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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

共 37467 条记录，以下是4541-4550 订阅

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Decentralized Smart Irrigation with Remote data Visualization 6th

Decentralized Smart Irrigation with Remote Data Visualizatio...

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6th international conference on Microelectronics and Telecommunication engineering, ICMETE 2022

作者： Kumar, Sanjeev Sharma, Vivek Department of Computer Science and Engineering Technocrats Institute of Technology Bhopal India

ISBN: (纸本)9789811995118

Every sector is evolving faster than ever in this fast-growing world. Smart technologies connected with different things are using the internet to collect and analyze data better to understand the manufacturing line, vehicles, etc. Same technologies can be used in the same way in the agriculture sector. The proposed system has multiple nodes, also known as things, connected to the Internet. These nodes are independent individual units, usually ESP8266, which can collect data using temperature, humidity, and moisture sensors for soil. This data can be stored and visualized in the cloud easily. This data can further be used along with weather reports to manage the irrigation system. Each node can independently control the irrigation of its area or sector. data is stored on Google Firebase Real-Time database. All the data can be visualized, and the farmer or cultivator can control irrigation through web clients. Thus, this system can manage irrigation and plantation on vast land with just one centralized portal with a click. It will improve the overall efficiency of the farming sector for farming and plantation in a greenhouse with a lesser workforce. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Irrigation

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Classification of Dementia using Machine Learning models and interpreting the results using LIME and SHAP

Classification of Dementia using Machine Learning models and...

引用

knowledge engineering and Communication systems (ICKECS), international conference on

作者： R K Charan Nischay M Kumar Ashwini Kodipalli Trupthi Rao Department of AI&DS Global Academy of Technology Bangalore India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

This work investigates the use of machine learning (ML) to the categorization of dementia using datasets. Transparency is improved by the development of strong machine learning models and interpretability strategies (LIME, SHAP). Handling missing values, standardizing features, and feature engineering (dimensionality reduction) are all part of comprehensive data preprocessing. Performance metrics evaluates models, and interpretability results highlight important features. The results highlight the value of interpretability and the effectiveness of machine learning. Strengths, weaknesses, biases, ethics, and practical difficulties are all discussed. Prospective paths prioritize ongoing enhancement and investigation of sophisticated machine-learning methods for dementia identification.

关键词： Measurement knowledge engineering Dimensionality reduction Ethics Accuracy data preprocessing Machine learning

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Segmentation of Brain Tumors using traditional Multiscale bilateral Convolutional Neural Networks 2

Segmentation of Brain Tumors using traditional Multiscale bi...

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2nd international conference on Sustainable Computing and data Communication systems, ICSCDS 2023

作者： Asha, P. Ankayarkanni, B. Joshila Grace, L.K. Kaarthik, K.M. Reddy, M. Hemanth Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665491990

Non-automated segmentation of brain tumors from the MRI images is a tedious task, which may sometimes produce inaccurate results. Therefore, the accuracy and reliability of brain tumor segmentation is also key to decision-making, treatment design, and analysis of treatment results. The automated brain tumor segmentation strategy uses manually developed parameters. As with convolutional neural networks, training requires excessive amount of annotated data that is generally challenging to retrieve in the medical field. The prior demand is the design of a task adapted along with robust feature selection and representation. This seems mandatory for effective and efficient segmentation of complicated brain tumors, which in turn is possible with two-channel Convolutional Neural Networks (CNN). Hence, two-channel CNN is adapted for the segmentation as it provided promising results for similar research. The two-channel CNN model contributes much in lessening jitter and over fitting parameters. Conclusively, a two-way cascade design to CNN is introduced, where the base CNN output is processed as an auxiliary source and combined at the final level. When testing the model in the BRATS2013 and BRATS2020 knowledge sets, the CNN integrated package in the 2-channel design was not hidden. Compared to the progressive method described above, the overall performance has been improved and the process quality remains attractive. © 2023 IEEE.

关键词： Decision making

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Consistency Guaranteed Two-Timescale Optimization Decision for Energy Management System in Nuclear Power Plant 4

Consistency Guaranteed Two-Timescale Optimization Decision f...

引用

4th international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Nie, Zelin Cheng, Wei Xie, Shushuai Zhou, Guanghui Chen, Xuefeng State Key Laboratory for Manufacturing Systems Engineering School of Mechanical Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China

ISBN: (纸本)9781510666412

When a nuclear power plant is in normal operation, the generated electric energy is divided into two circuits. One is boosted and transmitted to the State Grid, and the other (about 5%) is used for the electrical equipment in the nuclear power plant after voltage reduction. As an important part on the user side of smart grid, the Energy Management System (EMS) is of great significance for reducing energy consumption. Meanwhile, under the unique environment of high humidity and high salinity of nuclear power plants, Heating, Ventilation, and Air Conditioning (HV AC) accounts for most of the energy consumption except nuclear power unit. An EMS optimization decision problem is usually formulated in multiple timescales. In order to make the electric dispatching for electrical equipment (represented by HVAC) except nuclear power unit and improve environment comfort, a two-timescale optimization decision model is built in this paper. According to the characteristic of nuclear power plant, the state relationship of two-timescale is in mean value type, and the consistency of two-timescale is guaranteed. With the actual data of a nuclear power plant in China, the results of case study demonstrate the validity of the proposed model and it can satisfy the environment comfort with energy limited. © 2023 SPIE.

关键词： Energy management systems

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Modeling data, Information and knowledge for Security Protection of Tasks Scheduling Algorithms in Cloud Computing 23

Modeling Data, Information and Knowledge for Security Protec...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Linxuan, Xin Jiantao, Zhou Lei, Yu Inner Mongolia University Department of Computer Science Inner Mongolia Hohhot China

ISBN: (纸本)9781665494571

As a key element in cloud computing, with the increasing uncertainty and complexity, task scheduling becomes more and more import. How to perform cloud scheduling effectively while safeguarding the data and information meets its bottleneck that prevents cloud computing from moving forward. In order to protect the data under being processed, this essay raises an attribute-based outsourced encryption. which enables users to pass the data on shared equipment while making the data more invisible for hackers. Meanwhile, based on the task scheduling method of deep reinforcement learning (DRL), through intention analysis, consider the user's optimization preferences in task execution, and realize the interaction with the user. reasonable scheduling is performed according to the sensitivity of the data in the task and the credibility of the cloud server. It can be seen from the test results that the algorithm has significant superiority and versatility, can improve the efficiency and security of task scheduling, and has better performance and load balance. © 2021 IEEE.

关键词： Cloud computing Uncertainty Sensitivity Smart cities Scheduling algorithms Reinforcement learning Scheduling

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Enhancing Rice Crop Health Assessment: Evaluating Disease Identification with a CNN-RF Hybrid Approach

Enhancing Rice Crop Health Assessment: Evaluating Disease Id...

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2024 international conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Govindharaj, I. Rajput, Kapil Garg, Navin Kukreja, Vinay Sharma, Rishabh Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India Graphic Era Hill University Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319019

This research builds on a new method by using a convolutional neural network (CNN)-random forest (RF) hybrid model to augment the assessment of rice crop health. Aimed at addressing the critical challenge of disease identification in rice crops, the model focuses on the classification of four prevalent diseases: rust, Hispa, rust, and mildew. Making use of the combined abilities of CNN for feature extraction and RF for classification, the suggested hybrid model exhibits a higher accuracy and efficiency rate compared to the previous disease diagnosis methods and single machine learning (ML) models. The approach employed an extensive dataset of annotated rice crop images, the study enumerates the intricate process that features data collection, data preprocessing, feature extraction, and classification. The CNN-RF hybrid model scored a 93.5% accuracy rate, far more effective than the standalone CNN and RF models, which were at 88.2% and 86.7% respectively. This revolution manifests the prospects that exist with the blending of different machine-learning techniques that completely revitalize agricultural disease identification processes. The issue raised by this research is very important for existing agricultural practices, and even more so when we consider the issue of rice cultivation techniques in particular. The machine learning model performs an essential function of early and accurate disease detection which decreases losses of crops and thereby nutrition security across the world. This study is not only a groundbreaking step in the AI application for agriculture but also paves the way for a new research alley to build more effective and universal disease identification systems. This research brings to the fore the symbiosis of computational invention with agricultural know-how, which presents scalable prescriptions to address one of the key problems in world agriculture. This research adds to the growing body of knowledge on the use of machine learning in enhan

关键词： Diseases

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MSR: An Efficient Model for Personalized Movie Recommendation Based on Attention and Gating Mechanism 19

MSR: An Efficient Model for Personalized Movie Recommendatio...

引用

19th international conference on Natural Computation, Fuzzy systems and knowledge Discovery, ICNC-FSKD 2023

作者： Liu, Lei Zhu, Jie Mi, Jia Li, Jing Cao, Xinyu Wang, Haitao Beijing University of Technology Faculty of Science Beijing China Beijing Institute for Scientific and Engineering Computing Beijing University of Technology Beijing China Fundamental Standardization China National Institute of Standardization Beijing China

ISBN: (纸本)9798350304398

Personalized recommendation is an important technical means to deal with information overload and meets users' personalized customization needs. In response to the insufficient extraction of user historical features and cold start problem in traditional movie recommendation systems, we introduce an effective personalized movie recommendation model named Movie join with Starring and Ratings (MSR), based on attention and gate mechanisms. The paper uses multi-head attention mechanism to capture the intrinsic relationship between various data fields in user viewing records and obtains user features through the Basic Information-Rating Joint Attention Network (BRJA);the gate mechanism is used to integrate the basic information and reputation rating of the movie into the movie representation vector, obtaining candidate movie features. The model can provide good recommendations even with limited user information, effectively addressing the cold start problem. Comparative experiments on the public dataset Movie Lens and ablation experiments on key modules have demonstrated the effectiveness of the MSR model. © 2023 IEEE.

关键词： Recommender systems

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Optimization Techniques used in Heart Disease Prediction: A Systematic Analysis

Optimization Techniques used in Heart Disease Prediction: A ...

引用

2023 international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Behki, Priyanka Pal, Rishi University Institute of Engineering Chandigarh University Dept. of Computer Science and Engineering Punjab Gharuan India

ISBN: (纸本)9798350344387

Today, heart disease is one of the leading causes of death around the world, making it a top priority in healthcare systems everywhere. Predicting cardiac issues with precision is crucial for facilitating prompt medical attention. There is great potential for improved decision-making and data-driven forecasts in healthcare with the application of Particle Swarm Optimization (PSO) in conjunction with machine learning. By utilizing numerous algorithms, this method equips both patients and doctors to intervene early and avoid sickness. It's important to remember that the parameters you pick for your optimization strategy can have a significant impact on how well these models work. This research compares three popular optimization techniques - Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization - in the context of predicting cardiovascular disease. The fundamental goal of this study is to compare and contrast several optimization strategies, such as Feature Selection, Bayesian optimization, Evolutionary algorithms, and their hybrids, and other tried-and-true approaches from current scholarly literature. The overarching goal of these studies is to develop concepts for better heart illness diagnostics. The research also compares other Machine Learning methods developed to solve specific optimization problems. It is hoped that the knowledge gathered from this study would encourage further study into optimizing methods for cardiac illness diagnostics and the development of more efficient preventative measures. © 2023 IEEE.

关键词： data mining feature selection Heart disease Machine learning Optimization techniques

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Linking data Sovereignty and data Economy: Arising Areas of Tension 17

Linking Data Sovereignty and Data Economy: Arising Areas of ...

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17th international conference on Wirtschaftsinformatik, WI 2022

作者： Lauf, Florian Scheider, Simon Bartsch, Jan Herrmann, Philipp Radic, Marija Rebbert, Marcel Nemat, André T. Schlueter-Langdon, Christoph Konrad, Ralf Sunyaev, Ali Meister, Sven Fraunhofer Institute for Software and Systems Engineering ISST Healthcare Dortmund Germany TU Dortmund Chair of Industrial Information Management Dortmund Germany Department of Economics and Management Karlsruhe Germany Fraunhofer Center for International Management and Knowledge Economy IMW Corporate Development in International Competition Division Leipzig Germany Witten/Herdecke University Institute for Digital Transformation in Healthcare GmbH Witten Germany T-Systems International GmbH Digital Solutions - Data Intelligence Hub Frankfurt Germany Claremont Graduate University Drucker School of Management Claremont United States KASTEL Security Research Labs Karlsruhe Germany Witten/Herdecke University Faculty of Health School of Medicine Witten Germany

In the emerging information economy, data evolves as an essential asset and personal data in particular is used for data-driven business models. However, companies frequently leverage personal data without considering individuals’ data sovereignty. Therefore, we strive to strengthen individuals’ position in data ecosystems by combining concepts of data sovereignty and data economy. Our research design comprises an approach to design thinking iteratively generating, validating, and refining such concepts. As a result, we identified ten areas of tension that arise when linking data sovereignty and data economy. Subsequently, we propose initial solutions to resolve these tensions and thus contribute to knowledge about the development of fair data ecosystems benefiting both individuals’ sovereignty and companies’ access to data. © 2022 17th international conference on Wirtschaftsinformatik, WI 2022. All rights reserved.

关键词： Ecosystems

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Recurrent Neural Network for Chaotic Wind Speed Time Series Prediction 9

Recurrent Neural Network for Chaotic Wind Speed Time Series ...

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9th IEEE India international conference on Power Electronics, IICPE 2023

作者： Ahuja, Muskaan Saini, Sanju Electrical Engineering Deenbandhu Chhotu Ram University of Science and Technology Sonipat India

ISBN: (纸本)9798350307252

Accurate wind speed forecasting is essential for wind energy systems, but it's difficult due to the wind's uncertainty. In this study, short-time wind speed prediction is done on the basis of phase space reconstruction and using neural network techniques. The NREL (National Renewable Energy Laboratory) site collects the wind speed data. Firstly, the chaotic nature of the time series is ascertained to verify its short time predictability. Then training data is created by using the phase space reconstruction technique, i.e., using knowledge of embedding delay and embedding dimension. Using this data, two models of Artificial neural networks, i.e., Feedforward neural network (FFNN) and Recurrent Neural network (RNN) are trained to predict wind speed. Training accuracy of each model is compared on the basis of Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) & Symmetric Mean Absolute Percentage Error (SMAPE) metrics. Simulation results show the superiority of RNN (Recurrent Neural Network) over FFNN (Feed Forward Neural Network) for the prediction of wind speed. © 2023 IEEE.

关键词： Feedforward neural networks

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