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共 558 条记录，以下是201-210 订阅

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Analysis of Hainan Tourism Carbon Balance Information System Based on Big data 3rd

Analysis of Hainan Tourism Carbon Balance Information System...

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3rd international conference on Advanced intelligent Technologies, ICAIT 2022

作者： Wang, Lina Zhang, Qiuyue Cai, Guoqin Wang, Yitong Pan, Yongqiang School of Economy and Management Hainan Normal University Hainan Haikou571158 China

ISBN: (纸本)9789819952021

In the context of big data, this paper applies advanced intelligent technologies such as cloud computing and big data and green computing, based on the time series data related to tourism in Hainan Province 2010–2020, a carbon emission information system measurement model of tourism and tourism ecosystem carbon absorption information system is constructed, and the carbon balance information system of tourism in Hainan Province is preliminarily calculated to clarify the dilemma of carbon emission reduction in tourism, and explore the low-carbon development mode of tourism. The results show that: (1) from 2010 to 2020, the carbon emissions of tourism in Hainan Province increased from 3.02972 million tons to 4.61407 million tons, of which tourism transportation, warehousing, and post and telecommunications are the main sources of carbon emissions information system. (2) From 2010 to 2020, the total carbon absorption of the tourism ecosystem in Hainan Province increased from 2.78928 million tons to 3.52312 million tons, of which the tourism waters and tourism forest ecosystems are the main carbon sinks, and the capacity of tourism farmland and grassland ecosystems is relatively weak. (3) The estimated annual carbon deficit of the tourism industry is 693600 tons. It presents a serious carbon imbalance;Hainan tourism industry has become a significant carbon source, and energy conservation, emission reduction, and ecological exchange rate increase will be an effective way for future development. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Carbon

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Energy Management, Design Constraints and Solution for data Dissemination in Wireless Sensor Networks 3

Energy Management, Design Constraints and Solution for Data ...

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3rd international conference on intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Agbulu, G. Pius Kumar, G. Joselin Retna Gunasekar, S. Srm Institute of Science and Technology Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai603203 India Srm Institute of Science and Technology Tamil Nadu Chennai603203 India Sree Vidyanikethan Engineering College AndhraPradesh Sree Sainath Nagar Tirupathi 517102 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Wireless sensor network (WSN) technology has become an essential constituent of our lives in health care, homes, food, and vehicles. Nonetheless, the sensors in WSNs naturally have limited device lifecycle due to serious energy-constraints, and so cannot guarantee to satisfy the QoS (Quality of Service) demands of different monitoring applications. This presents a significant setback to the effective application of WSNs in real-word. Therefore, to enhance the lifecycles of WSNs, it is fundamental to improve the energy management at the nodes as they are mostly set-out in geographically isolated zones. To achieve this, several communications mechanisms for energy-management and reliable routing operations is required. In this paper, we examine energy-management and the reliable routing mechanisms of WSNs with specific emphases on mobile and static deployments. We also consider fundamental design challenges and mechanisms to deal with routing challenges posed by innate constraints of the node. We assess various recent routing schemes for energy management in WSNs and propose an application-specific time-driven distributed routing scheme named Wi-Fi-based multi-hop routing algorithm (WMHR). We show the performance of the proposed WMHR in terms of energy -saving in comparison with other classical time-driven routing models. © 2022 IEEE.

关键词： Energy management

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Deep Learning for Personalized Treatment Strategies in Colorectal Tumor Patients

Deep Learning for Personalized Treatment Strategies in Color...

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advances in data Engineering and intelligent computing systems (ADICS), international conference on

作者： G. Vinudevi S.P. Vijayaragavan M. Anand K. Subbulakshmi Department of Electronics and Communication Engineering Bharath Institute of Higher Education and Research Selaiyur Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Bharath Institute of Higher Education and Research Selaiyur Chennai Tamil Nadu India Department of Electronics and Communication Engineering Dr.M.G.r. Educational & Research Institute Chennai Tamil Nadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

This research presents a novel deep learning model that effectively customizes treatment regimens for patients diagnosed with colorectal cancer, yielding exceptional results. The model's 97% success rate in predicting treatment reactions on the “The Cancer Genome Atlas (TCGA)” dataset provides evidence of its effectiveness. The model demonstrates its capacity to reduce false positives and negatives through its recall of 0.86 and accuracy of 0.91. The model exhibited exceptional discriminatory capability, as evidenced by its AUC-ROC value of 0.89. A balanced measure of accuracy and recall, the F1-score of 0.88, further suggests that the model successfully attained consistent performance across all treatment categories. The results of this research illustrate the immense potential of deep learning to transform the field of colorectal cancer treatment through the provision of tailored recommendations. This research makes a valuable contribution to the field of precision medicine by illuminating the transformative potential of data-driven models in enhancing patient outcomes and quality of life.

关键词： Deep learning Precision medicine Genomics Transforms Predictive models data engineering Bioinformatics

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Research on Industrial Internet data Security Technology based on Formal Verification of SM4 2

Research on Industrial Internet Data Security Technology bas...

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2nd Asia conference on Electrical, Power and Computer Engineering, EPCE 2023

作者： Chen, Zhenya Deng, Yushen Yang, Ming Mu, Chao Xu, Shuo Wu, Fazong Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Sanke Intelligent Technology Co. LTD Jinan China

ISBN: (纸本)9798350310818

Industrial Internet promotes the transformation of industrial platform from business system driven to data driven through the flow of industrial data between massive multi-source equipment and heterogeneous systems. The data flow of industrial Internet involves the interactive transmission of a large number of key sensitive information between enterprises. Coupled with its inherent openness and heterogeneity, it is facing serious security challenges such as sensitive data leakage and integrity destruction. Cryptography technology is one of the important methods to ensure data security. Among them, the national cryptographic algorithm can meet the requirements of security, autonomy and controllability of Chinese industrial Internet data, and avoid the risk of "backdoor"when the current mainstream international cryptography algorithm protects the key sensitive data. However, there are still security threats such as memory leakage and time-side channel attack which cause the invalidity of encryption mechanism when the national cryptographic algorithm is deployed in industrial Internet system. Formal verification method is an important method to verify the security and reliability of cryptographic algorithms. Existing researches have designed a variety of formal verification mechanisms for the underlying cryptographic algorithms of some structures and protocols, but the research on formal verification for national cryptographic algorithm is still in a blank stage. This paper proposes a formal verification method of SM4 cryptographic algorithm based on programming framework F∗, which can ensure the memory security and resist the time-side channel attacks during the implementation of the algorithm. And the method can reduce the security risks, when the national cryptographic algorithm is deployed in the industrial internet. © 2023 IEEE.

关键词： Sensitive data

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Modeling of An Feedback System for Interpretation of Emotion Using AI 2

Modeling of An Feedback System for Interpretation of Emotion...

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2nd IEEE international conference on Advanced Technologies in intelligent Control, Environment, computing and Communication Engineering, ICATIECE 2022

作者： Shah, Pradeep Kumar Manjunath, C.R. Teerthanker Mahaveer University Department of Computing Science and IT Moradabad India Jain University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665493963

Studies in mental neuroscience and brain research are gradually demonstrating how feelings play a crucial role in thought processes. This information is gradually being used to the reproduction and mental cycle demonstrations in the Counterfeit Canny and Fake Life areas. However, there aren't many comparisons between projects and very little research is done on possible components of sensation that might be used in computational systems initiatives. It is crucial for system improvement to comprehend the emotions underlying these supplied opinions at a finer granularity. Such crucial information cannot be fully ascertained through AI-based big data feeling analysis;as a result, text-based emotion identification incorporating AI in gaming big data has emerged as an urgent topic of normal language processing study. [1] The subjective audit takes into account various inclination models, datasets, calculations, and application fields of text-based feeling discovery, despite the fact that the examination work in this sector is ongoing. Additionally, SA aids in comprehending genuine comments made on a variety of platforms, like Amazon, Excursion Counsel, and others. This thorough review's primary goals were to summarise key findings from earlier studies and to provide an overview of SA models in the context of AI-driven SA. Additionally, this study provides an overview of SAs that are lexicon-and ontology-based as well as AI models that are used to analyze the ambience of a given environment. © 2022 IEEE.

关键词： Cell proliferation

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English-Chinese Translation Online Teaching System Based on data Mining Algorithm 2

English-Chinese Translation Online Teaching System Based on ...

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2nd IEEE international conference on Advanced Technologies in intelligent Control, Environment, computing and Communication Engineering, ICATIECE 2022

作者： Li, Chenxi Jilin University of Finance and Economics Department of Public Foreign Language Teaching and Research Changchun Jilin China International School of Chinese Studies Northeast Normal University Changchun Jilin China

ISBN: (纸本)9781665493963

With the rapid development of modern communication technology, mobile phones, as an indispensable communication tool for people, record the trajectory of people's activities, not only in the real space, but also in the network space. The analysis and mining of all kinds of information data has been very. data mining, which plays a crucial role in many scenarios, can help us discover a lot of valuable knowledge and anomaly patterns. The cultivation of translation ability is the core goal of current translation teaching, and the research on the composition of translation ability and the methods to improve translation ability is increasingly becoming the focus of translation teaching research. The development of translation ability is a dynamic interactive process, not a linear process guided by gradual input, but a dyna mic systematic behavior full of peaks, valleys, advances, reversals, stagnation, and even leaps forward, since it is composed of multiple sub-Abilities and the multi-sub-Abilities do not develop synchronously. The characteristics of dynamic assessment coincide with the dynamic change and complexity of the development of translation ability. In this paper, data mining algorithms are used to study the existing English-Chinese translation online systems. © 2022 IEEE.

关键词： data mining

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intelligent Decision Making in Autonomous Vehicles using Cognition Aided Reinforcement Learning

Intelligent Decision Making in Autonomous Vehicles using Cog...

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IEEE Wireless Communications and Networking conference (IEEE WCNC)

作者： Rathore, Heena Bhadauria, Vikram Univ Texas San Antonio Dept Comp Sci San Antonio TX 78285 USA Texas A&M Univ Texarkana Dept Comp Sci Math MIS Texarkana TX USA

ISBN: (数字)9781665442664

ISBN: (纸本)9781665442664

As recent advances in sensing, computing, and communications expedite proliferation of autonomous vehicles (AV), their sharing the road with human driven vehicles presents a challenge that demands urgent investigation. AVs can excel at deterministic programmed behavior, still human drivers have the edge because of the faculty of cognition, which evolved over millennia. This paper presents Cognition Aided Reinforcement Learning (CARL) algorithm that harnesses inputs from five principles of cognition - memory, attention, language, perception, and intelligence. Sensors build perception, data facilitate memory, and safety messages enable language support. Intelligence fuses information with attention focused on specific actions for reward maximization. Simulation results show CARL to be 10 times faster as compared to the state of the art model-free reinforcement learning algorithms. Additionally, by using the principle of metacognition (art of learning how to learn), CARL achieves optimal rewards in a heterogeneous environment composed of vehicles with varying degrees of autonomy.

关键词： autonomous vehicles cognition meta-cognition reinforcement learning

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(NMRNN-LSTM) - Novel Modified RNN with Long and Short-Term Memory Unit in Healthcare and Big data Applications

(NMRNN-LSTM) - Novel Modified RNN with Long and Short-Term M...

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2022 international conference on Advancements in Smart, Secure and intelligent computing, ASSIC 2022

作者： Deepa, N. Prabakeran, S. Devi, T. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Department of Networking and Communications Chengalpattu District Kattankulathur India

ISBN: (纸本)9781665461092

In the modern world, people's expectations and needs are automatically supportive and easy to use such as voice messages, playing music, or movies automatically which may reduce the manual operations mostly. In past decades technological advances such as machine learning and its application over many data like structured and unstructured are very much tedious. Whereas the operations based on non-categorical data, and categorical data are working rapidly using Natural Language Processing (NLP) comparatively, existing ones were not very productive. Each process on the internet is carrying an enormous amount of information which can lag in storage as well as performance. When any CRUD operations such as create, modify, update and delete are being analyzed one at a time, complex data such as unstructured and structured data are used in any field. In such a way the location analysis, social media data, health organization information, etc are categorized in natural language processing (NLP). The proposed work is organized as i) managing the huge amount of data in healthcare and log files created due to electronic health record management(EHR), ii) Unstructured data that are generated from all electronic equipment such as monitoring heartbeat, brain waves, etc that can be interpreted to classify using machine learning algorithms. To overcome the complications and medical records access inefficiency due to the complex structure of the dataset, Natural language processing uses the recurrent neural network along with the novel modified long and short-term memory unit (NMRNN-LSTM). Using the big data types such as structured, unstructured, and reinforcement kind of databases which handle images such as CTs, X-rays, MRI, raw texts, video streaming medical history to have effective systems and clinical records for enhancing the technological Medical care. © 2022 IEEE.

关键词： Records management

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Reconfigurable intelligent Surface-Based Cooperative Spectrum Sensing Over Noisy Reporting Channel

Reconfigurable Intelligent Surface-Based Cooperative Spectru...

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international conference on advances in data Science and computing Technologies, ASDC 2022

作者： Sharma, Girraj Vyas, Sandeep Nayyar, Anand Sharma, Ritu Jaipur Engineering College and Research Center Jaipur India Graduate School Faculty of Information Technology Duy Tan University Da Nang Viet Nam Malaviya National Institute of Technology Jaipur India

ISBN: (纸本)9789819936557

The term "cognitive radio (CR)" refers to revolutionary technique in which a transceiver can detect the empty radio spectrums. The transceiver then switches to a vacant channel, avoiding the ones that are currently in use. These can help to make the radio frequency (RF) spectrum more efficient. The reconfigurable intelligent surface (RIS) can regulate electromagnetic wave propagation by varying the surface's electric and magnetic characteristics. These surfaces can be used to change the properties of radio channels in a certain situation. This gives wireless systems new ways to improve their performance. The goal of this research is to investigate the notion of combining the two ideas inside a network in order to maximize the anticipated advantages based on both RIS and CR. The study looks at a variety of performance indicators for a cognitive radio network (CRN) based on cooperative spectrum sensing (CSS) and a RIS. The results show the influence of essential parameters and a significant increase in the sensing performance of a CR network using RIS technique. There is an increment of 33.3% in detection probability when RIS is employed to a non-RIS CR network. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cognitive radio

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FORTUNATE: Decrypting and Classifying Malware by Variable Length Instruction Sequences 13

FORTUNATE: Decrypting and Classifying Malware by Variable Le...

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13th IEEE international conference on Cloud Networking, CloudNet 2024

作者： Andrade, Cesar A. B. Rocha Filho, Geraldo P. Meneguette, Rodolfo I. Maranhao, Joao Paulo A. Sant'ana, Ricardo Duarte, Julio Cesar Serrano, Andre Luiz M. Goncalves, Vinicius P. University of Brasília Electrical Engineering Dept. Brasília Brazil State University of Southwest Bahia Exact and Technological Sciences Dept. Vitória da Conquista Brazil Institute of Mathematics and Computer Science Universityof São Paulo São Carlos Brazil Brazilian Army Systems Development Center Brasília Brazil Military Institute of Engineering Defense Engineering Dept. Rio de Janeiro Brazil Military Institute of Engineering Computing Engineering Dept. Rio de Janeiro Brazil University of Brasília Production Engineering Dept. Brasília Brazil

ISBN: (纸本)9798350376562

After identifying an artifact as malware, the additional challenge arises of correctly classifying it into a specific family. This raises the challenge of classifying malware amid growing complexity and the increasing volume of cyber threats. Therefore, we introduce FORTUNATE, a framework that employs variable-length instruction sequences to classify malware more efficiently and accurately, focusing on real and active malware. By introducing effective methods for extracting opcodes and their representation in the smallest possible vectors, FORTUNATE surpasses the limitations of previous approaches, enabling the correct identification of malware in their respective families, while avoiding redundancies in the data. The results achieved with FORTUNATE demonstrate advances in both precision and computational efficiency, consolidating it as a significant contribution to the field of cybersecurity. © 2024 IEEE.

关键词： Long short-term memory

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