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Intelligent Unmanned AI Detection Model for Financial Volatility in Stock Exchange 6

Intelligent Unmanned AI Detection Model for Financial Volati...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Ahamed, Fayaz Biswal, Sthitipragyan Sekhar Nanda, Sudhansu Pundir, Sumit Soubhari, Tushar Boggavarapu, Saikrishna College of Economics & Business Administration Department of Business Administration India ASTHA School of Management Department of Finance Odisha Bhubaneswar India Kirloskar Institute of Management Department of Finance & Accounting KarnatakaYantrapur India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India N.S.S. College University of Calicut Department of Commerce Kerala Manjeri India University of the Cumberlands Department of Information Technology Prospect HeightsIL United States

ISBN: (纸本)9798350304480

At the present days, the popularity of the stock marketing is very high as this is only the legal platform where the investor can doubling their investments within very short span. However, besides the chance of profitability, there is huge risk present due to the extreme volatility. In this context, to reducing the risk of financial this research study has developed a AI detection model so that a predictive future data related to the stock exchange can be obtained through the past data train. In order to predict where prices will go in the future, this model uses machine learning algorithms and NLP techniques to decipher historical market data. Backtesting on simulated data demonstrates that high levels of accuracy, precision, recall, and Fl score may be attained in the prediction of market volatility using the proposed model. the proposed model may be used by investors and financial institutions as a tool for making more informed investment decisions, reducing risk, and identifying untapped investment opportunities. An important contribution to the field of finance, this research shows how machine learning algorithms and natural language processing methods show potential in predicting market volatility. © 2023 IEEE.

关键词： accuracy financial uncertainty Investing investment decisions machine learning algorithms natural language processing methods precision recall the stock market

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Natural language processing and Ontology based Decision Support System for Diabetic Patients 9

Natural language Processing and Ontology based Decision Supp...

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

作者： Chandra, Ritesh Shukla, Abhaya Tiwari, Sadhana Agarwal, Sonali Svafrullah, M. Adiyarta, Krisna Indian Institute of Information Technology Allahabad Information Technology Department Prayagraj India Manipal Institute of Technology Udupi Electronics and Communication Department Manipal India Universitas Budi Program of Master of Computer Science Luhur Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

Diagnosis of diabetes disease is very promising because it may create various other acute or chronic health problems in the human body. this study proposes a recommendation system for diagnosis and treatment of Diabetes Mellitus as suggested by the Clinical Practice Guidelines (CPG). A treatment plan is designed which includes medications and lifestyle changes, according to the patient information, symptoms, and lab tests. Various tasks such as gathering, modelling, and implementing diabetic domain expertise knowledge collected from the experts, CPG, and other sources are used to construct an ontology for diabetes and a decision support system to manage diabetes in its early stages. this work also uses an Optical Character Recognition (OCR) and a Natural language processing (NLP) model that help directly to read doctors prescription and extract useful information useful for developing the ontology. web Ontology language (OWL) is used to build the proposed ontology, and the Semantic web Rule language (SWRL) is used to create the rules for providing restrictions on the ontology. © 2022 Institute of advanced Engineering and Science (IAES).

关键词： Electronic Health Records Natural language processing Optical Character Recognition SWRL

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web Application For Rto Vertification System 6

Web Application For Rto Vertification System

引用

6th IEEE international conference on Signal processing, Computing and Control, ISPCC 2021

作者： Hari Krishnan., S Sathish., G Arish Narayanan., V Freeda, R. Adline KCG College Of Technology Department Of Information Technology Chennai India

ISBN: (纸本)9781665425520

the RTO the board framework is intended to make your enlistment and protection framework more straightforward and quicker. this application covers all registration and insurance systems from the start of the installation period to the results. the first user must complete the registration form and log in to their account. the user then has the option to register for a driver's license, insurance, and other documents. the applicant's certificate guarantees the delivery of all personal information and credentials, driver's license, registration information, and more. they are stored in a database. © 2021 IEEE.

关键词： Degradation Databases Insurance Transportation Authentication Manuals Documentation

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A Review of Empathetic Conversational Systems

A Review of Empathetic Conversational Systems

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6th international conference on Intelligent Computing and Signal processing (ICSP)

作者： Shuang Guo Bo Ning School of Information Science and Technology Dalian Maritime University Dalian China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

With the rapid development of natural language processing (NLP) technology, there has been increasing research on dialogue systems, as traditional dialogue systems have exhibited a series of flaws. As an important branch of dialogue systems, empathetic conversational systems have gained widespread attention from researchers. Research has shown that incorporating emotions and cognitive reasoning into dialogue systems can achieve superior results. In this paper, we introduce empathetic conversational systems from five aspects. Firstly, we discuss the concepts, definition, and development of empathetic conversational systems, and list several common datasets and standards. Additionally, based on an innovative classification of the main structure of empathetic dialogue system models, we summarize the current major methods and model structures. Finally, we discuss the shortcomings of previous work and future directions for empathetic conversational systems.

关键词： Measurement Analytical models Reviews Knowledge based systems Signal processing Market research Cognition Natural language processing Standards

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CAVI: A Contrastive Image Captioning Framework with the Contributions of All Visual information

CAVI: A Contrastive Image Captioning Framework with the Cont...

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international conference on Signal processing Proceedings (ICSP)

作者： Ziwei Shang Gaoyun An Shan Cao Institute of Information Science Beijing Jiaotong University Beijing China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China College of Information Engineering Capital Normal University Beijing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Image captioning is a multimodal task combining computer vision (CV) and natural language processing (NLP). Contrastive language image pre-training has made significant progress by providing effective multimodal representation. In this paper, we propose a contrastive learning-based perceptual image captioning model that utilizes all visual *** make improvements in visual encoder, mainly reflected in first storing the visual information encoded by each layer of encoder, and obtaining aligned visual input by averaging to avoid the loss of key visual *** results show that CAVI improves the ability to describe the relationship between image objects and enhances the quality of generated descriptions. It performs well on the relevant indicators of the Flickr dataset, and also demonstrates certain cross-domain description capabilities on the COCO dataset as a validation set.

关键词： Visualization Computer vision Image databases Computational modeling Contrastive learning Signal processing Natural language processing Decoding web sites Multimedia communication

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PhyloTransformer: A Self-supervised Discriminative Model for SARS-CoV-2 Viral Mutation Prediction Based on a Multi-head Self-attention Mechanism 6

PhyloTransformer: A Self-supervised Discriminative Model for...

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6th international Workshop on Knowledge Discovery from Healthcare Data at 32nd international Joint conference on Artificial Intelligence, KDH@IJCAI 2023

作者： Wu, Yingying Xu, Shusheng Yau, Shing-Tung Wu, Yi Harvard University Center of Mathematical Sciences and Applications United States University of Houston Houston United States Tsinghua University Institute for Interdisciplinary Information Sciences Beijing China Shanghai Qi Zhi Institute Shanghai China

In this article, we developed PhyloTransformer, a Transformer-based self-supervised discriminative model, which can model genetic mutations that may lead to viral reproductive advantage. We trained PhyloTransformer on 1,765,297 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequences to infer fitness advantages, by directly modeling the nucleic acid sequence mutations. PhyloTransformer utilizes advanced techniques from natural language processing to enable efficient and accurate intra-sequence dependency modeling over the entire RNA sequence. We measured the prediction accuracy of novel mutations and novel combinations using our method and baseline models that only take local segments as input. We found that PhyloTransformer outperformed every baseline method with statistical significance. We also predicted the occurrence of mutations in each nucleotide of the receptor binding motif (RBM) and predicted modifications of N-glycosylation sites. We anticipate that the viral mutations predicted by PhyloTransformer may identify potential mutations of threat to guide therapeutics and vaccine design for effective targeting of future SARS-CoV-2 variants. © 2022 Copyright for this paper by its authors.

关键词： Coronavirus

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English to Arabic Braille Neural Machine Translation through Corpus Augmentation 6

English to Arabic Braille Neural Machine Translation Through...

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6th international conference on AI in Computational Linguistics, ACLing 2024

作者： Joshi, Nisheeth Katyayan, Pragya Ahmed, Syed Afroz Speech and Language Processing Lab Center for Artificial Intelligence Vidyapith Rajasthan Banasthali India School of Computer Science University of Petroleum and Energy Studies Utttrakhand India Umm Al Quwain University United Arab Emirates

In this paper we have shown the development of English to Arabic Braille Neural Machine Translation (NMT) System. For our experiments we have developed two NMT systems. the first was the baseline NMT system which was trained only on the English-Arabic parallel corpus whereas the second NMT system has some sub modules for handling syntax transfer of English to Arabic and translation/transliteration of English Named Entities into Arabic equivalents. then the system used this code-mixed text to train the NMT model. the English-Arabic parallel corpus was taken from Opus parallel corpus repository. For preprocessing of the English text Stanford's Stanza NLP Library was used. the trained NMT models were evaluated using standard automatic MT evaluation metrics and their results were also corelated with human judgements. It was found that the corpus augmented NMT system performed better than the baseline NMT system. © 2024 the Authors. Published by Elsevier B.V.

关键词： Neural machine translation

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Research on Multi-objective Optimal Scheduling Model of Industrial Text information Based on Machine Learning

Research on Multi-objective Optimal Scheduling Model of Indu...

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Natural language processing (ICNLP), international conference on

作者： Lili Gao Shuaiyin Ma Jinfeng He Lei Yin Xianguang Kong Jianchao Luo School of Computer Science Xi'an University of Posts and Telecommunications Xi'an China Jingye Iron and Steel Co Shijiazhuang China School of Electro-Mechanical Engineering Xidian University Xi'an China The Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Aiming at flexible flow shop scheduling (FFSP) in industrial production process, this study introduces machine learning technology to build a multi-objective optimization algorithm model based on industrial text information. In order to improve the efficiency of industrial production and reduce energy consumption, an architecture based on Cyber-Physical System is proposed. Industrial text information is analyzed and extracted by natural language processing technology to obtain key information for multi-objective optimization in industrial production process. Within this framework, a problem for solving complex multi-objective optimization challenges in FFSP using Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed. Finally, the validity of our proposed method is verified by using the industrial production process data of Hebei Jingye Group.

关键词： Productivity Energy consumption Machine learning algorithms Job shop scheduling Production Machine learning Optimal scheduling Natural language processing Sorting Genetic algorithms

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Early Malware Characterization based on Online Social Networks

Early Malware Characterization based on Online Social Networ...

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international conference on advanced Communication Technologies and Networking (CommNet)

作者： Alireza Sadighian Ines Abbes Gabriele Oligeri Division of Information and Computing Technology (ICT) College of Science and Engineering (CSE) Hamad Bin Khalifa University (HBKU) Doha Qatar

Online social networks (OSNs) spread information worldwide and in a fast and effective way. Given the terrific number of sources, OSNs can be used for event forecasting and its characterization. Although the vast majority of information is noise, OSNs can be a source of data for the early detection and characterization of malware spreading-this representing a significant advantage for the defense team, which can be informed much in advance of when the malware affects the system. In this paper, we propose an early malware characterization technique that combines statistical analysis with Natural language processing (NLP). Using this approach, we analyze various malware behaviors over time and discover their characteristics, such as target system types, target applications, vulnerabilities, locations, propagation scale, etc., in order to appropriately prevent/detect/mitigate their malicious activities and implement suitable actions effectively. We tested and evaluated our approach on a dataset collected from Twitter that includes widespread ransomware indicators. the results show that our approach is effective in early characterizing various types of malware, thus can be considered as one of the first line of defense.

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Natural language processing (NLP) Techniques: Usability in Human-Computer Interactions

Natural Language Processing (NLP) Techniques: Usability in H...

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Natural language processing (ICNLP), international conference on

作者： Satyesh Das Divyesh Das Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Amethi Uttar Pradesh India Electronics and Computer Engineering Thapar Institute of Engineering and Technology Patiala Punjab India

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

the study sheds light on the concept of Natural language processing (NLP) which enables Human-Computer Interaction (HCI). Recent advances in Artificial Intelligence (AI) and brain architecture have led to significant advancements in NLP. these upgrades have made it possible to use more recent dialect models and computations. Machines are now capable of understanding and producing human language with astounding accuracy and unmatched attentiveness due to the development of deep learning and the availability of large datasets. Evaluating the role of NLP in improving human-computer interaction is the main purpose of this paper. In regard to this, secondary methods have been used for data collection as well as thematic analysis process has been adopted for interpreting data. As results, it has been identified that NLP techniques enable computerised opinion research, supporting organisations in making information-driven decisions and boosting customer loyalty.

关键词： Human computer interaction Deep learning Technological innovation Privacy Force Finance Medical services Natural language processing Usability Artificial intelligence

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