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A Data Commercialization Evaluation Model Based on Fuzzy Multi-attribute Group Decision Making 23

A Data Commercialization Evaluation Model Based on Fuzzy Mul...

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Proceedings of the 2023 4th International Conference on Big Data Economy and information Management

作者： Bin Zhao Li Cao Yilong Gao Peng Zhai School of Information Science and Engineering Linyi University China School of Mathematics and Computer Application Technology Jining University China

ISBN: (纸本)9798400716669

The core purpose of data commoditization transformation is to lower the threshold of data utilization and facilitate data analysis and mining at the user end. In this paper, we propose a data commoditization degree evaluation model for data trading websites. First, the evaluation index system of data commercialization is constructed, combining the abstract indexes reflecting data commonality and industry-specific data commercialization standards to assess the degree of data commercialization; second, the fuzzy multi-attribute group decision-making method is utilized to conduct fuzzy evaluation of the indexes in response to the issue of user evaluation preference; and the fuzzy function corresponding to the fuzzy evaluation is calculated based on the fuzzy structured element afterwards. The degree of superiority of the program is calculated by fuzzy compromise group decision-making method. Finally, the simplicity, convenience, and effectiveness of the data commoditization assessment model are demonstrated through a speech data case study.

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Internet of Things application: E-health data acquisition system and Smart agriculture 10

Internet of Things Application: E-health data acquisition sy...

引用

10th International Conference on Emerging Trends in Engineering and technology - Signal and information Processing, ICETET-SIP-22 2022

作者： Mishra, Adarsh Ravi Vishwakarma, Nitin Kumar Shukla, Ragini Mishra, Ravi Computer Application and Information Technology Dr. C.V. Raman University Bilaspur India GHRIET Electronics and Communication Department Nagpur India

ISBN: (纸本)9781665467414

IoT has emerged as one of the most advanced techniques of the 21 century in recent years. With the help of low cost computation, big data, cloud technology, embedded system and mobile technology, physical objects are able to communicate and collect information with minimal human interaction. This major motive of the paper is to demonstrate how the IoT is adaptable to variety of fields, for instance healthcare, agriculture, manufacturing, logistics, and transportation. For smart healthcare application, this paper discusses an e-health data acquisition, transmission, and monitoring system for cardiac patient in which the health parameters of patients are monitored by a wireless sensor network and relayed to the far end through GPRS. For smart agriculture application this paper discusses an autonomous irrigation system, in which a sensor analyses soil humidity and waters the plant accordingly, saving time and water and requiring less human work. © 2022 IEEE.

关键词： Internet of things

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AMPpred-CNN: Prediction of Antimicrobial Peptide by Using 1D Convolution Neural Network and Composition/Transition/Distribution (CTD) Encoding 7th

AMPpred-CNN: Prediction of Antimicrobial Peptide by Using ...

引用

7th International Conference on Soft Computing: Theories and applications, SoCTA 2023

作者： Mitra, Uddalak Singh, Amit Kumar Department of Computer Application Siliguri Institute of Technology Hill Cart Road Salbari West Bengal Sukna734009 India Amity Institute of Information Technology Amity University Rajasthan SP-1 Kant Kalwar RIICO Industrial Area NH-11C Rajasthan Jaipur300202 India

ISBN: (纸本)9789819720880

Antimicrobial resistance has become a global health crisis due to antibiotic misuse. Antimicrobial peptides (AMPs) show promise with broad pathogenic activity. However, experimental AMP discovery is expensive, and computational methods face challenges with data availability and resistance patterns. We introduce a method AmpPred-CNN based on 1D convolutional neural network and peptide encoding for their computational identification. Validated on a diverse dataset of 3268 AMPs and 166,791 non-AMPs, our model achieved impressive accuracy of 96%. Comparative evaluations confirmed its superiority. Notably, even with just the top 10 features, an accuracy of 94% is obtained. Additionally, AMP’s antimicrobial activities were found independent of sequence length. These findings underscore the significant improvement in AMP prediction accuracy through effective peptide encoding techniques and diverse training datasets, offering hope in combating antimicrobial resistance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Temporal-Aware Multi-behavior Contrastive Recommendation 28th

Temporal-Aware Multi-behavior Contrastive Recommendation

引用

28th International Conference on Database Systems for Advanced applications, DASFAA 2023

作者： Xuan, Hongrui Li, Bohan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Nanjing China National Engineering Laboratory for Integrated Aero-Space-GroundOcean Big Data Application Technology Nanjing China

ISBN: (纸本)9783031306716

Modeling various types of users’ interactions and jointly considering individual preferences from multiple perspectives, multi-behavior recommendation has attracted increasing attention recently. However, most existing multi-behavior recommendations only focus on the behavioral interaction itself, attempting to extract user preferences merely by modeling behaviors, while ignoring the properties of the interaction (e.g., the temporal information). Meanwhile, the introduction of multi-behavior information is bound to generate additional noise signals, which further affects the accuracy of recommendation. Thus, to tackle the above challenges, two aspects need to be explored: (1) How to fuse interaction attributes to construct unified cross-behavior dependencies while distinguishing subtle differences between individuals’ behaviors. (2) How to mitigate the effects of noise to a certain extent while preserving multi-behavioral information. In this work, we propose a Temporal-Aware Multi-Behavior Contrastive Learning (TMCL) framework to explore the patterns of multiple behaviors of individuals through temporal information, jointly capture the correlation of users’ preference evolution, and alleviate the impact of multi-behavior noise. Extensive experiments and ablation tests on the three datasets indicate our TMCL outperforms various state-of-the-art methods and verify the effectiveness of our model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： User profile

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Research on elephant flow detection method based on information entropy and improved random forest

Research on elephant flow detection method based on informat...

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2022 International Conference on Algorithms, Microchips and Network applications

作者： Hu, Xiaohong Miao, Xianghua Yuan, Meiyu Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9781510653290

In the increasingly complex network environment, various attacks emerge one after another. Being able to accurately detect elephant flow and mouse flow plays an extremely important role in defending against large-scale network attacks. Aiming at some shortcomings of current methods for detecting elephant flow and mouse flow, this paper proposes a detection method based on information entropy and improved random forest. After preprocessing the data, first calculate the data feature score with information entropy, screen out the truly valuable features according to the score, then put them into the improved random forest classifier, and finally get the detection results. In this paper, the grid search algorithm is used to optimize the tree and depth of random forest tree, so that the detection results can be obtained quickly and accurately. Experiments show that due to the significant difference between the data characteristics of elephant flow and mouse flow, this method can effectively identify elephant flow and mouse flow. The accuracy rate is 97.93%, the precision rate is 99.99%, the recall rate is 97.91%, and the F1-score is 98.94%, which is improved compared with other algorithms. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Complex networks

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Internet of Things for Healthcare: Research Challenges and Future Prospects 1st

Internet of Things for Healthcare: Research Challenges and F...

引用

1st International Conference on Advanced Communication and Computational technology, ICACCT 2019

作者： Verma, Garima Prakash, Shiva Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur India

ISBN: (纸本)9789811553400

In current scenario, there is a need of such a structure with related devices, individuals, time, spots, and frameworks, which is completely participated in what is called as Internet of things (IoT). This IoT combines the whole world with the PC-based system and around the end, it results in a greater efficiency, accuracy, and advantage for the customer. Security and assurance are the basic two factors for monitoring the health of any patient. A system is required for the blend of confirmation show with an essentialness capable access control instrument. IoT has emerged as one of the most trending domain in the present scenario. Health care is one of the leading research areas where people are moving to provide better solutions efficiently. This paper demonstrates a comprehensive and comparative study portraying the related work done by the current writers dependent on the wireless body zone systems (WBANs). Through this paper, different correlations between a several parameters and strategies of using various IoT devices related with healthcare frameworks are being furnished. Various research challenges of IoT healthcare domain have been also discussed. The comparative study presented here is based on various parameters like techniques used in healthcare frameworks, data collection techniques, etc. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Emerging Security Threats, Countermeasures, Issues, and Future Aspects on the Internet of Things (IoT): A Systematic Literature Review 1

引用

2nd International Conference on Future Learning Aspects of Mechanical Engineering, FLAME 2020

作者： Verma, Garima Prakash, Shiva Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur India

ISBN: (数字)9789811599569

ISBN: (纸本)9789811599552

IoT consolidates the entire world with the PC-based framework and around the end;it brings about a more prominent way of communication for the clients. Therefore, Security of IoT devices is also a major concern which helps in protecting IoT-enabled devices for the communication purpose. With the extent of the IoT framework, major security issues of various heterogeneous devices in any sensor network are being increasing day by day. Through this paper, we explore various IoT security concerns at various layers. Because of the investigation, we will feature the most basic issues with the point of controlling future research directions. Also, we have discussed a detailed literature survey along with various threats and security aspects on each layer of IoT architecture. A comparative study has also been discussed describing various parameters of IoT protocols based on features and limitations in IoT Security illustrated by various researchers. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： CIA model Cyberthreat Internet of Things (IoT) IoT communication protocols IoT security countermeasures

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Prediction of GDP Carbon Emission Based on Grey Model and Neural Network 4th

Prediction of GDP Carbon Emission Based on Grey Model and Ne...

引用

4th EAI International Conference on Robotic Sensor Networks, 2020

作者： Zhang, Feng Zhao, Huihuang Ramasamy, Manimaran College of Computer Science and Technology Hengyang Normal University Hengyang China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang China

ISBN: (纸本)9783030704506

In order to improve the prediction accuracy of Gross Domestic Product (GDP) carbon emission, combining the advantages of grey prediction and neural network, a discrete grey neural network prediction model named DGMBP is established. This model not only has the advantages of less data sample and larger development coefficient range of GM 1,1 model but also combines the advantages of neural network to deal with nonlinear data sample. Taking the GDP carbon emission data of China, India, the United States and the European Union as an example, the prediction results obtained are stable, and the accuracy of the prediction data is over 95%, which is much higher than that obtained by the linear regression equation and the logical regression equation. Finally, the carbon emission of GDP in 2020–2030 is predicted by using this model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Revolutionizing Plant Disease Management Through Image Processing technology

Revolutionizing Plant Disease Management Through Image Proce...

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2023 International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Kumar, Gaurav Kumar, Ankit Singhal, Manoj Singh, Kamred Udham Kumar, Lalan Singh, Teekam Gla University Department of Computer Engineering & Applications UP Mathura India Gl Bajaj Institute of Technology and Management Department of Information Technology India School of Computing Graphic Era Hill University Dehradun India Gl Bajaj Institute of Technology and Management Department of Master of Computer Application India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Agricultural productivity plays a vital role in the economy, and disease detection in plants is crucial for the agriculture sector as plant diseases are widespread. Failure to take appropriate care can lead to serious consequences for plant quality, quantity, or productivity. For instance, a disease called little leaf disease has been found in pine trees in the United States, which can severely affect plant health. Automated methods for detecting plant diseases are useful as they reduce the need for manual inspection in large crop fields, and they can identify disease symptoms as soon as they appear on plant leaves. This paper presents an algorithm for image segmentation for the automatic identification and classification of plant leaf diseases. It also discusses various disease classification methods that can be used for plant leaf disease detection. Genetic algorithms are used for image segmentation, which is a crucial aspect of disease detection in plant leaves. The paper focuses on identifying the Powdery Mildew disease in organic grape products, which can cause significant damage if not detected in time. The accurate identification and classification of diseases from leaf images are crucial for preventing significant losses in output and agricultural inputs. The proposed process can help improve the accuracy and efficiency of disease detection in plants and lead to better agricultural productivity. © 2023 IEEE.

关键词： Fuzzy logic

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Improving Search Engine Results Using AI Methods 3

Improving Search Engine Results Using AI Methods

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Katariya, Jitendra Kumar Geetha, B. Mahajan, Rupali A. Singh, Manpreet Yadav, Rakesh Kumar Vivek, V. Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

This paper examines how artificial Intelligence (AI) algorithms can be used to improve seek engine consequences. It outlines key terms, demanding situations, and opportunities related to AI -based totally search engine optimization. initial studies shows that AI-based totally tactics display promising ability to increase accuracy and relevancy in search engine consequences. AI strategies such as natural language processing (NLP), system getting to know (ML), and sentiment analysis are explored in element. additionally, the paper compares current answers that rent AI algorithms to conventional search engines and evaluates their performance, scalability, and applicability. ultimately, the paper discusses the destiny outlook of AI in seo and its potential implications for web users, corporations, and seeks engine developers. © 2023 IEEE.

关键词： algorithms developers engine potential strategies

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