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检索条件"任意字段=4th International Multi-Conference on Computing in the Global Information Technology"

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Energy Consumption Analysis of R-Based Machine Learning Algorithms for Pandemic Predictions 4th

Energy Consumption Analysis of R-Based Machine Learning Algo...

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

作者： Benedict, Shajulin Agrawal, Prateek Prodan, Radu Indian Institute of Information Technology Kottayam Kottayam Kerala686635 India Lovely Professional University Phagwara Punjab India University of Klagenfurt Klagenfurt Austria

ISBN: (纸本)9789811636592

the push for agile pandemic analytic solutions has attained development-stage software modules of applications instead of functioning as full-fledged production-stage applications – i.e., performance, scalability, and energy-related concerns are not optimized for the underlying computing domains. And while the research continues to support the idea that reducing the energy consumption of algorithms improves the lifetime of battery-operated machines, advisable tools in almost any developer setting, an energy analysis report for R-based analytic programs is indeed a valuable suggestion. this article proposes an energy analysis framework for R-programs that enables data analytic developers, including pandemic-related application developers, to analyze the programs. It reveals an energy analysis report for R programs written to predict the new cases of 215 countries using random forest variants. Experiments were carried out at the IoT cloud research lab and the energy efficiency aspects were discussed in the article. In the experiments, ranger-based prediction program consumed 95.8 J. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Energy utilization

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IOT Enabled Enhanced Method for Water Leakage Detection and Localization in Water Distribution Systems

IOT Enabled Enhanced Method for Water Leakage Detection and ...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Niyitegeka Janvier Ndayisenga Gilbert Mbayingabo J.M.V Kagaju Clarisse Harerimana Sophonie Electronics and Telecommunication Technology Information Communication Technology. Rwanda Polytechnic /IPRC Tumba Rulindo Rwanda

In existence, Human beings and other living creatures require water for their survival. Moreover, in numerous other sectors like Agribusiness, Industry, and so on, water is additionally utilized as one of the input keys for their production. thus, it is very important for a precise administration of this asset preservation for social and financial advancement *** the agriculture sector, water-based artificial systems (crops irrigation systems) are mostly used for maintaining the optimum conditions of the crops in the soil regardless of the environmental climate change. However, when the irrigation system is damaged it is very difficult for individuals to locate the water leakage by relying on the conventional way of water spillage location and observing framework. Sometimes, it would take several days for the concerned specialists to find pipe spills and repair them. It isn’t also simple to know the life condition of the water distribution system found beneath the ground and this delay causes water several amounts of water to be *** is in line with the aforementioned need, the current paper proposes a doable strategy to be utilized in a water distribution system for identifying and finding the pipe spills in a short period of time which leads to a decrease in water wastage due to the breakings, erosion or the unearthing of the channeling frameworks. the system prototype implemented in this work mainly consists of three important subsystems. the first subsystem is the sensing part which uses flow rate sensors, attached to the edges of the pipes for comparing the flow rate of the source and destination pipe. the second is the actuating subsystem that uses the solenoid valve that is used to shut off the water flow in case of a leak is detected, then the communication network that uses of GSM module for allowing the end-users to monitor the life of the water distribution systems remotely for immediate support in case it is needed.

关键词： Location awareness Irrigation Crops Prototypes Production Soil Valves

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Transforming Business Management Practices for Increased Production through Deep Learning and Cloud computing integration

Transforming Business Management Practices for Increased Pro...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： P. Santhosh Kumar Anupam Singh Soniya Shaik Farhana Amar Choudhary Susmi Biswas Department of Information Technology Ramapuram Faculty of Engineering and Technology Srm Institute of Science and Technology Chennai India Department of Computer Science and Engineering Graphic Era Hill University Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Management Sanskar College of Engineering Ghaziabad India Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department Electronics and Communication Engineering Alliance College of Engineering and Design (ACED) Alliance University Bengaluru India Department of Management Science Haldia Institute of Management MAKAUT India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

this research is vital to meet the need for improved predictive maintenance in manufacturing, thus enabling predictive operations and minimizing expensive downtime. the aim is to assess and compare different machine learning models like Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Decision Trees( DT ) with respect to predicting maintenance requirements using historical sales data, social media trends, and machinery sensor readings. the methodology included gathering a dataset of 3,200 records from the industry and then the pre-processing phase to compute the preprocessing operations like data cleaning, normalization and feature selection. the various models were then trained and tested, using 70% of the data for training to generalise the model, with the rest of the unseen test set consisting of the remaining 30%. the models were evaluated based on performance metrics (accuracy, precision, recall, F1 score and Dice Similarity Coefficient or DSC). Results showed the proposed ANN model as the most accurate and efficient prediction mechanism in comparison with precision, recall and F1 score for remaining useful life (RUL) predictions. the SVM model also worked well, with an accuracy of 95.45% but low rates of false positives and negatives. Although the DT model was informative, its performance (93.34% accuracy) was less robust than that of our RF ensemble trainer. this research demonstrates the use of these findings to be applied in real-time maintenance prediction by ANN. this research would help industries and other manufacturing sectors to reduce downtime, achieve optimal planned maintenance scheduling and improve production efficiency.

关键词： Support vector machines Accuracy Computational modeling Artificial neural networks Production Predictive models Data models Real-time systems Maintenance Manufacturing

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Design of Intelligent Monitoring System for Air Visibility Data Based on UAV 4th

Design of Intelligent Monitoring System for Air Visibility D...

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4th EAI international conference on Advanced Hybrid information Processing, ADHIP 2020

作者： Zhang, Jun Liu, Jun-jun College of Electrical Engineering Zhengzhou University of Science and Technology Zhengzhou450064 China Department of Information Engineering Information Engineering Zhengzhou University of Science and Technology Zhengzhou450064 China

ISBN: (纸本)9783030678708

UAV has the advantages of small size, low cost and strong performance. How to use UAV to realize the intelligent monitoring of atmospheric visibility data has become one of the research hotspots. therefore, an intelligent monitoring system of visibility data based on UAV is designed. through the overall design of the system, the requirements for the collection, processing, transmission, display, monitoring and positioning of atmospheric visibility data are realized. the airborne air visibility sensor module is used to collect gas data, the microprocessor is used to process data, the global positioning system is used to complete monitoring and positioning, and the general packet wireless service is used to complete data transmission and data display of the ground terminal. the experimental results show that the air visibility data intelligent monitoring system based on the UAV has good accuracy and high monitoring efficiency It can meet the requirements of data monitoring system. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Visibility

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Deep Recognition of Chinese Herbal Medicines Based on a Caputo Fractional Order Convolutional Neural Network 1

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5th international Workshop on Internet of things of Big Data for Healthcare, IoTBDH 2023

作者： Li, Tao Yang, Jiawei Li, Chenxi Lv, Lulu Liu, Kang Yuan, Zhipeng Li, Youyong Yu, Hongqing College of Railway Transportation Hunan University of Technology Zhuzhou China Department of Computer Science University of Sheffield Sheffield United Kingdom Hubei Provincial Hospital of Traditional Chinese Medicine Wuhan China School of Computing and Engineering University of Derby Derby United Kingdom

ISBN: (数字)9783031522161

ISBN: (纸本)9783031522154

Chinese herbal medicines are the treasure of Chinese traditional medicine, which contains active ingredients that have significant therapeutic effects for many diseases. To solve the fluctuation and inefficiency problems faced by the existing image recognition of Chinese herbal medicines relying on manual subjectivity, a deep classification and recognition method based on a fractional order convolutional neural network is proposed for the images of Chinese herbal medicines. this method introduces Caputo fractional order gradient descent to update the model parameters, which improves the accuracy of gradient descent in the process of training the model, and solves the problem that integer order gradient descent is prone to fall into the local optimal solution and thus leads to low accuracy. By establishing a multi-scale standard Chinese herbal medicines dataset, the feature recognition ability based on Caputo Fractional Order Convolutional Neural Network (CFO-CNN) is trained and tested. And at the same time, the fractional order backpropagation function is used, so that the model can effectively find the global optimal solution, and achieve the output of the feature deep recognition. Experiments show that the method can effectively achieve the deep classification recognition of Chinese herbal medicines varieties, species and grades. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image recognition

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Digital Twins for Brain Tumor Detection using MRI Images by Deep Learning

Digital Twins for Brain Tumor Detection using MRI Images by ...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Kranthika Terala Chinnaswamy C N Arjun Nelikanti Dev Vikas Juneja Mohammad Khaja Shaik Pundru Chandra Shaker Reddy Department of CSE (AIML) Geethanjali College of Engineering and Technology Telangana India Department of Information Science and Engineering The National Institute of Engineering Mysuru Karnataka India Computer Science and Engineering Vignana Bharathi Institute of Technology Telangana India Department of Computer Science Pimpri Chinchwad College of Engineering Pune Maharashtra India Department of AIML St Ann's College of Engineering and Technology Chirala AP India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

the aberrant growth and proliferation of brain tumors causes them to have one of the highest fatality rates among all cancers, despite the rarity of the disease. When aberrant muscles buildup in the brain, a tumor forms. there is a wide variety of aberrant tissue types, but most of the time they grow normally and then die off without bothering anyone. there are a few other names for digital twins: digital-mirror, mapping, and duplicate. Each of these words describes the similar thing; they are interchangeable. It is a method for moving data across other domains, whether that data is digital or physical. the goal of image processing is to accomplish some task by adding or removing data from a picture. An example of a neural network is a convolutional neural network, which takes picture signals as input and outputs either the full image or a choice of its modules. this study details a method for diagnosing brain malignancies using magnetic resonance imaging (MRI) scans by employing digital twins and sophisticated machine learning techniques. Using digital devices connected to the IoT, MRI images are stored in a central cloud. After that, we access the cloud storage to get the input images and any other health-related data. Features are selected using the Particle Swarm Optimization method. Image classification of brain tumors is accomplished by means of deep-learning(DL) strategies such as CNN. When it comes to investigating MRI-images for the aim of detecting brain-tumors (BT), the CNN algorithm shows significantly better accuracy.

关键词： Deep learning Cloud computing Magnetic resonance imaging Brain tumors Personal digital devices Muscles Real-time systems Digital twins Convolutional neural networks Particle swarm optimization

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Improving Quality of Seaport's Work Schedule: Using Aggregated Indices Randomization Method 4th

Improving Quality of Seaport's Work Schedule: Using Aggregat...

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4th international Scientific conference on Intelligent information Technologies for Industry (IITI)

作者： Olga, Vasileva Vladimir, Kiyaev Artur, Azarov St Petersburg State Univ St Petersburg Russia St Petersburg State Univ Econ St Petersburg Russia Russian Acad Sci St Petersburg Inst Informat & Automat 39 14 Th Line VO St Petersburg Russia

ISBN: (纸本)9783030500979

Genetic algorithms can be used in combination with multi-agent monitoring and data collection technologies for compiling and adjusting the dynamically changing work schedule of an economic entity with complex internal infrastructure. In this case, the important issue is the quality of the drawn-up schedule and its monitoring. the article reviews the possibility of applying the method of randomized summary indicators for assessing the quality of the compiled and dynamically adjusted service schedules for ships in the seaport.

关键词： Scheduling Genetic algorithms multi-agent technologies the method of summary indicators

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Linear Policy Recommender Scheme for Large-Scale Attribute-Based Access Control 1

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4th EAI international conference on Security and Privacy in New computing Environments, SPNCE 2021

作者： Wang, Jing Huang, Weijia Liu, Wenfen Wang, Lingfu Zhang, Mingwu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9783030967918

ISBN: (纸本)9783030967901

In the large-scale data sharing platform, access control mechanism plays an important role in protecting data security and privacy. Significantly, Attribute-Based Access Control (ABAC) can support fine-grained access control, which would make the data sharing platform more flexible, efficient and manageable. However, in ABAC, data owner need to manually assign access policies for each data, which would incur lots of workload and limit the usability of the system. thus, we propose a linear policy recommender scheme for ABAC in this work. Firstly, we propose a general form of access policy named linear policy, which describes the policy as a linear function. Comparing with other forms of policy, linear policy is more flexible and efficient. Secondly, we propose a matrix factorization based linear policy recommender scheme. the scheme learns a policy matrix and a security threshold vector from access logs and recommends the optimal linear policy for each data by a binary matrix factorization model. Intuitively, the policy matrix and security threshold vector can be viewed as the optimal linear policy of each data, which is helpful for ABAC to improve policy generation and management. Finally, sufficient experiments are given to present the performance of the proposed policy recommender system. the result shows that our policy recommender system is efficient and accurate in calculation. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Access control

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Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer 29

Improving Fake News Detection of Influential Domain via Doma...

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29th international conference on Computational Linguistics, COLING 2022

作者： Nan, Qiong Wang, Danding Zhu, Yongchun Sheng, Qiang Shi, Yuhui Cao, Juan Li, Jintao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance’s contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario. © 2022 Proceedings - international conference on Computational Linguistics, COLING. All rights reserved.

关键词： Fake detection

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Edge-to-Fog Collaborative computing in a Swarm of Drones 10th

Edge-to-Fog Collaborative Computing in a Swarm of Drones

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10th international conference on New Trends in Model and Data Engineering, MEDI 2021 held with 4th international Workshop on Modeling, Verification and Testing of Dependable Critical Systems, DETECT 2021, Symposium on Intelligent and Autonomous Systems, SIAS 2021, 1st Workshop on Control Software: Methods, Models, and Languages, CSMML 2021, Workshop on Blockchain for Inter-Organizational Collaboration, BIOC 2021 and 1st international Health Data Workshop, HEDA 2021

作者： Rahbari, Dadmehr Mahtab Alam, Muhammad Le Moullec, Yannick Jenihhin, Maksim Thomas Johann Seebeck Department of Electronics Tallinn University of Technology Ehitajate tee 5 Tallinn19086 Estonia Department of Computer Systems Tallinn University of Technologies Akadeemia tee 15A Tallinn12618 Estonia

ISBN: (纸本)9783030876562

Recently, drones technology started unleashing numerous novel applications and services and became one of the killer applications for the Mobile Edge computing (MEC) paradigm. the main challenge for real-time applications is, however, transferring data between data processing servers and drones, which yields considerable latency and energy consumption and reduces the autonomy and dynamics of operations. In this paper, we consider a swarm of heterogeneous and autonomous drones deployed to detect objects in run-time video streaming. Each drone can execute data-processing related computation tasks locally, or offload them to other nodes in the swarm. For efficient cooperation at the swarm level, forming a system of systems, drones need to cooperate in using each other’s resources and managing the communication and offloading operations. To overcome the mentioned challenges and also data privacy, we propose a novel edge-to-fog collaborative computing framework employing federated learning (FL)-based offloading strategy with a rating method. the proposed method also aims to offload the computation tasks between drones in a fair and efficient manner. © 2021, Springer Nature Switzerland AG.

关键词： Drones

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