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Disruptive Technologies (ICDT), International Conference on

作者： Anup Kumar Srivastava Hoor Fatima Mridul Dharwal Vishal Sarin School of Business Studies Sharda University Knowledge Park III Greater Noida Uttar Pradesh India Computer Science Engineering Sharda University Knowledge Park III Greater Noida Uttar Pradesh India School of Business Studies Sharda University Park III Greater Noida Uttar Pradesh India Department of Economics Mittal School of Business Knowledge Lovely Professional University (LPU) Phagwara Punjab India

The insurance sector is an immense data-driven enterprise with no produced product to develop and market. The data created in such an industry would be financial, risk, customer, producer, and actuarial data. Data acquired by such sectors from prior decades was structured data complemented by information on the goods and the policyholders. However, a vast volume of unstructured/semi-structured data is now available, which is still not investigated. Further to this, the insurer will still be ignorant to utilize the data fruitfully. Healthcare delivery and funding have been obscured throughout the last century by life insurance issues, although there are major similarities between the two. Research finds the optimum places for organizations that require unstructured and structured data for their success. Applied analytics will enhance the usage of insurance sector data. Additionally, insurance-industry big data analytics are examined with adoption methods of big data such as educating, Exploring, Engaging, and Executing. This article addresses the data transformation techniques used in the Insurance Industry and highlights all the models of the data adoption and transformation mechanisms that assist the Insurance Industry to develop better and enhanced data analysis and prediction. Using "Big Data Analytics" necessitates a fundamental rethinking of the current structure of health care services. Aside from examining how this new era of sophisticated and enhanced data management is benefiting the insurance industry, we'll also analyze the different consequences, characteristics, and use cases that lead to new technologies and ultimately contribute to economic success, which we'll cover in this study.

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Bridging the Gap with Sensor Networks and Cloud Computing in the Geospatial Web for Optimized Data Services

Bridging the Gap with Sensor Networks and Cloud Computing in...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Veeresh G N. Sreevani Sorabh Lakhanpal Saurabh Sharma B.Ravali Reddy Taqi Mohammed Khattab Al-Rubaye New Horizon College of Engineering Bangalore India Department of CSE(Cyber Security) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Implementing cloud computing and data networks for local internet services is a priority. This research addresses the issue by introducing a novel approach to enhance the quality of regional internet data services. The proposed technique, Geographical Data Fusion with Dynamic Cloud Resource Allocation (GDF-DCRA), enhances map analytics by combining geographical data fusion with dynamic cloud resource allocation. GDF-DCRA enhances the speed of geospatial analytics by reducing sensor data, efficiently allocating cloud resources, and integrating spatial and temporal data. It consistently outperforms six industry benchmarks in terms of speed, latency, scalability, data accuracy, bandwidth efficiency, fault tolerance, and resource utilisation. Despite the increasing difficulty of working with real-time location data, the proposed approach has the potential to address these challenges.

关键词： Cloud computing Spectral efficiency Soft sensors Web and internet services Data integration Dynamic scheduling Real-time systems Spatial databases Geospatial analysis Resource management

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Bayesian Network Structure Learning and Application

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Mobile Information Systems 2022年 2022卷

作者： Zhang, Liwei Liu, Dayou Wang, Zheng Wang, Xinlu College of Computer Science and Technology Jilin University Jilin130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin130012 China Department of Computer Aviation University of Air Force Jilin130022 China Engineering Training Center Changchun Institute of Engineering Jilin130012 China

With the continuous development of artificial intelligence technology and information technology, a large number of background data are constantly generated. How to obtain effective and useful data in a large and complex data group becomes important and meaningful. The traditional Bayesian network can represent the probability distribution of data variables from a large number of data based on graphical models. It has relatively clear and reliable reasoning ability and decision-making mechanism. However, the traditional Bayesian network structure has serious shortcomings in the recognition accuracy of corresponding key data, so the efficiency of the corresponding algorithm is seriously low. Based on this, this study adds an adaptive genetic algorithm with causality to the original Bayesian structure, so as to optimize the strategy of its structure operation, quantitatively describe the order of the corresponding data nodes, creatively arrange the corresponding data nodes in order by using the node priority, and initialize the initial architecture of Bayesian network based on this. Finally, the network is initialized through information exchange and data score correction, so as to get the final learning results. In this study, the convolution neural network algorithm in a database is verified in the experiment. The experimental results show that the accuracy of the experimental results given by the Bayesian network structure proposed in this study is about 10% higher than the traditional accuracy, and its corresponding learning results basically cover the important algorithms, hypotheses, and verification of convolution neural network, from this level;the algorithm proposed in this study has obvious advantages in bibliometrics. © 2022 Liwei Zhang et al.

关键词： Bayesian networks

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Hierarchical features-based targeted aspect extraction from online reviews

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Intelligent Data Analysis 2021年第1期25卷 205-223页

作者： He, Jin Li, Lei Wang, Yan Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei Anhui China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Department of Computing Macquarie University Sydney Australia MiningLamp Academy of Sciences Mininglamp Techonologies Beijing China

With the prevalence of online review websites, large-scale data promote the necessity of focused analysis. This task aims to capture the information that is highly relevant to a specific aspect. However, the broad scope of the aspects of the various products makes this task overarching but challenging. A commonly used solution is to modify the topic models with additional information to capture the features for a specific aspect (referred to as a targeted aspect). However, the existing topic models, either perform the full analysis to capture features as many as possible or estimate the similarity to capture features as coherent as possible, overlook the fine-grained semantic relations between the features, resulting in the captured features coarse and confusing. In this paper, we propose a novel Hierarchical Features-based Topic Model (HFTM) to extract targeted aspects from online reviews, then to capture the aspect-specific features. Specifically, our model can not only capture the direct features posing target-to-feature semantics but also capture the latent features posing feature-to-feature semantics. The experiments conducted on real-world datasets demonstrate that HFTMl outperforms the state-of-the-art baselines in terms of both aspect extraction and document classification. © 2021 - IOS Press. All rights reserved.

关键词： Aspect extraction Focused analysis Online reviews Text mining Topic modeling

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Meta-path-based outlier detection in heterogeneous information network

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Frontiers of computer science 2020年第2期14卷 388-403页

作者： Lu LIU Shang WANG Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012China College of Software Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China College of Communication Engineering Jilin UniversityChangchun 130012China Department of Computer Science New Jersey Institute of TechnologyUniversity HeightsNewark NJ 07102USA

Mining outliers in heterogeneous networks is crucial to many applications,but challenges *** this paper,we focus on identifying meta-path-based outliers in heterogeneous information network(HIN),and calculate the similarity between different types of *** propose a meta-path-based outlier detection method(MPOutliers)in heterogeneous information network to deal with problems in one go under a unified *** calculates the heterogeneous reachable probability by combining different types of objects and their *** discovers the semantic information among nodes in heterogeneous networks,instead of only considering the network *** also computes the closeness degree between nodes with the same type,which extends the whole heterogeneous ***,each node is assigned with a reliable weighting to measure its authority *** experiments on two real datasets(AMiner and Movies dataset)show that our proposed method is very effective and efficient for outlier detection.

关键词： data mining heterogeneous information network outlier detection short text similarity

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Utilizing Bidirectional Encoder Representations from Transformers for Answer Selection

Utilizing Bidirectional Encoder Representations from Transfo...

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International Conference on Applied Mathematics, Modeling and Computational science, AMMCS 2019

作者： Laskar, Md Tahmid Rahman Hoque, Enamul Xiangji Huang, Jimmy Department of Electrical Engineering and Computer Science York University Toronto Canada School of Information Technology Information Retrieval & Knowledge Management Research Lab York University Toronto Canada School of Information Technology York University Toronto Canada

ISBN: (纸本)9783030635909

Pre-training a transformer-based model for the language modeling task in a large dataset and then fine-tuning it for downstream tasks has been found very useful in recent years. One major advantage of such pre-trained language models is that they can effectively absorb the context of each word in a sentence. However, for tasks such as the answer selection task, the pre-trained language models have not been extensively used yet. To investigate their effectiveness in such tasks, in this paper, we adopt the pre-trained Bidirectional Encoder Representations from Transformer (BERT) language model and fine-tune it on two Question Answering (QA) datasets and three Community Question Answering (CQA) datasets for the answer selection task. We find that fine-tuning the BERT model for the answer selection task is very effective and observe a maximum improvement of 13.1% in the QA datasets and 18.7% in the CQA datasets compared to the previous state-of-the-art. © 2021, Springer Nature Switzerland AG.

关键词： Large dataset

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The Future of Wire Arc Additive Manufacturing Comprehensive Strategies for Improved Close Loop Monitoring and Control

The Future of Wire Arc Additive Manufacturing Comprehensive ...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Ginni Nijhawan G Lalitha V Asha V J Suresh Praveen Zahraa H. Abdulzahraa Lovely Professional University Phagwara Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Dept. of CSE St. Martin’s Engineering College Secunderabad Telangana India Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh College of Health and Medical Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This article describes a series of approaches that improve wire are additive manufacturing (WAAM) for manufacturing many metal items. ATM, MCA 2, ALTC 2, and RSM are the five techniques in the framework. Ablation research examined each approach and how they work together to improve WAAM accuracy, distortion, and material compatibility. The thorough performance comparison, including photographs, indicates that WAAM is better in deposition rate, surface quality, clarity, material diversity, cost-effectiveness, and scalability. The integrated framework enables dynamic modifications, tracking, and iterative improvements. This makes WAAM the optimal additive manufacturing process for many applications. The data indicates how significant each strategy is and how they work together to improve WAAM. This study illuminates sophisticated additive manufacturing technologies and indicates that WAAM is a feasible and flexible alternative to large metal component manufacturing.

关键词： Accuracy Scalability Heuristic algorithms Metals Three-dimensional printing Distortion Wire

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Correction to: Ensemble methods-based comparative study of Landsat 8 operational land imager (OLI) and sentinel 2 multi-spectral images (MSI) for smart farming crop classification (Multimedia Tools and Applications, (2024), 10.1007/s11042-024-19737-0)

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Multimedia Tools and Applications 2024年 1-1页

作者： Gupta, Priyanka Gupta, Prateek Singh, Suraj Kumar Thakur, Bhavna Kumar, Manoj Suresh Gyan Vihar University Jaipur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Centre for Sustainable Development Suresh Gyan Vihar University Jaipur India School of Agriculture & amp Allied Science DBS Global University Dehradun India School of Computer Science FEIS University of Wollongong in Dubai Dubai Knowledge Park Dubai United Arab Emirates MEU Research Unit Middle East University Amman Jordan

In the original publication of this article, all authors have been mistakenly affiliated with "MEU Research Unit, Middle East University, Amman, Jordan." Manoj Kumar is the only author affiliated with this address. The original article has been corrected. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

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Empirical estimation of various data stream mining methods

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International Journal of Computational Intelligence Studies 2021年第1期10卷 13-26页

作者： Srivastava, Ritesh Mittal, Veena Computer Science and Engineering Department Galgotias College of Engineering and Technology Knowledge Park II Uttar Pradesh Greater Noida201310 India Computer Science and Engineering Department Faculty of Engineering and Technology MRIIRS Faridabad India

Online learning is done in order to work on dynamic environments in which the concept tends to change with time and the accuracy of classifiers decreases. The current and previous research is done in static environments, but there is a need of a real time data streaming due to the potentially larger number of applications available in the scientific and business domains. There are several methods used in learning in the presence of dynamic environments like single classifier methods such as batch and incremental learning approaches, classification methods with explicit drift detection method, windowing techniques and ensemble approaches. This paper, investigates these approaches for determining the best suitable method among them. We utilised light emitting diode (LED) data generator for evaluating the performance of the methods. © 2021 Inderscience Enterprises Ltd.

关键词： Mining

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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