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Chaotic Association Feature Extraction of Big Data Clustering Based on the Internet of Things

Informatica (Slovenia) 2022年第3期46卷 333-342页

作者： Liu, Xiaoming Singh, Thipendra Pal Gupta, Rajeev Kumar Onyema, Edeh Michael JingZhou Vocational College of Technology Software Engineering Institute Hubei Jingzhou434000 China School of Computer Science University of Petroleum and Energy Studies Dehradun India Pandit Deendayal Energy University Gandhinagar India Department of Mathematics and Computer Science Coal City University Enugu Nigeria

This article addresses the stabilization of chaotic characteristics in abnormal data by proposing chaotic correlation feature extraction of big data clustering based on the Internet of things. The chaotic features in big data usually show complex folding and distortion without obvious rules and order and non-synchronization. In this article, the dimension of extracted correlation is utilized as the chaotic feature for the clustering of big data. The one-dimensional time series that can be extended in multi-dimensional space is analysed based on phase space reconstruction, to extract the chaotic correlation dimension (CCD) features. After the relevant experimental analysis, this paper mainly compares the energy consumption and processing time of the two respective algorithms. In the simulation parameter design, the time interval of big data packet generation is 0.1s, and the data is generated from the simulation time of 300s. The results obtained show that when dealing with the same amount of data, the energy consumption of this algorithm is significantly lower than that of the traditional algorithm. When dealing with the same amount of data, the time required by this algorithm is significantly lower than that of the traditional algorithm. This is because this algorithm is easy to implement and has good clustering efficiency for data, so the clustering time is short. With the gradual increase in the amount of data, the correlation dimension of this algorithm tends to be stable. While the correlation dimension of the traditional algorithm fluctuates greatly, it is revealed that the proposed approach has high data clustering efficiency and verifies the effectiveness of this algorithm. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Feature extraction

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Analysis of Non-Fungible Token Pricing Factors with Machine Learning

Analysis of Non-Fungible Token Pricing Factors with Machine ...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Ho, Kin-Hon Hou, Yun Chan, Tse-Tin Pan, Haoyuan The Hang Seng University of Hong Kong Department of Computing Hong Kong Shenzhen University College of Computer Science and Software Engineering China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Rarity is known to be a factor in the price of non-fungible tokens (NFTs). Most investors make their purchasing decisions based on the rarity score or rarity rank of NFTs. However, not all rare NFTs are associated with a higher price, especially for play-to-earn gaming NFTs. In this paper, we studied the top-ranked play-to-earn gaming NFTs on Axie Infinity. We found that, in addition to rarity, utility is also a significant factor influencing the price. Furthermore, we use utility as a predictor to predict the price of Axies using the XGBoost regressor. Our results reveal that, compared to using rarity-based predictors only, leveraging utility-based predictors can improve the prediction accuracy, thus highlighting utility as a price determinant for play-to-earn gaming NFTs. © 2022 IEEE.

关键词： Machine learning

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A Complementary Fused Method Using Gru and Xgboost Models for Long-Term Solar Energy Hourly Forecasting

SSRN

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SSRN 2024年

作者： Xu, Yaojian Zheng, Shaifeng Zhu, Qingling Wong, Ka-Chun Wang, Xu Lin, Qiuzhen College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong

Solar photovoltaic (PV) energy plays a crucial role in global renewable energy generation and has received extensive attention from the academic and industrial communities in recent decades. However, the inherent volatility and stochastic nature of solar energy generation present challenges for the integration of solar energy with smart grids. Accurate and reliable long-term forecasting of solar energy is the key to improving energy scheduling, planning, and intelligent decision-making. In this paper, we propose a complementary fusion of GRU and XGBoost models to improve the performance of long-term solar energy hourly forecasting. The proposed method includes data preprocessing, feature engineering, training of GRU and XGBoost models, and fusion of the forecasting results. Specifically, based on historical solar energy, solar irradiance and numerical weather prediction (NWP) data (e.g. temperature, wind direction, wind speed, etc.) are used as input data to forecast the hourly solar energy for the next ten days. In the UNiLAB Algorithm Competition Track 3: Forecasting of Generated Renewable Energy, held by Tianchi in 2022, our proposed approach won the first rank in the rematch among 513 participating teams, thereby confirming its dominance in the competition. The experimental results showed that the proposed method achieved highly competitive performance in terms of forecasting accuracy, which outperformed six state-of-the-art long-time series forecasting models DLinear using four different forecasting horizons from both the competition dataset and the GEFCom2014 dataset. © 2024, The Authors. All rights reserved.

关键词： Solar energy

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Smart Energy Management System Using Machine Learning

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computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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Optimized Flame Detection Using a YOLOv5 Neural Network

Optimized Flame Detection Using a YOLOv5 Neural Network

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2024 International Conference on Computing, Robotics and System sciences, ICRSS 2024

作者： Karim, Shahid Ibrar, Muhammad Bilal, Anas Qadir, Akeel Bibi, Irfana School of Information Engineering Xi'an Eurasia University Xi'an710065 China Software College Shenyang Normal University Shenyang China College of Information Science and Technology Hainan Normal University Haikou571158 China Faculty of Computing and Information Technology University of Punjab Department of Computer Science Punjab Lahore Pakistan

ISBN: (纸本)9798331511296

With the rising prevalence of smart homes, there's an increasing demand for comprehensive automation solutions to mitigate fire risks, especially when homeowners are absent or in homes with elderly residents. This study focuses on enhancing flame detection techniques, particularly for smaller, hard-to-detect fire sources. A customized Yolov5 neural network was developed to improve detection precision. This enhancement was facilitated using a dataset of over 2,000 images from lab camera observations and online platforms. These images were then manually labeled and clustered for anchor dimensions using the K-means++ algorithm. The CIOU and diou - nms techniques were integrated to optimize the network's performance. Upon training, the refined model was tested on the Jet Nano, revealing its high efficacy in flame detection. © 2024 IEEE.

关键词： Smart homes

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Optimizing MEMS Optical Arrays with Silicon Micro Mirrors using Machine Learning Techniques 3

Optimizing MEMS Optical Arrays with Silicon Micro Mirrors us...

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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Joshi, Vilas Ramrao Sharma, Surender Kumar Galande, Vijay Agatrao Sachin Patil, Pallavi Patil, Ushadevi Suttarwala, Hussain Sharma, Deepak K. Savitribai Phule Pune University Department of Computer Engineering Pune India Poornima University Department of Electrical Engineering Jaipur India KJEI's Trinity Academy of Engineering Department of Engineering Science Pune India Genba Sopanrao Moze College of Engineering Department of Civil Engineering Pune India American Express Senior Software Engineer PhoenixAZ85054 United States University of Technology Jaipur India

ISBN: (纸本)9798350361391

In the rapidly evolving landscape of optical microsystems, the infusion of Machine Learning (ML) techniques stands out as a catalyst for unprecedented progress. This research paper delves into the intricate interplay between Micro-Electro-Mechanical Systems (MEMS), optical arrays, and silicon micro mirrors, shedding light on the transformative prowess unleashed by Machine Learning in optimizing these intricate components. Focusing on the limitations posed by conventional designs, the paper not only illuminates the challenges encountered but also presents a compelling narrative of how ML algorithms emerge as formidable allies, poised to surmount these hurdles with ingenuity and precision. The exploration extends beyond a mere technical discourse;it unfolds as a narrative of innovation, where the convergence of MEMS, optical arrays, and silicon micro mirrors, guided by the intelligence of Machine Learning, transcends traditional boundaries, propelling optical microsystems into an era of unparalleled efficiency and adaptability. This research beckons a new epoch, where the intricate dance of silicon micro mirrors and the intricate mechanics of MEMS find harmony through the symphony of Machine Learning, reshaping the landscape of optical microsystems and inspiring future possibilities that were once deemed beyond reach. © 2024 IEEE.

关键词： Microchannels

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Geometric Spanning Trees Minimizing the Wiener Index 1

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18th International Symposium on Algorithms and Data Structures, WADS 2023

作者： Abu-Affash, A. Karim Carmi, Paz Luwisch, Ori Mitchell, Joseph S. B. Software Engineering Department Shamoon College of Engineering Beer-Sheva84100 Israel Department of Computer Science Ben-Gurion University Beer-Sheva84105 Israel Department of Applied Mathematics and Statistics Stony Brook University Stony BrookNY United States

ISBN: (数字)9783031389061

ISBN: (纸本)9783031389054

The Wiener index of a network, introduced by the chemist Harry Wiener [30], is the sum of distances between all pairs of nodes in the network. This index, originally used in chemical graph representations of the non-hydrogen atoms of a molecule, is considered to be a fundamental and useful network descriptor. We study the problem of constructing geometric networks on point sets in Euclidean space that minimize the Wiener index: given a set P of n points in Rd, the goal is to construct a network, spanning P and satisfying certain constraints, that minimizes the Wiener index among the allowable class of spanning networks. In this work, we focus mainly on spanning networks that are trees and we focus on problems in the plane (d= 2 ). We show that any spanning tree that minimizes the Wiener index has non-crossing edges in the plane. Then, we use this fact to devise an O(n4) -time algorithm that constructs a spanning tree of minimum Wiener index for points in convex position. We also prove that the problem of computing a spanning tree on P whose Wiener index is at most W, while having total (Euclidean) weight at most B, is NP-hard. Computing a tree that minimizes the Wiener index has been studied in the area of communication networks, where it is known as the optimum communication spanning tree problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Atoms

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Unmanned aerial vehicles advances in object detection and communication security review

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Cognitive Robotics 2024年第1期4卷 128-141页

作者： Laghari, Asif Ali Jumani, Awais Khan Laghari, Rashid Ali Li, Hang Karim, Shahid Khan, Abudllah Ayub Software College Shenyang Normal University Shenyang China School of Electronic and Information Engineering South China University of Technology Guangdong Guangzhou China Department of Computer Science ILMA University Karachi Sindh Pakistan Department of Mechanical Engineering Technology Sindh Institute of Management and Technology Sindh Karachi Pakistan Research and development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China Benazir Bhutto Shaheed University Liyari Pakistan

Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years, with a wide range of applications in areas such as surveying, delivery, and security. UAV technology plays an important role in human life. Integrating Artificial Intelligence (AI) techniques into UAVs can significantly enhance their capabilities and performance. After the integration of AI in UAVs, their efficiency can be improved. It can automatically detect any object and highlight those objects with detailed information using AI. In most of the security surveillance places, UAV technology is beneficial. In this paper, we comprehensively reviewed the most widely used UAV communication protocols, including Wi-Fi, Zigbee, and Long-Range Wi-Fi (LoRaWAN). The review further explores valuable insights into the strengths and weaknesses of these protocols and how cognitive abilities such as perceptions and decision-making can be incorporated into UAV systems for autonomy. This paper provides a comprehensive overview of the state-of-the-art UAV object detection in remote sensing environments, as well as its types and use cases in different applications. It highlights the potential applications of these techniques in various domains, such as wildlife monitoring, search and rescue operations, and surveillance. The challenges and limitations of these methods and open research issues are given for future research. © 2024

关键词： Object detection

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TFBSnet: A deep learning-based tool for predicting transcription factor binding site from DNA sequences

TFBSnet: A deep learning-based tool for predicting transcrip...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Du, Zhihua Huang, Tianyou Li, Jianqiang Uversky, Vladimir N. Shenzhen University College of Computer Science and Software Engineering Shenzhen China University of South Florida Morsani College of Medicine Department of Molecular Medicine Usf Health Byrd Alzheimer's Research Institute FL United States

ISBN: (纸本)9798350337488

Transcription factors (TFs) are crucial proteins that regulate gene transcription by binding to specific sites on DNA, known as transcription factor binding sites (TFBSs). Identifying TFBSs enables the design of drugs to modulate gene expression, making it important for drug design and gene therapy. While deep learning-based methods have been proposed for predicting TFBSs, there is room for improvement. This study introduces TFBSnet, a novel deep learning-based technique that accurately predicts TFBSs by extracting diverse feature data from DNA sequences and utilizing a convolutional neural network (CNN) combined with SKNet. Experimental results show that TFBSnet outperforms existing methods. It also demonstrates accurate prediction of TF binding sites in human cells without label data and exceptional performance in predicting TFBSs in plant cells using 265 TFs in Arabidopsis. Ablation analysis highlights the integration of different features and advanced feature extraction by SKNet as contributors to TFBSnet's superior predictive capability. © 2023 IEEE.

关键词： deep learning DNA sequence neural network transcription factor binding site

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An Improved Marine Predators Algorithm Based on Group Learning 19

An Improved Marine Predators Algorithm Based on Group Learni...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Li, Liying Zhao, Jian Wang, Jiacun Guo, Xiwang School of Science University of Science and Technogly Liaoning Anshan China Monmouth University Department of Computer Science and Software Engineering United States Computer and Communication Engineering College Liaoning Petrochemical University Monmouth University Fushun China

ISBN: (纸本)9781665472432

To address the shortcomings of the marine predators algorithm (MPA) in solving complex problems, such as low optimization accuracy and easily falling into local optimization, this paper proposes an improved marine predators algorithm based on group learning (GLMPA). An opposition-based learning method is adopted to enhance the quality of the initial solutions. Then, a group learning strategy is used to diversify the population. Two subgroups are produced by fitness evaluation and employing different updating mechanisms. In addition, a new position-updating rule is used to help the proposed algorithm escape from the local optima in the later stage of iteration. Finally, six test functions are utilized to test the GLMPA, and the simulation results verify the effectiveness of the proposed algorithm when compared with other famous algorithms. © 2022 IEEE.

关键词： Iterative methods

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