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Special Topic on Reinforcement Learning and Intelligent Decision

ZTE Communications 2023年第3期21卷 1-2页

作者： GAO Yang Department of Computer Science and Technology Nanjing University National Institute of Healthcare Big Data at Nanjing University

Over the past few years,deep reinforcement learning(RL)has made remarkable progress in a range of applications,including Go games,vision-based control,and generative dialogue *** error-and-trial mechanisms,deep RL enables data-driven optimization and sequential decision-making in uncertain *** to traditional programming or heuristic optimization methods,deep RL can elegantly balance exploration and exploitation and handle environmental *** a result,this learning paradigm has attracted increasing attention from both academia and industry and is paving a new path for largescale complex decision-making applications.

关键词： handle optimization programming

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Semantic grounding of LLMs using knowledge graphs for query reformulation in medical information retrieval

Semantic grounding of LLMs using knowledge graphs for query ...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Tommasel, Antonela Assent, Ira Isistan CONICET-UNCPBA Argentina Aarhus University Department of Computer Science Denmark Digit Aarhus University Centre for Digitalisation Big Data and Data Analytics Denmark

ISBN: (纸本)9798350362480

The widespread adoption of electronic health records has generated a vast amount of patient-related data, mostly presented in the form of unstructured text, which could be used for document retrieval. However, querying these texts in full could present challenges due to their unstructured and lengthy nature, as they may contain noise or irrelevant terms that can interfere with the retrieval process. Recently, large language models (LLMs) have revolutionized natural language processing tasks. However, despite their promising capabilities, their use in the medical domain has raised concerns due to their lack of understanding, hallucinations, and reliance on outdated knowledge. To address these concerns, we evaluate a Retrieval Augmented Generation (RAG) approach that integrates medical knowledge graphs with LLMs to support query refinement in medical document retrieval tasks. Our initial findings from experiments using two benchmark TREC datasets demonstrate that knowledge graphs can effectively ground LLMs in the medical domain. © 2024 IEEE.

关键词： Knowledge graph

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A Communication-Aware and Energy-Efficient Genetic Programming Based Method for Dynamic Resource Allocation in Clouds 28th

A Communication-Aware and Energy-Efficient Genetic Programm...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Fang, Zhengxin Ma, Hui Chen, Gang Hartmann, Sven Chen, Shiping Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-Zellerfeld Germany CSIRO Data61 Sydney Australia

ISBN: (纸本)9783031900648

Loosely coupled microservices have emerged as a new paradigm for efficiently deploying applications in clouds. However, dynamic resource allocation in clouds introduces significant challenges to microservice application deployment. On the one hand, frequent invocations between microservices may lead to substantial communication overhead if microservices are not allocated properly. On the other hand, the increasing number of microservices in modern applications makes it very challenging to minimize the energy consumption of a cloud data center as it introduces a bi-level optimization problem with an extremely large search space. In this paper, we propose a new communication-aware and energy-efficient genetic programming based method that automatically learns heuristics for dynamic resource allocation to jointly minimize the communication overhead and the energy consumption. Comprehensive experiments using real-world datasets show that our proposed method can evolve effective heuristics that noticeably outperform existing approaches for dynamic microservice deployment in clouds. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic programming

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A Length-Adaptive Non-Dominated Sorting Genetic Algorithm for Bi-Objective High-Dimensional Feature Selection

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1834-1844页

作者： Yanlu Gong Junhai Zhou Quanwang Wu MengChu Zhou Junhao Wen College of Computer Science Chongqing UniversityChongqing 400044China IEEE Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China

As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected *** computing(EC)is promising for FS owing to its powerful search ***,in traditional EC-based methods,feature subsets are represented via a length-fixed individual *** is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training *** work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional *** LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space ***,a dominance-based local search method is employed for further *** experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.

关键词： Bi-objective optimization feature selection(FS) genetic algorithm high-dimensional data length-adaptive

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A Smart Air Pollutants Monitoring System Using IOT Technologies

A Smart Air Pollutants Monitoring System Using IOT Technolog...

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Pyingkodi, M. Blessing, N R Wilfred Thenmozhi, K. Kanimozhi, K. Pavithira, L. Balakrishnan, P K Sandhya Kongu Engineering College Department of Computer Applications Erode India IT Department Oman Kristu Jayanti College Department of Computer Science Bengaluru India Sri Krishna Adithya College of Arts and Science Department of Information Technology Coimbatore India PSG College of Arts and Science College Department of Computer Science with Data Analytics Coimbatore India CMS College of Science and Commerce Department of Computer Science Coimbatore India

ISBN: (纸本)9798350335835

On international basis, one in all our century's essential problems is air pollutants. Every year, the range of individuals killed via way of means of pollutants climbs, with air pollutants being the main cause. The Internet of Things (IoT) is used worldwide community of 'inventive gadgets' which could sense, and join to, and have interaction with different humans and systems. The solution is furnished via way of means of a sensor-primarily based totally hardware module that can be deployed anywhere. These gadgets, which can be hooked up on mild poles, homes, and different structures, wirelessly speak air pleasant facts to a far flung server. The microprocessor collects facts from IoT sensing gadgets and sends it to a cloud server for processing. These facts can be used to expecting while the vicinity is maximum polluted. The machine additionally gives air pleasant statistics through a cell application, permitting clients to choose alternatives wherein the air pleasant is great. It is likewise a totally less expensive device. © 2023 IEEE.

关键词： Internet of things

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data-Driven Exploration: Analyzing Triage data's Role in Predicting Intensive Care Unit Transfers within 24 Hours 2

Data-Driven Exploration: Analyzing Triage Data's Role in Pre...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Crispin, Andrian Hulu, Victor Zaldi, Muhammad Atqa Adzkia Dharma, Abdi Universitas Sumatera Utara Department of Data Science and Ai Medan Indonesia Universitas Prima Indonesia Department of Public Health Medan Indonesia Universitas Prima Indonesia Department of Computer Science Medan Indonesia

ISBN: (纸本)9798350351613

In the realm of healthcare analytics, the importance of data quality and comprehensiveness cannot be overstated. This research focuses on conducting a detailed exploratory data analysis (EDA) of triage data to support the development of an automated model with the aim of addressing the high false positive rate associated with existing early warning systems in healthcare. Our investigation centers on the triage data of patients admitted to the Emergency department who subsequently require ICU transfers within 24 hours. Our analysis reveals data distribution patterns, such as a normal temperature range of 95 to 102 degrees Fahrenheit, heart rates spanning 50 to 125 beats per minute, and a gradual decline in oxygen saturation levels from 100% to around 90%. These findings enable us to effectively manage hidden outliers and appropriately encode and score the features within the dataset. While our implemented feedforward neural network classifier has made progress in reducing the false positive rate, it maintains a moderate level of accuracy. This indicates that while we have improved the system's performance, there is still room for enhancement. Our ongoing commitment is to continue refining the real-world application of our system, with a primary focus on minimizing false positives and maximizing its impact on real-time patient care. This combination of analytical insights and clinical knowledge holds great potential to enhance patient outcomes and streamline patient management. Ultimately, our research aims to pave the way for a future in which healthcare professionals can more effectively direct their attention to where it is most urgently needed, thereby improving the overall quality of care. © 2024 IEEE.

关键词： data quality

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OIDS: A Smart Framework for Intrusion Detection in 5G WSN's by using Ontology

OIDS: A Smart Framework for Intrusion Detection in 5G WSN's ...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Bisht, Sover Singh Rajput, Anuj Singh Mandal, Sanjeev Kumar Noida Institute of Engineering and Technology Department of Data Science Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

The increasing depth and applications of 5G wireless sensor networks also raise the possibility of network intrusions. In this research, a network intrusion detection system based on the ontology notion is proposed. As it creates ontologies and maintains the relationships among sensor nodes in a network, it is referred upon as a smart system. This proposed system finding is able to secure data in 5G networks. The current detection technique's parameters such as patrol nodes, live nodes, hop, sensor nodes, and energy (in joules) are used to gauge how well the suggested system performs. Attack estimation rate (AER) and precision are the types of metrics in use. © 2023 IEEE.

关键词： Intrusion detection system Ontology patrol nodes and wireless networks Sensor nodes

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An Enchanced Approach for Early Chronic Kidney Disease Prediction using Advanced Machine Learning and data Processing Techniques 8

An Enchanced Approach for Early Chronic Kidney Disease Predi...

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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Poongodi, S. Sasirekha, K. Rini, Sheela Jeyapriya, S. Rashmi, G. Dona Department of Data Science PSGR Krishnammal College for Women Coimbatore India Department of Computer Science(Artificial Intelligence and Data Science) Dr.SNS Rajalakshmi College of Arts and Science Coimbatore India Deparrtment of Computer Science(PG) PSGR Krishnammal College for Women Coimbatore India Department of Computer Science Sri Krishna Arts and Science College Coimbatore India Department of Artificial Intelligence and Machine Learning Kongunadu Arts and Science College Coimbatore India

ISBN: (纸本)9798331512248

Chronic renal disease is a medical disorder in which the kidneys gradually slow down, reducing their ability to filter blood. Diabetes, high blood pressure, heart disease, and a family history of renal failure are the primary risk factors for this condition. The prognosis for patients with chronic kidney disease (CKD) varies according to the stage at which they are diagnosed. Stages 0 and 1 represent the early stages of the disease, whereas stage 4 represents the severe stages, where the kidneys are nearly at the point of failure. Early diagnosis is therefore essential for the patient's survival and the appropriate therapies. Blood tests, biopsies, urine tests, and imaging tests are among the medical diagnostic techniques that can be used to diagnose chronic kidney disease (CKD). However, these medical diagnostic techniques have certain drawbacks and restrictions. They are employed to identify diseases in their later stages and can occasionally yield erroneous results;they are also expensive. Consequently, the researcher has discovered a novel technique for the early identification of CKD that use machine learning algorithms to overcome these complications because machine learning algorithms are becoming increasingly significant in healthcare datasets. These methods also have issues with overfitting, class imbalance, biased results, problems with dataset interpretability, and high computing costs. Therefore, improved preprocessing techniques and machine learning algorithms are used in this research work to overcome the inadequacies of the existing approaches for CKD prediction. The model is tested using the Kaggle dataset on chronic renal disease. Class imbalance and missing attribute values are issues with this dataset. Additionally, this dataset has several categorical attribute values and additional variances in attribute values. A few preprocessing techniques are used to improve the data quality, including K-Nearest Neighbour for filling in missing attribute val

关键词： Chronic Kidney Disease ELM K-NN PSO SMOTE

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(Information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Analysis of Trie-Based Lan File Search Engine

Analysis of Trie-Based Lan File Search Engine

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Shanmugasundaram, C. Umamaheswari, C. Suriyaa, V. Tr, Kartikeyan Manakula Vinayagar Institute of Technology Department of Electrical and Electronics Engineering India St. Thomas College of Arts and Science Department of Computer Science with Data Science India Anna University Department of Computer Technology Chennai India

ISBN: (纸本)9798350315127

Searching and Accessing files in a computer device is one of the most important and basic task performed by the computer today. Each Operating System has a dedicated search bar for this, But however, In a network encompassing a multitude of systems, retrieving a specific file from various systems can pose a formidable task. Put differently, users frequently find it challenging to recollect the precise location where they've stored a particular file. This undertaking offers a user-friendly application interface wherein the user inputs the file's name, subsequently receiving both the precise file path and the IP address of the computer device within the network as output. We can access the file by clicking on the path generated. Accessing Files from multiple systems in a network is quite difficult and a Slow process and there are not many Applications which serve this purpose. We aim to overcome this problem by providing an efficient Trie data Structure based Searching Algorithm to search and access files in a network from any node in the Local Area Network. © 2023 IEEE.

关键词： Local area networks

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