检索结果-内蒙古大学图书馆

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Vinoth Kumar, R. Malathy Manjula, M. Veerandeswari, J. Indumathy, M. Raghul, B.S. Rajiv Gandhi College of Engineering & Technology Information Technology Department Puduherry Kriumambakkam India Data Science and Business Systems Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350383522

Crop production is a vital aspect of human survival. Reducing global poverty heavily relies on increasing the robustness in crop yields. The quantity of crops harvested from a given agricultural land, also known as crop yields of that land is the important parameter that helps to increase the smartness in adopting novel agricultural plans. Measuring this parameter can be done through supervised machine learning techniques which helps with accurate predictions given the historical data of crop yield. This paper proposes an ideation, that uses Support Vector Machines (SVMs) because of its strength being accuracy, robust and flexible. SVM can predict reliable crop yield estimates that help to allocate resources and smart planning for agricultural activities. Also, SVMs are good enough to handle the imperfections in historical crop yield datasets, outliers, and inconsistencies. Furthermore, SVMs can bring out the relationships and any nonlinear patterns in the crop yields which are influenced by uncertain environmental factors over various seasons. This helps to identify the anomalies in the historical data. The anomalies could be exceptionally high yield or the failures which happen to be pinpointed as abnormal years. The identified outliers are excluded by the SVM, thus bringing out a prediction model that focusses on 'normal years'. By this, it is possible to represent more accurate inter-annual trends of crop yields avoiding the anomalies aiding the farmer to make better decisions for much more productivity in the crop yield. Based on the ideation, simulations are performed that showcase the success of the SVM in predicting good for the crop yields in inter-annual period. © 2024 IEEE.

关键词： Anomaly detection crop yield inter-annual variability SVM

来源：评论

学校读者我要写书评

暂无评论

Application of Bayes Rule for Precise Analysis of Class-Imbalanced Stroke data

Application of Bayes Rule for Precise Analysis of Class-Imba...

引用

2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Viswapriya, S.E. Rajeswari, D. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (纸本)9798350319200

The interpretability of stroke risk prediction is still lagging and it can be solved by using deep analysisofdata with the application of the Bayes rule. The prediction model has to be produced with accuracy, clarity, and comprehensibility without ambiguity, especially in machine learning and data analytics. The class imbalance problem is a common issue affecting the raw data, which means the imbalanced categorization of outcome feature value. In many fields, such as fraud detection, network intrusion detection, and medical diagnostics, this class imbalance issue is present where negative class samples outnumber positive class samples. If some datasets have class imbalance and imperfect issues, there will be no proper classification pattern. There are many resampling methods to address this class imbalance problem. However, these resampling methods can make an even distribution of classes by generating or omitting some samples, but theycannot handle imperfect data. The probabilistic analysis can lead to the identification of issues that exist in the dataset and provide direction to medical experts on dealing with the condition to avoid *** strength of applying the Bayes rule will provide a meaningful interpretation of stroke data. This paper investigates the Baye rule's significance in stroke data analysis. © 2023 IEEE.

关键词： Bayes rule Class imbalance probabilistic analysis Resampling Stroke data

来源：评论

学校读者我要写书评

暂无评论

Synthesizing Robust Observer-Based Guaranteed Cost Linear Parameter Variable Control for Artificial Pancreas systems Under Input Saturation

引用

IEEE Access 2024年 12卷 29971-29983页

作者： Hooshmandi, Kaveh Mobayen, Saleh Zhilenkov, Anton A. Arak University of Technology Department of Electrical Engineering Arak*** Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliou640301 Taiwan Saint Petersburg State Marine Technical University Department of Cyber-Physical Systems Saint Petersburg190121 Russia

The artificial pancreas (AP), commonly known as a closed-loop insulin delivery control system, consists of three primary components: a glucose sensor for measuring blood glucose concentration (BGC), a control algorithm responsible for determining the rate of exogenous insulin delivery (IDR), and an insulin infusion pump. In this paper, a novel approach is introduced for designing a guaranteed cost observer-based linear parameter variable (GC-LPV) control system for blood glucose levels in Diabetic Patients. The Bergman Minimal Model (BMM), which is widely used for understanding the glucose-insulin regulatory system, is described as a Quasi-Linear Parameter Varying (QLPV) system. The main innovation of the paper lies in presenting a new method that optimizes performance without considering input in the cost function and the concurrent design of LPV observer and LPV controller. One notable aspect of the design conditions is the incorporation of input saturation considerations during the design process. This inclusion leads to performance improvement by addressing the constraint of insulin injection effectively. Another significant contribution of the paper is its unified approach to transforming the problem into a convex optimization problem, incorporating Linear Matrix Inequalities (LMIs). The proposed adaptive dynamic output structure provides flexibility in controller design while ensuring stability and performance, even in the presence of parametric uncertainty. Simulation results confirm that the GC-LPV controller yields improved performance compared to state-of-the-art methods. © 2013 IEEE.

关键词： Glucose

来源：评论

学校读者我要写书评

暂无评论

Submersible Position Prediction and Identification Based on Hybrid Neural Network Algorithm and Monte Carlo Method 2

Submersible Position Prediction and Identification Based on ...

引用

2nd International Conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Xie, Lingyi Li, Xinqi Zhang, Mufan Southern University of Science and Technology Department of Mechanical and Energy Engineering Shenzhen China Southern University of Science and Technology Department of Information Systems and Management Engineering Shenzhen China Southern University of Science and Technology Department of Statistics and Data Science Shenzhen China

ISBN: (纸本)9798350386639

This paper proposes a method for predicting and identifying the position of a submersible using a hybrid neural network algorithm and the Monte Carlo method. Initially, we employ a hybrid neural network algorithm to establish a predictive model for ocean current parameters based on data from the Ionian Sea detection station. Additionally, we analyze the forces acting on the submersible and its motion to construct a physical model for the submersible's vertical movement. By integrating the effects of these two models, we can predict the three-dimensional trajectory of the submersible. Furthermore, to accurately analyze the potential location of the submersible after an accident, we utilize the Monte Carlo method and infinitesimal method, employing the K-means clustering algorithm to analyze the initial deployment points of the device. Based on the probability distribution, we select appropriate search methods. As indicated by the established probability function model, the probability of the submersible being covered decreases over time. The search results serve as adjustment parameters for the probability function model;the more search results obtained, the higher the probability of locating the submersible is. © 2024 IEEE.

关键词： Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

引用

3rd International Conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

VEMUT-Sub-Sampling: A Novel Method for Sparse Multivariate Time-Series Vehicle data

VEMUT-Sub-Sampling: A Novel Method for Sparse Multivariate T...

引用

2023 IEEE International Conference on Big data, Bigdata 2023

作者： Apel, Nadin-Katrin Antoniou, Constantinos Porsche Ag Department of Vehicle Quality Data Stuttgart Germany Technical University Munich Chair of Transportation Systems Engineering Munich Germany

ISBN: (纸本)9798350324457

In today's automotive industry, modern vehicles are equipped with numerous sensors to monitor the vehicle's state. Accurate prediction of component wear-and-tear requires the identification and alignment of the appropriate sensors. However, this task is complicated by the sheer number of sensor features, their intermittent logging activity, and non-uniform data. Furthermore, the number of vehicles deemed suitable for analysis is exceedingly limited. In this paper, we address the prevalent challenge of sparse, irregular, and multivariate time series data encountered in various domains, with a particular focus on the automotive industry. Conventional methods like imputation or interpolation, which attempt to estimate data points, are inapplicable when the dataset is limited in size, consists of highly irregular time spans, a large feature space, and data sparsity. To tackle these issues, we introduce a novel subsampling methodology for VEhicle MUltivariate Irregular Time series data (VEMUT). Utilizing VEMUT, multiple sub-samples generate an increased dataset size, allowing machine learning models to learn high-performing and generalizable models, even when the dataset is limited in size. We examine the performance of the resulting dataset from VEMUT's sub-sampling across three simple network architectures: a fully convolutional network, a fully convolutional network with attention, and a convolutional network with pooling. Our findings reveal over 99% accuracy for all architectures on the validation data, leading to a correct prediction of the wear-and-tear of a Porsche Taycan's air suspension module. These results highlight the potential of our approach to significantly enhance models capable of recognizing patterns in data and predicting future developments. Such advancements are especially vital for prognosing wear-and-tear of various components in modern vehicles, ultimately contributing to improved maintenance and cost efficiency. © 2023 IEEE.

关键词： automotive irregular multivariate time-series predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

An optimal hybrid cascade regional convolutional network for cyberattack detection

An optimal hybrid cascade regional convolutional network for...

引用

作者： Alqahtani, Ali Khan, Surbhi Bhatia Department of Networks and Communications Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Data Science School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Cyber-physical systems (CPS) and the Internet of Things (IoT) technologies link urban systems through networks and improve the delivery of quality services to residents. To enhance municipality services, information and communication technologies (ICTs) are integrated with urban systems. However, the large number of sensors in a smart city generates a significant amount of delicate data, like medical records, credit card numerics, and location coordinates, which are transported across a network to data centers for analysis and processing. This makes smart cities vulnerable to cyberattacks because of the resource constraints of their technology infrastructure. Applications for smart cities pose many security challenges, such as zero-day attacks resulting from exploiting weaknesses in various protocols. Therefore, this paper proposes an optimal hybrid transit search-cascade regional convolutional neural network (hybrid TS-Cascade R-CNN) to detect cyberattacks. The proposed model combines the hybrid transit-search approach with the cascade regional convolutional neural network to create an optimal solution for cyberattack detection. The cascade regional convolutional network uses a hybrid transit search algorithm to enhance the effectiveness of cyberattack detection. By integrating these two approaches, the system can leverage both global traffic patterns and local indicators to improve the accuracy of attack detection. During the training process, the proposed model recognizes and classifies malicious input even in the presence of sophisticated attacks. Finally, the experimental analysis is carried out for various attacks based on different metrics. The accuracy rate attained by the proposed approach is 99.2%, which is acceptable according to standards. © 2023 John Wiley & Sons Ltd.

关键词： Zero-day attack

来源：评论

学校读者我要写书评

暂无评论

Efficient allocation of independent gridlet on small, medium, and large grid

引用

Personal and Ubiquitous Computing 2023年第3期27卷 1029-1037页

作者： Rajeswari, D. Ramamoorthy, S. Srinivasan, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

Gridlet allocation in a computational grid environment is a major research issue to obtain not only the efficient gridlet allocation technique but also the time needed to obtain the efficient allocation technique. Grid computing networks have various nodes to process user jobs. To achieve the high performance of computational grid, task scheduling is an important issue. The users who are using services of grid systems are more cautious about time to complete their job. Hence, this work concentrates on gridlet allocation method used for time reduction by a genetic algorithm with the MapReduce programming model for independent tasks in computational grid. In computational grid environment, multi-objective problem formulation minimization of makespan and flowtime is considered. In this proposed technique, fitness function formulation for makespan and flowtime has been formulated mathematically. The genetic algorithm with the MapReduce programming model is implemented using MapReduce written in Java and then combined with GridSim. The experimental outcome with regard to time needed, flowtime, and makespan clearly reveals that the genetic algorithm with the MapReduce model effectively optimizes time, makespan, and flowtime in computational grid environment. A comparative study of performance efficiency among genetic algorithm with the MapReduce and sequential genetic algorithm (SGA) and parallel genetic algorithm (PGA) depicts the usefulness of the model. The execution time achieved by GA with the MapReduce model in small grid is 10.48 s, medium grid is 18.76 s, and large grid is 33.73 s. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： MapReduce

来源：评论

学校读者我要写书评

暂无评论

Enhancing Agricultural Yield Predictions with Real-Time IoT Sensor data and Machine Learning Integration 5

Enhancing Agricultural Yield Predictions with Real-Time IoT ...

引用

5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Chandiraprakash, N. Chinnasamy, A. Ashok, M. Malla Reddy College of Engineering Department of Artificial Intelligence and Machine Learning Hyderabad India School of Computing Department of Data Science and Business Systems SRMIST kattankulathur campus Chennai India Malla Reddy College of Engineering Hyderabad India

ISBN: (纸本)9798331518097

This research extends previous studies on the effects of climate change on agricultural yield predictions by integrating advanced Machine learning (ML) methods with real time environmental sensing data. It builds on the basic usage of Long Short-Term Memory (LSTM) and Random Forest (RF) methods by employing real time data streams from IoT based field sensors that measure soil moisture, nutrient levels and pest activity. The aim is to enhance the prediction model's adaption to changing climatic circumstances, therefore increasing their utility in agricultural management. This study proposes a dynamic adaptation feature that uses real time data to make timely adjustments to crop management predictions. Moreover, the integration of Convolutional Neural Network (CNN) with LSTM is being investigated to increase the spatial and temporal accuracy of these predictions. By integrating conventional climatic data with detailed micro environmental elements from IoT sensors, this method obtains a aspects which affects the crop growth. This study also focuses on developing a userfriendly interface that allows farmers to easily access predictive developments and recommendations, allowing for more informed decision making. This technique is predicted to make substantial advances in precision agriculture, giving more effective methods for regulating agricultural productivity in dynamic climatic variability. These results may improve food security and sustainability by optimizing resource utilization and reducing the environmental impact of farming operations. © 2024 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

High efficient net gain and low noise figure based vertical cavity semiconductor optical amplifiers for wavelength division multiplexing applications

引用

Journal of Optical Communications 2025年第2期46卷 247-256页

作者： Prabu, Ramachandran Thandaiah Balasubramanian, Arunsundar Balakrishnan, Nithyasundari Jebanazer, Jeneetha Kandasamy, Mohana Sundaram Govindaswamy, Nishanthi Mahmoud, Rashida Maher Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Tamilnadu Chennai India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai93104 India Department of ECE S.A. Engineering College Tamilnadu Chennai India Department of ECE Panimalar Engineering College Tamilnadu Chennai India Department of CSE R.M.D. Engineering College Kavarapettai Tamilnadu Chennai India Department of CSE R.M.K. Engineering College Kavarapettai Tamilnadu Chennai India Sohag Institute of Technology Sohag Egypt

This paper has demonstrated the high efficient net gain and low noise figure based vertical cavity semiconductor light amplifiers for wavelength division multiplexing applications. Previous study on the chip reflective gain variations versus SOA current under temperature effects is clarified. We have transferred light semiconductor amplifiers for wavelength multiplexing schemes applications. Amplifier output power is demonstrated with bias current and amplifier active layer region length variations based input signal power of 3, 6.5, 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. The amplifier output noise variations are clarified against the bias current and amplifier active layer region width variations based input signal power of 3, 6.5, 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. The output OSNR variations are studied clearly and deeply against the bias current and amplifier active layer region length variations based input signal power of 3, 6.5, 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. The signal loss is demonstrated versus both active layer width/length and temperature based optimum 30 % gallium core dopant ratio and 28 % arsenide cladding dopant ratio at optimum input signal power of 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. SOA amplifier output power can be enhance with the management of both bias current and input signal power and the reduction of active amplifier length. The amplifier output noise can be enhance with the management of both bias current and input signal power and the reduction of active amplifier width. Output OSNR system can be improved with the management of both bias current and input signal power and the reduction of active amplifier length. The SOA amplifier gain can be improved with the management of both bias current and input signal power and the reduction of active amplifier length. The SOA amplifie

关键词： Noise figure

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：