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Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

computers, Materials & Continua 2022年第11期73卷 3351-3365页

作者： R.Rajender C.S.S.Anupama G.Jose Moses E.Laxmi Lydia Seifedine Kadry Sangsoon Lim Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Engineering Sungkyul UniversityAnyang14097Korea

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile *** encompasses several heterogeneous resource and communication standard in ensuring incessant availability of *** the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,*** UAV networks,energy efficiency and data collection are considered the major process for high quality network *** these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted *** issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G *** this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G *** proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets *** presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct *** QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of *** performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

关键词： 6G unmanned aerial vehicles resource allocation energy efficiency artificial intelligence clustering data collection

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Mathematical Model and AI Integration for COVID-19: Improving Forecasting and Policy-Making

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SN computer science 2024年第2期5卷 1-19页

作者： Panda, Dev Sourav Dixit, Rahul Dixit, Anuja Dwaracherla, Harshitha Shukla, Anupam Bosch Digital Department Bosch Global Software Technologies Private Limited Karnataka Bangalore India Department of Artificial Intelligence Sardar Vallabhbhai National Institute of Technology Gujarat Surat India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj India Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Pune India Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat India

In this work, a new susceptible–exposed–infectious–recovered (SEIR) compartmental model is proposed which has additional media influence for precise quantization of the coronavirus disease 2019 (COVID-19). In the proposed model, first-order ordinary differential equations (ODEs) are used for the formulation of basic reproduction number, whereas genetic algorithm (GA) is used for its estimation. The inclusion of climatic parameters, governmental impact, and human behavioral response toward the disease provides an upper hand in determining the dynamics of its transmissibility, thereby indicating their significance in precising the outcomes. In addition, the future trends for the new normalized confirmed cases of COVID-19 are predicted using the long short-term memory (LSTM) model which helps in evaluating and modifying the current preventive actions taken to improve the situation. The robustness of the proposed model is measured by five different error functions which are tested in five different countries. According to the experimental results, this is observed that the proposed model has a smaller prediction deviation as well and the proposed scheme outperforms state-of-the-art models of COVID-19. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithm LSTM model Reproduction number SEIR-type compartmental model Social media Transmission dynamics

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Rice Disease Recognition using Improved Visual Geometry Group Deep Learning Algorithms 23

Rice Disease Recognition using Improved Visual Geometry Grou...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Lee, Shu-Hung Cheng, Chia-Hsin Tsai, Yu-Shun Huang, Yung-Fa Liu, Shing-Hong Chen, Ching-Mu School of Intelligent Manufacturing and Automotive Engineering Guangdong Business and Technology University Zhaoqing526238 China National Formosa University Department of Electrical Engineering Yunlin Taiwan Chaoyang University of Technology Department of Information and Communication Engineering Taichung Taiwan Chaoyang University of Technology Department of Computer Science and Information Engineering Taichung Taiwan National Penghu University of Science and Technology Department of Electrical Engineering Penghu Taiwan

ISBN: (纸本)9798331528041

Rice diseases are one of the major factors affecting rice production. Traditionally, the identification and assessment of rice diseases have been done manually by experts and farmers, which is time-consuming and cannot provide real-time disease prediction, leading to significant losses. Therefore, there is a need for faster and more efficient methods for rice disease recognition. In this paper, we propose a solution for rice disease detection using artificial intelligence in image recognition. We investigate the performance of the visual geometry group (VGG) deep learning algorithms in recognizing rice diseases. Simulation results show that the VGG16 and VGG19 pretrained models achieve validation accuracies of 67% and 72.5%, respectively. © 2024 IEEE.

关键词： Artificial Intelligence Rice Diseases Visual Geometry Group

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A Study of Jamming Technique for Countering LFM Radar 9

A Study of Jamming Technique for Countering LFM Radar

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Li, Yuan Pang, Zhaobin College of Information and Electronic Engineering Shandong Technology and Business University Yantai China School of Computer Science Xi'an Shiyou University Xi'an China

ISBN: (纸本)9798350376548

In order to counter linear frequency modulation (LFM) radar, a novel jamming method based on sinusoidal phase modulation is proposed from the perspective of the range profile of the target. In this paper, firstly, the jamming method based on sinusoidal phase modulation is designed. Then, the frequency domain characteristics of the jamming signal are analyzed in detail. It shows that the range profile of the jamming signal is similar to that of the real target, making it difficult for the enemy to distinguish between true and false targets. Based on real radar echo data from a real aircraft, the simulation experiments have been done to justify the validity and efficiency of the new jamming technique at last. Compared to radio frequency (RF) noise interference, the novel technique can achieve effective jamming at lower power. © 2024 IEEE.

关键词： Frequency domain analysis

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Visualization of Covid 19 pattern using machine learning

Visualization of Covid 19 pattern using machine learning

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Thanjaivadivel, M. Pious, Ignatious K. Jagannathang, J. School of Computing and Information Technology Reva University Bengaluru India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India School of Information Technology and Engineering Vellore Institute of Technology Vellore Dt Tamil Nadu Katpadi India

ISBN: (纸本)9781032669663

The Wuhan market in China is the source of a terrible and undetectable threat to the entire planet. The time between the outbreak and the epidemic was barely a few months. Visual data analysis makes it easier to get information from numerous data sources in a better method because it helps to understand how the world combats this disease. In this essay, we’ll demonstrate how one might visualise a map of their own nation, along with the regions where this dreadful sickness has spread. This essay discusses the COVID-19 epidemic’s data identification and analysis. In a smart, smart, and global nation, data recognition aids in the integration of all data sources, including the number of instances discovered, deaths, and current cases. Data is disseminated by putting it into visual aids like graphs and maps. This offers current and trustworthy information that enables us to monitor the number of persons admitted to hospitals in every region of India as well as in other nations around the world. This clarifies how you can handle the threat that the Coronavirus poses. In this essay, we’ll show how our theory can be used to analyse and forecast the spread of COVID-19. © 2024, CRC Press/Balkema. All rights reserved.

关键词： COVID-19

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Bi-Static Inverse Synthetic Aperture Radar Imaging Based on 5G NR Signal 23

Bi-Static Inverse Synthetic Aperture Radar Imaging Based on ...

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23rd International Symposium on Electrical Apparatus and Technologies, SIELA 2024

作者： Lazarov, Andon Kostadinov, Todor Nikola Vaptzarov Naval Academy Information Technology Department Varna Bulgaria Burgas Free University Computer Science and Engineering Faculty Burgas Bulgaria

ISBN: (纸本)9798350382365

This study addresses Bi-static Inverse Synthetic Aperture Radar (BISAR) concept with a 5G NR waveform. BISAR scenario with uncooperative transmitter and specially designed receiver, and flying target as helicopter is discussed. The positions of the transmitter, receiver and flying target are defined by vector kinematic equations. Mathematical models of 5G NR waveform and BISAR signal reflected from a target with complex geometry are described. Two dimensional (2-D) image reconstruction is realized by applying correlation with an emitted waveform or Fourier transform on a range direction, and Fourier transform on an azimuth direction. Simulation experimental results are provided to illustrate the correctness of mathematical models and imaging algorithms. © 2024 IEEE.

关键词： Inverse synthetic aperture radar

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Research on Semantic Communication Based on Joint Control Mechanism of Shallow and Deep Neural Network

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Chinese Journal of Electronics 2025年第2期34卷 698-711页

作者： Xie Wenwu Xiong Ming Ren Ziqing Wang Ji Yang Zhihe School of Information Science and Engineering Hunan Institute of Science and Technology Yueyang 414006 China School of Computer Science Hefei Normal University Hefei 230001 China College of Physical Science and Technology Central China Normal University Wuhan 430079 China

With the rapid development of deep learning, various semantic communication models are emerging, but the current semantic communication models still have much room for improvement in the coding layer. For this reason, a joint-residual neural networks (Joint-ResNets) framework based on the joint control of shallow neural networks (SNNs) and deep neural networks (DNNs) is proposed to cope with the problems in semantic communication coding. The framework synergizes SNNs and DNNs based on their shared utility, and uses variable weight \begin{document}$\alpha$\end{document} term to control the ratio of SNNs and DNNs to fully utilize the simplicity of SNNs and the richness of DNNs. The article details the construction of the Joint-ResNets framework and its canonical use in classical semantic communication models, and illustrates the control mechanism of the variable weight \begin{document}$\alpha$\end{document} term in the Joint-ResNets framework and its importance in balancing the model complexity between SNNs and DNNs. The article takes the task-oriented communication model in the device edge collaborative reasoning system as an example for experimentation and analysis. The experimental validation shows that DNNs and SNNs can be combined in a more effective way to standardize semantic coding, which improves the overall predictive performance, interpretability, and robustness of semantic communication models, and this framework is expected to bring new breakthroughs in the field of semantic communication.

关键词： Semantic communication Residual neural networks (ResNets) Joint control mechanisms Edge reasoning

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Industrial Systems Optimization from Combining Internet of Things and Cloud Computing

Industrial Systems Optimization from Combining Internet of T...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Nachappa, M.N. Faujdar, Pramod Kumar Agarwal, Parag Department of Computer Science and Information Technology Bangalore India Mechanical Engineering Vivekananda Global University Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Business structures optimization from combining the net of things (IoT) and Cloud Computing is a subject of study that recognizes the vital function that the two technology can play collectively in optimizing industrial methods. IoT leverages numerous statistics resources;the cloud gives a repository where these statistics resources may be stored and maintained, and cloud computing evaluates and optimizes the records to discover potential efficiencies and enhance overall operational performance. This analysis results in becoming aware of and implementing process upgrades and reducing the production fee. As a result, the industrial gadget is in a position to use the statistics generated using the IoT and Cloud Computing to enhance operational efficiency and patron delight. ultimately, the combination of IoT and Cloud Computing resulted in reduced manufacturing fees and improved operational performance. Business structure optimization by combining the Internet of Factors (IoT) and Cloud Computing can bring about stepped forward performance and productivity of organizations and their production approaches. IoT in business settings presents agencies with correct and real-time facts from systems and machines, and cloud computing gives value-effective storage and management of large amounts of such facts. By combining records from one-of-a-kind linked devices, IoT and Cloud Computing may create a 'business Intelligence community,' which may be used to screen and optimize approaches. It permits improved selection-making, elevated production speed, and more advantageous product excellence. Additionally, with the aid of leveraging predictive analytics, the community can be used to assume ability issues in manufacturing techniques and diagnose and prevent them as nicely. This combination of IoT and Cloud Computing can ultimately assist businesses in growing operational efficiency, reducing fees, and gaining an aggressive advantage in the market.. © 2024 IEEE.

关键词： Business efficiency Intelligence resources statistics

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Prediction of Web Page Relevance in Focused Crawling Using Artificial Neural Networks 3

Prediction of Web Page Relevance in Focused Crawling Using A...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Dhabliya, Dharmesh Alawadi, Ahmed Hussein Varkale, Akshay Aljanabi, Mohammad Vishwakarma Institute of Information Technology Department of Information Technology Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Deportment of Computer Science & Engineering Bhopal India Alirqia University Department of Computer Baghdad Iraq

ISBN: (纸本)9798350342338

In the rapidly expanding digital ecosystem, efficient web crawling is paramount to extract relevant information from the vast expanse of the internet. This research addresses the challenge of predicting web page relevance in focused crawling using Artificial Neural Networks (ANNs). The study leverages a curated dataset of web pages, annotated for their relevance to specific domains. A multi-layer perceptron architecture was employed for the ANN, with features extracted from the web pages serving as inputs. The model was trained and validated using a split of 70% training and 30% testing data. Experimental results indicate that the ANN achieved an accuracy of 93.20%, outperforming traditional methods by a margin of 8%. This demonstrates the potential of ANNs in enhancing the efficiency of focused web crawlers, ensuring more accurate data extraction in less time. The findings pave the way for integrating deep learning techniques into web crawling systems, optimizing them for the evolving digital landscape. © 2023 IEEE.

关键词： Artificial Neural Networks Data Extraction Deep Learning Techniques Focused Crawling Web Page Relevance

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Intelligent Irrigation Mechanism to Enhance The Sensor Network Topology by Using Machine Learning And Artificial Intelligence 2

Intelligent Irrigation Mechanism to Enhance The Sensor Netwo...

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Sriram, K.P. Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sujatha, P. Kola Assistant Professor Information technology St.Joseph's Institute of Technology OMR Chennai – 600119 Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattur OMR Siruseri Chennai Associate Professor Department of Information Technology MIT Campus Anna University Chennai Tamil Nadu India – 600044

ISBN: (纸本)9798350383867

This research paper is about the effectiveness of a fusion of machine learning and artificial intelligence into smart irrigation systems in terms of both safety and benefit of the environment and the economy in farming. We've experienced a intensive process of experiment and application of this succeeded method in a real world scenario and we've also detailed the performance of the dynamic irrigation scheduling and the adapter sensor network optimization. Results indicate a significant improvement in key metrics: scheduling was done dynamically which on average was a 10% rise in crop yield and 15% water saving, on the other hand, sensor network optimization with adaptive technology resulted into a 15% increase in cover area and 20% improvement achieved in data transmission reliability. Furthermore, the introduction of the smart irrigation system got impressive results signified by a 10 kg more production per hectare, irrigation efficiency that rose from 70% to 85%, and saving 20% of the water consumed. This calls to mind the capability of cutting-edge technologies to contribute to innovative water management paradigms, improved resource use, and security in food production in face of unpredictable climatic variability. ©2024 IEEE.

关键词： Adversarial machine learning

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