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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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Interactive Blood Bank Repository: Django AWS Approach 5

Interactive Blood Bank Repository: Django AWS Approach

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sujatha, E. Manickavasagan, V. Lakshminarayanan, S. Ani Minisha, R. Rekha, K.S. Loganathan, V. Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu India Department of Computer Science and Business Systems Sri Sairam Engineering College Tamil Nadu India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu India Department of Information Technology Saveetha Engineering College Tamil Nadu India Department of Artificial Intelligence and Data Science Vel Tech Multi Tech Dr Rr and Dr Sr Engineering College Tamil Nadu India

ISBN: (纸本)9798350385649

Everyday, there are a growing number of people who require blood transfusions and there is no substitute for human blood. The secure blood donors are voluntary non-remunerated blood donors from risk populations. Donors in India and across the globe are three types namely, Voluntary donors, Professional donors and replacement donors. The deadline-driven web framework that supports Django's clean, practical design and speedy development. The existing system has a manual work restriction. This has restrictions on securely controlling the task for timely results declaration, and has a significant resource consumption. Web-based blood donation is the coordination of the supply and demand of blood through an online portal. The main goal of this research study is to give blood donors and blood searchers an online platform and to develop an engaging method of connecting the two groups. The suggested system provides a great deal of information regarding blood types, blood supply, and blood donation specifics to raise awareness among the public. Additionally, other system features like password-based security protection, report generation, chat functionality, and stock shortages can even improve the management tasks by streamlining them. © 2024 IEEE.

关键词： Economics

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Machine learning-enabled MIMO-FBMC communication channel parameter estimation in IIoT: A distributed CS approach

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Digital Communications and Networks 2023年第2期9卷 306-312页

作者： Han Wang Fida Hussain Memon Xianpeng Wang Xingwang Li Ning Zhao Kapal Dev College of Physical Science&Engineering Yichun UniversityYichun336000China State Key Laboratoty of Marine Resource Utilization in South China Sea Hainan UniversityHaikou570228China Faculty of Data Science City University of MacaoMacao999078China Physics and Electronic Information Engineering Henan Polytechnic UniversityJiaozuo454000China Department of Mechatronics AMM Lab Jeju National UniversitySouth Korea Department of Electrical Engineering Sukkur IBA UniversityPakistan Department of Institute of Intellligent Systems University of JohannesburgSouth Africa

Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning *** mainly emphasizes the sparsity of the *** this paper,we study an ML-based CS Channel Estimation(CE)method for wireless communications,which plays an important role in Industrial Internet of Things(IIoT)*** the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation(MIMO-FBMC/OQAM)systems,a Distributed Compressed Sensing(DCS)-based CE approach is studied.A distributed sparse adaptive weak selection threshold method is proposed for ***,the correlation between MIMO channels is utilized to represent a joint sparse model,and CE is transformed into a joint sparse signal reconstruction ***,the number of correlation atoms for inner product operation is optimized by weak selection threshold,and sparse signal reconstruction is realized by sparse *** experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit(OMP)method and other traditional DCS methods in the time-frequency dual selective channels.

关键词： IIoT Machine learning Distributed compressed sensing MIMO-FBMC Channel estimation

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Geospatial Analysis of Extreme Temperature Impacts in Agricultural systems Using Machine Learning 10

Geospatial Analysis of Extreme Temperature Impacts in Agricu...

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10th IEEE International Symposium on systems engineering, ISSE 2024

作者： Wheeler, Rayshaun L. Miller, John S. Smith, Gia N. Lambert, James H. Pennetti, Cody A. Louis, Garrick E. Johnson, Deandre A. University of Virginia Department of Civil & Environmental Engineering CharlottesvilleVA United States University of Virginia School of Data Science CharlottesvilleVA United States University of Virginia Department of Systems & Information Engineering CharlottesvilleVA United States University of Virginia Department of Engineering & Society CharlottesvilleVA United States

ISBN: (纸本)9798350353723

Recent higher temperatures, observed compared to previous years, have led to more frequent and intense heat waves, adversely affecting agriculture and human health. This paper explores using Inverse Distance Weighting (IDW) and Kriging to develop GIS layers for a heat-based risk index. Recent increases in global mean temperatures have led to more frequent and intense heat waves, impacting food supply, vegetation, and human health. Optimal plant growth temperatures in temperate climates range from 20°C to 30°C, with nighttime temperatures also being critical. Exceeding these temperature ranges can reduce plant immunity, cause disease, and result in poor crop yields. For example, mango cultivars need a "safe"temperature range of 10°C to 12°C to avoid damage. The intensification of higher temperatures established July 2023 record the hottest days on Earth, highlighting the need for effective monitoring and management tools. IDW, a deterministic method, offers a straightforward and efficient approach by assuming closer points have more influence. Kriging, a probabilistic geostatistical technique, accounts for spatial autocorrelation and prediction uncertainty, making it suitable for complex datasets. The study compares these methods' performance in creating continuous surfaces representing heat-based risk. IDW is simple and efficient but less accurate in sparsely populated areas, while Kriging is more accurate and detailed but requires more computational resources and expertise. An automated workflow for data preprocessing, model training, and cross-validation ensures consistent results. The study recommends adopting IDW for initial assessments and transitioning to Kriging as expertise grows. Enhanced data collection on temperature and population density is also advised to improve GIS layer accuracy, aiding better decision-making and resource allocation to mitigate extreme heat effects on agriculture and public health. © 2024 IEEE.

关键词： Anomaly detection Climate change Complex systems Risk management System identification

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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Image-based Cutting Tool Health Diagnosis in Multi-machine Settings 6

Image-based Cutting Tool Health Diagnosis in Multi-machine S...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Mishra, Debasish Pattipati, Krishna R. Bollas, George M. Data Science and Information Systems Krea University 5655 Central Expressway Sri City517646 India Electrical and Computer Engineering University of Connecticut 371 Fairfield Way Storrs06269 United States Chemical and Biomolecular Engineering University of Connecticut 159 Discovery Drive Storrs06269 United States

Manufacturers are increasingly focused on evaluating the condition of cutting tools during machining processes to ensure high-quality production. This study introduces a method for accurately and cost-effectively diagnosing the health of cutting tools. The method employs image similarity indices-such as Bhattacharyya distance, Fourier transform, and Cosine similarity-to compute indicators of tool health from wavelet coefficient maps of cutting signals. These indicators are then used in an unsupervised machine learning (ML) algorithm, agglomerative clustering, to categorize the tool health state. To validate the method, run-to-failure tests of the tool were conducted on two different machines with varying specifications and machining parameters. The results demonstrate a strong association between the computed indicators and the tool health, regardless of the differences between the two machines and their cutting settings. This is due to the reliance of the method on the physics of machining processes to derive the health indicators. The ML model effectively categorizes the progressive health of cutting tools and provides easily interpretable results on the health of the tool due to the explainability of the indicators. © 2025 The Authors. Published by Elsevier B.V.

关键词： Clustering Machine Learning Precision machining Similarity Tool condition monitoring Unsupervised

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Scavenger: Better Space-Time Trade-Offs for Key-Value Separated LSM-trees 40

Scavenger: Better Space-Time Trade-Offs for Key-Value Separa...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhang, Jianshun Wang, Fang Qiu, Sheng Wang, Yi Ou, Jiaxin Huang, Junxun Li, Baoquan Fang, Peng Feng, Dan School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China China Shenzhen Huazhong University of Science and Technology Research Institute China Bytefrance Inc.

ISBN: (纸本)9798350317152

Key-Value Stores (KVS) implemented with log-structured merge-tree (LSM-tree) have gained widespread ac-ceptance in storage systems. Nonetheless, a significant challenge arises in the form of high write amplification due to the compaction process. While KV-separated LSM-trees successfully tackle this issue, they also bring about substantial space am-plification problems, a concern that cannot be overlooked in cost-sensitive scenarios. Garbage collection (GC) holds significant promise for space amplification reduction, yet existing GC strategies often fall short in optimization performance, lacking thorough consideration of workload characteristics. Additionally, current KV-separated LSM-trees also ignore the adverse effect of the space amplification in the index LSM-tree. In this paper, we systematically analyze the sources of space amplification of KV-separated LSM-trees and introduce Scavenger, which achieves a better trade-off between performance and space amplification. Scavenger initially proposes an I/O-efficient garbage collection scheme to reduce I/O overhead and incorporates a space-aware compaction strategy based on compensated size to minimize the space amplification of index LSM-trees. Extensive experiments show that Scavenger significantly improves write performance and achieves lower space amplification than other KV-separated LSM-trees (including BlobDB, Titan, and TerarkDB). © 2024 IEEE.

关键词： Economic and social effects

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DeepCensored: Deep-Learning Based Probabilistic Forecasting Framework for Censored data

DeepCensored: Deep-Learning Based Probabilistic Forecasting ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Jiahao Tian Michael D. Porter School of Data Science University of Virginia Charlottesville VA USA Dept. of Systems and Information Engineering School of Data Science University of Virginia Charlottesville VA USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Accurate time series forecasting is critical in various fields, including resource allocation and crime prevention. While traditional approaches often focus on continuous data, count data forecasting, especially with partially observed (censored) data, remains challenging. This paper introduces DeepCensored, a novel deep learning-based framework that combines the Expectation-Maximization (EM) algorithm with deep neural networks to deliver robust probabilistic forecasts. DeepCensored naturally handles interval-censored event data, where exact event times are unknown but fall within a specific interval. Through extensive simulations and real-world crime data analysis, our method significantly outperforms traditional forecasting approaches, reducing the Mean Absolute Error (MAE) by 50% and better detection of emerging crime trends. These results highlight DeepCensored's potential to provide actionable insights for law enforcement and public safety by predicting crime intensity and enabling resource-efficient policing strategies.

关键词： Time series analysis Predictive models Probabilistic logic Market research data models Time measurement Public security Resource management Forecasting Synthetic data

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Successful Integration of IoT and Blockchain Technologies Using Several Machine Learning Algorithms 5

Successful Integration of IoT and Blockchain Technologies Us...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Calarany, C. Indumathy, M. Senthilraja, P. Suganya, D. Gopi Vardhan, V. Rahul, Yelisetty Shanmukh Tagore Government Arts and Science College Department of Computer Science Puducherry India Rajiv Gandhi College of Engineering & Technology Department of Information Technology Kirumampakkam India K.S.Rangasamy College of Technology Department of Computer Science Tamil Nadu India Rajiv Gandhi College of Engineering & Technology Department of Computer Science Kirumampakkam India Data Science and Business Systems Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350383522

Internet of Things (IoT) produces massive amounts of data that need to be processed and saved securely. The strong features of Blockchain makes it as a best candidate for storing the data received from IoT sensors. However, there is a need of concern to take care of the challenges associated with both IoT and blockchain paradigms. Firstly, the enormous amount of data should be effortlessly handled by Blockchain network, without adding much complexity. Secondly, the heterogeneous nature of data that are received from various IoT Sensors should be stored within the blockchain. In this paper, a conceptual framework based on Multimodal Machine Learning (MML) algorithm is proposed as an interface between the IoT sensors and Blockchain networks. The MML algorithm is used to understand the different modalities of the data and provides a way to manage the heterogeneity in that data. The MML algorithms provides an intelligent data processing that allows for real-time data storage in the blockchain networks. The conceptual frame assumes that audio, video, and text are the three types of data as received from IoT. These inputs are processed in simplistic Recurrent neural network RNN that acts as MML and then passed to Blockchain networks for secure storage. Simulation of the above said conceptual framework is performed assuming all other relevant requirements of IoT, MML and Blockchain are taken into account with default parameters and values. The results obtained provide a promising note for future scope in this direction. © 2024 IEEE.

关键词： IoT MML RNN

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Reactions’Descriptors Selection and Yield Estimation Using Metaheuristic Algorithms and Voting Ensemble

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Computers, Materials & Continua 2022年第3期70卷 4745-4762页

作者： Olutomilayo Olayemi Petinrin Faisal Saeed Xiangtao Li Fahad Ghabban Ka-Chun Wong Department of Computer Science City University of Hong KongKowloon TongHong Kong SAR Information Systems Department College of Computer Science and EngineeringTaibah UniversityTaybaMedina42353Saudi Arabia Hong Kong Institute for Data Science City University of Hong KongKowloon TongHong Kong SAR

Bioactive compounds in plants,which can be synthesized using N-arylationmethods such as the Buchwald-Hartwig reaction,are essential in drug discovery for their pharmacological *** descriptors are necessary for the estimation of yields in these *** study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for *** algorithms were evaluated based on computational time and the number of selected *** show that robust performance is obtained with more descriptors,compared to cases where fewer descriptors are *** essential descriptor was deduced based on the frequency of occurrence within the 50 extracted data subsets,and better performance was achieved with the voting ensemble than other algorithms with RMSE of 6.4270 and R^(2) of *** results and deductions from this study can be readily applied in the decision-making process of chemical synthesis by saving the computational cost associated with initial descriptor selection for yield *** ensemble model has also shown robust performance in its yield estimation ability and efficiency.

关键词： Buchwald-Hartwig reaction descriptor selection machine learning metaheuristic algorithm palladium-catalyzed cross-coupling reaction voting ensemble

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