. the results show that this combined approach is beneficial in providing a more appropriate response in less time.

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Research and Design of a Medical Dialogue System Based on the Rasa Framework

Research and Design of a Medical Dialogue System Based on th...

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international conference on information Systems and Computer Aided Education (ICISCAE)

作者： Hongyang Li Baolong Yuan School of Automation and Electrical Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

As deep learning, natural language processing, and dialogue systems keep advancing, these technologies have not only revolutionized human-computer interaction but have also found applications in various fields such as customer service, healthcare, and education. this progress enables more natural and effective communication between humans and machines, driving innovation and creating new possibilities for intelligent automation and personalized user experiences, as well as increasing attention to personal and family health, medical dialogue systems have garnered significant attention like never before. In this context, this paper proposes a medical question-and-answer system based on the Rasa framework. this study proposes a medical question-and-answer system utilizing the Rasa framework. the two primary modules of the Rasa development framework are introduced and discussed in detail, Rasa NLU and Rasa Core, and then provides a detailed description of the system design and implementation methods, including an in-depth explanation of the dialogue management module's dialogue strategy. Finally, the paper presents the system's web interface.

关键词： Training Technological innovation Accuracy Intent recognition Systems architecture Oral communication Natural language processing Data models User experience System analysis and design

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Xplore Word Embedding Using CBOW Model and Skip-Gram Model

Xplore Word Embedding Using CBOW Model and Skip-Gram Model

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international conference on Signal processing and Communication (ICSC)

作者： Kailash Choudhary Ruby Beniwal Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9781665427401

Word embedding has gained a lot of attention in recent years. To learn the systematic relation between words from a large junk of unlabeled text data is quite popular these days while using in Machine Translation, Question Answering or even in Text summarization. Computer understand only 0’s and 1’s now can understand text by simply using word embedding and making vectors in corpus i.e. word vector to represent them in numerical form which are used to perform linear algebra on the words which are now in numerical form in vectors. Word embedding is very effective tool in natural language processing (NLP), both CBOW (Continuous bag of words) and SKIP GRAM have increased the efficiency of word embedding along with it both models have reduced the training time. this paper is divided into two parts. In the first part we have discussed what is word embedding along with two models to implement word embedding, these models are continuous bag of word and skip gram. Our objective is to implement these two-model using shallow neural networks. In second part we have compared the efficiency of both the model while using different size of data sets.

关键词： Training Systematics Neural networks Linear algebra Signal processing Knowledge discovery Data models

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Research on data processing and interactive management mode based on web Server technology 22

Research on data processing and interactive management mode ...

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Proceedings of the 7th international conference on Intelligent information processing

作者： Ting Li Wuhan Business University China

ISBN: (纸本)9781450396714

Curriculum is the foothold of the current teaching reform. the establishment of a curriculum teaching platform can make full use of the interdisciplinary teaching resources of professional departments to form a special course group, which can be used as the basic form of general curriculum system construction in applied business colleges. the system adopts the B/S structure to provide functional modules such as course management, teacher management, and student management, which can improve the efficiency of the entire teacher's work and the learning efficiency of students, and has very important practical significance for curriculum reform and management.

关键词： B/S structure

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