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Assessing mowing intensity:A new index incorporating frequency,type of machinery,and technique

Grassland research 2024年第3期3卷 264-274页

作者： Margarita Hartlieb Sebastian Raubitzek Johanna L.Berger Michael Staab Juliane Vogt Manfred Ayasse Andreas Ostrowski Wolfgang Weisser Nico Blüthgen Ecological Networks Department of BiologyTechnical University of DarmstadtDarmstadtGermany SBA-Research ViennaAustria Natura 2000-Station Unstrut-Hainich/Eichsfeld Wildtierland Hainich gGmbHHörselberg-HainichGermany Institute of Evolutionary Ecology and Conservation Genomics Ulm UniversityUlmGermany Heinz Nixdorf Chair for Distributed Information Systems Faculty of Mathematics and Computer ScienceFriedrich Schiller UniversityJenaGermany Department of Life Science Systems School of Life SciencesTechnical University of MunichTerrestrial Ecology Research GroupFreisingGermany

Background:Only a few decades ago,colorful,small-scale,heterogeneous,and species-rich hay meadows or extensive pastures were common,but have often been replaced by species-poor,uniform,large-scale multicut *** advancements and improved efficiency in grassland management have come at the cost of ***:In Germany,150 grassland plots have been investigated since *** these extensive data,we propose a new compound index for estimating the site-specific mowing intensity in order to facilitate assessment of the impact of mowing intensity on biodiversity and ecosystem *** index integrates the various qualitative components of mowing machine type,mowing height and use of a conditioner,with the annual number of ***:The newly proposed index achieves a much finer gradation of mowing intensity compared to the previous quantification based on the number of cuts ***,a decrease in plant and arthropod species was observed at higher mowing ***:The proposed mowing intensity index offers enhanced precision in calculations and can easily be integrated into assessments of land-use intensity in ***,it could serve as a basis for providing subsidies to farmers,who adopt low-impact mowing practices.

关键词： applications conditioner mower mowing height number of cuts species richness

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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HTTP Flood DOS Attack Detection on Big Data Using Data Mining 1

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13th International Conference on Information systems and Advanced Technologies, ICISAT 2023

作者： Haddadi, Mohamed Khiat, Abdelhamid Abidi, Yasmina Derradji, Yaakoub Department of Commercial Sciences Faculty of Economic Commercial and Management Sciences M’hamed BOUGARA University of Boumerdes Independence Avenue Boumerdes35000 Algeria Networks and Distributed Systems Division Research Center on Scientic and Technical Information Ben Aknoun Algeria Department of Computer Science Faculty of Sciences University of Ferhat Abbas Setif1 Setif Algeria

ISBN: (数字)9783031605918

ISBN: (纸本)9783031605901

Nowadays, increasing use of Internet connection, security becomes a huge challenge for individuals as well as governments and organizations. Therefore, in the last decade, the world is moving towards green computing in the purpose either to store energy or to decrease operational costs. So, this new technology uses web servers to provide web applications to end user. Generally, these web servers become unavailable because of HTTP flood DoS attack. This paper applied data mining techniques such as WEKA and TANAGRA tools for data preprocessing in the aim to get a consistent data in one hand, and for classifying the traffic in normal behavior or anomaly using like J48, Random tree, SMO, Naïve bayes, IBK, and combined classifier in the other hand. For this aim, a well-known big data set is used which called http_csic_2010_full dataset. Results show that the two data mining tools are well close with all the classifiers, just MLP in WEKA, and CVM in TANAGRA they didn’t perform well in terms of detection accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： HTTP

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Repository of Interval Models of Dynamics of Concentrations of Harmful Emissions of Motor Transport 12

Repository of Interval Models of Dynamics of Concentrations ...

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12th International Conference on Advanced computer Information Technologies, ACIT 2022

作者： Dyvak, Mykola Melnyk, Andriy Dostalek, Libor Ostroverkhov, Viktor Honchar, Lyudmyla Romanets, Ihor West Ukrainian National University Department of Computer Science Ternopil Ukraine West Ukrainian National University Faculty of Economics and Management Ternopil Ukraine Czech Technical University in Prague Computer Networks Research Group Department of Computer Systems Prague Czech Republic

ISBN: (数字)9781665410502

ISBN: (纸本)9781665466479

To build mathematical models based on the obtained interval data, it is necessary to solve the problems of structural and parametric identification. The result of the identification is the interval difference equations of the dynamics of concentrations of harmful emissions of vehicles. Procedures for structural and parametric identification of such models are quite complex. Therefore, in practice, based on the experience gained in modeling these processes, it is advisable to create a repository of interval models that would describe these processes, or easily customized to specific application conditions. This task is considered in the article. The basis for repository management is the ontological model. The features of building an appropriate ontological model, analysis the shortcomings of existing tasks, which are solving with its help also presented in this paper. © 2022 IEEE.

关键词： Knowledge management

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An empirical investigation of deep transfer learning approach to automatic detection of coronavirus disease (COVID-19) in CT images 2

An empirical investigation of deep transfer learning approac...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Folorunso, Sakinat Oluwabukonla Adeniyi, Abidemi Emmanuel Ogundele, Lukman Adebayo Awotunde, Joseph Bamidele Brahma, Biswajit Bhoi, Akash Kumar Olabisi Onabanjo University Artificial Intelligent Systems Research Group Department of Mathematical Sciences Ago-Iwoye Nigeria Bowen University Department of Computer Science Iwo Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria McKesson Corporation San FranciscoCA94104 United States Sikkim Manipal University Research Sikkim Gangtok737102 India

ISBN: (纸本)9798350370188

The recently discovered COVID-19 coronavirus pneumonia is highly contagious, pathogenic, and incurable without the use of an antiviral medication or vaccination that has received clinical approval. The commonest COVID-19 symptoms include a dry cough, fever, and sore throat. A severe type of pneumonia with life-threatening difficulties, such as infected tremor, multi-organ failure, pulmonary edema, and acute respirational distress syndrome (ARDS), can improve as a consequence of these symptoms. While computer-aided diagnosis (CAD) methods may aid in the early discovery of COVID-19 anomalies, it may also aid in monitoring the evolution of the illness, thereby lowering death rates. For automatic COVID-19 categorization, we compare well-known deep learning-technique feature extraction structures in this study. Deep convolutional neural networks were drawn from a source, VGG16, Xception, and GoogleNet were proposed to create the most precise and precise attribute, which is a crucial part of learning. The extracted features were then used to categorize images as either COVID-19 cases or controls using a variation of machine learning classifiers. To enable a stronger ability to generalize for unidentified data, our approach shunned data pre-processing for certain tasks techniques. A COVID-19 dataset of and CT scan images was used to validate the performance of the suggested technique. With 90% classification accuracy, the Xception feature extractor and CNN delivered the best performance. © 2024 IEEE.

关键词： Coronavirus

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Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors

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computers, Materials & Continua 2023年第10期77卷 583-599页

作者： Fadhil Mukhlif Norafida Ithnin Roobaea Alroobaea Sultan Algarni Wael Y.Alghamdi Ibrahim Hashem Information Assurance and Security Research Group(IASRG) Faculty of ComputingUniversiti Teknologi Malaysia(UTM)Johor BahruJohor81310Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia College of Computing and Informatics Department of Computer ScienceUniversity of SharjahSharjah27272United Arab Emirates

The World Health Organization(WHO)refers to the 2019 new coronavirus epidemic as COVID-19,and it has caused an unprecedented global crisis for several *** every country around the globe is now very concerned about the effects of the COVID-19 outbreaks,which were previously only experienced by Chinese *** of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare *** time the pandemic surprises them by providing new values for various parameters,all the connected research groups strive to understand the behavior of the pandemic to determine when it will *** prediction models in this research were created using deep neural networks and Decision Trees(DT).DT employs the support vector machine method,which predicts the transition from an initial dataset to actual figures using a function trained on a *** short-term memory networks(LSTMs)are a special sort of recurrent neural network(RNN)that can pick up on long-term *** an added bonus,it is helpful when the neural network can both recall current events and recall past events,resulting in an accurate prediction for *** provided a solid foundation for intelligent healthcare by devising an intelligence COVID-19 monitoring *** developed a data analysis methodology,including data preparation and dataset *** examine two popular algorithms,LSTM and Decision tree on the official ***,we have analysed the effectiveness of deep learning and machine learning methods to predict the scale of the *** issues and challenges are discussed for future *** is expected that the results these methods provide for the Health Scenario would be reliable and credible.

关键词： Healthcare framework AI COVID-19 machine&deep learning LSTM RNN decision tree

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PpNNT: Multiparty Privacy-Preserving Neural Network Training System

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 370-383页

作者： Feng, Qi He, Debiao Shen, Jian Luo, Min Choo, Kim-Kwang Raymond Wuhan University School of Cyber Science and Engineering Wuhan430072 China Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China School of Information Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Shanghai201204 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

By leveraging smart devices [e.g., industrial Internet of Things (IIoT)] and real-time data analytics, organizations, such as production plants can benefit from increased productivity, reduced costs, enhanced self-monitoring, and autonomous decision-making. In such a setting, machine learning plays an important role in data analytics, but the use of conventional centralized machine learning solutions may raise uncomfortable concerns about data privacy. Hence, one can explore the use of federated learning. In this article, we propose privacy-preserving deep neural network training (PpNNT), which is designed to support federated learning in the multiparty setting. To minimize the overall costs, we further design a hybrid architecture to fully maximize resource utilization. Our proposed design allows the PpNNT system to provide high security, efficiency, and scalability for IIoT data analytics, as evidenced by our theoretical security proof and experimental results on the CIFAR10 dataset. © 2020 IEEE.

关键词： Decision making

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Exploiting Federated Learning for EEG-based Brain-computer Interface System

Exploiting Federated Learning for EEG-based Brain-Computer I...

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2023 IEEE International Conference on Omni-Layer Intelligent systems, COINS 2023

作者： Ghader, Mohammadnavid Farahani, Bahar Rezvani, Zahra Shahsavari, Mahyar Fazlali, Mahmood Shahid Beheshti University Department of Computer Science Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran Tehran Iran Alzahra University Department of Computer Science Tehran Iran Donders Institute Radboud University Nijmegen Netherlands University of Hertfordshire Cybersecurity and Computing Systems Research Group Hatfield United Kingdom

ISBN: (纸本)9798350346473

Motor imagery (MI) is a widely used technique in brain-computer interface (BCI) systems, which allows users to control external devices using their brain signals. Electroen-cephalogram (EEG) signals are commonly used to detect and classify MI tasks. However, the lack of annotated data hampers the performance of machine learning (ML) algorithms. Federated learning (FL) is a promising approach to address these challenges, as it allows models to be trained on decentralized datasets without exchanging data. This study proposes an FL approach for MI-EEG signal classification using a convolutional neural network (CNN) on the PhysioNet dataset containing EEG recordings of left and right-hand imagery movements. We evaluate the performance of the FL approach using two different aggregation methods, namely FedAvg and FedProx, and compare it to the centralized ML approach. Furthermore, we explore the effect of increasing the number of clients who participate in the learning process on the performance of the model. Our findings demonstrate that FL maintains consistent classification accuracy comparable to the centralized ML approach while reducing data leakage risks. Thus, FL shows great promise as an essential instrument for MI-EEG signal classification and BCI systems, enabling distributed training of a comprehensive model when privacy-sensitive data is dispersed across multiple clients. © 2023 IEEE.

关键词： Electroencephalography

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Software-defined event-based camera and vision algorithms for digitizing highly dynamic manufacturing tasks

Software-defined event-based camera and vision algorithms fo...

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2024 Low-Cost Digital Solutions for Industrial Automation, LoDiSA 2024

作者： Walker, Emily Turner, Christopher J. Oyekan, John Department of Automatic Control & Systems Engineering The University of Sheffield SheffieldS1 3JD United Kingdom Surrey Business School The University of Surrey Surrey GuildfordGU2 7XH United Kingdom Real Time & Distributed Systems Group Department of Computer Science University of York Heslington YorkYO10 5GH United Kingdom

ISBN: (纸本)9781837242672

Event-based cameras are inspired by the way the retina in the eye process information. Rather than capturing image frames at a constant rate, event cameras generate data asynchronously, based on changes in pixel value between frames. As a result, these cameras have a high spatiotemporal accuracy, low power constraints and reduced artefacts from motion blur. Hence, they represent favourable properties for digitizing highly dynamic manufacturing tasks such as machine vibrations and rapid human assembly actions. However, the costs of event-based cameras are prohibitively high for small to medium sized enterprises (SMEs). In this research, we present the development of a rig that combines low-cost visual sensors with low-cost algorithms to perform action recognition for highly dynamic manual manufacturing tasks. We applied our algorithms to achieve an action classification system via a novel threshold-based approach and in parallel use a neural network to perform classification of objects in the video feed. The results achieved demonstrated that our approach was able to successfully identify various rapid manual tasks being performed on identified objects. In addition to SMEs, our approach would be suited for providing low-cost digitization on legacy machines. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/).

关键词： Machine vibrations

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A Review of Pseudonym Change Strategies for Location Privacy Preservation Schemes in Vehicular networks

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ACM Computing Surveys 2025年第8期57卷 1-37页

作者： Benarous, Leila Zeadally, Sherali Boudjit, Saadi Mellouk, Abdelhamid Department of Computer science Amar Telidji University of Laghouat Laghouat Algeria LISSI-Tinc-NET Laboratory University of Paris-Est Creteil Créteil France College of Communication and Information University of Kentucky Lexington United States Department of Electronic Engineering Kyung Hee University Seoul Korea Republic of Litis Lab Intelligent Transportation Systems Group University of Rouen Normandy Mont-Saint-Aignan France Lissi Laboratory Department of Networks and Telecom University Paris-Est Créteil Val de Marne Créteil France

Location privacy protection in vehicular networks has been a primary priority to ensure because of its direct impact on human physical safety. Leakage and violation of road users' location privacy may be perilous beyond simple curiosity as it may scale to cyberstalking and tracking. This may lead to road users being subjected to mental and physical distress, from blackmailing to targeted advertising, and trap planning. The results of tracking may be fatal to road users. Over the past 17 years, researchers have been actively investigating location privacy protection mechanisms for vehicular networks. Numerous research results have been published that examined the use of pseudonym change strategies, which have been considered as the most appropriate solution that maintains the tradeoff between ease of application, good protection level, and the correct functionality of the network. We evaluate, classify, and analyze pseudonym change strategies aimed at location privacy preserving solutions for vehicular networks and discuss their performance. We also review the simulation tools used for evaluating pseudonym change techniques used in location privacy preserving solutions for the vehicular environment. The results of this survey will help researchers in this field to develop more robust, efficient, and cost-effective location privacy protection schemes in the future. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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