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检索条件"机构=Computer Engineering and Application"

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An Energy Efficiency Enhanced through Duty Cycle based on Clusters in Wireless Body Area Network 3

An Energy Efficiency Enhanced through Duty Cycle based on Cl...

引用

3rd International Conference on Power Electronics and IoT applications in Renewable Energy and its Control, PARC 2024

作者： Rani, Anita Kumar, Rakesh Ram, Anant GLA University Computer Science of Engineering and Application Mathura India Computer Science Govt. Polytechnique College Jalaun India

ISBN: (纸本)9798350309409

Wireless body area network (WBANs) plays an important role in monitoring and helping the patient to maintain the health surveillance system. In this research paper, a variable duty cycle-based clustering algorithm is designed to increase the energy efficiency, by varying the awake and sleep period according to the criticality of the sense data that need to be transmitted. In the proposed approach, the network consists of two phases as LEACH Protocol. first steady phase and second setup phase. Firstly, applying the variable duty cycle and distance centroid has a maximum energy of alive node is considered as initial cluster heads (CHs). The successive CHs are recognized based on residual energy based on the threshold distance from the current cluster head (CH). After the formation of the clusters, the Cluster head aggregates the emergency data through alive nodes in Variable duty cycle values and sends it to the base station. In this algorithm no need to transmit the data in normal conditions of the patient (i.e.) they are dead nodes This technique provides more energy efficiency in the network. The performance evaluation of the proposed protocol Energy Efficient Duty Cycle-Based Cluster Approach (EEDCCA) is done using MATLAB tool and results are analyzed. The simulation result validates that the proposed technique increases the energy efficiency of the network and also Enhancement of Lifetime of the whole WBAN network. © 2024 IEEE.

关键词： Energy efficiency

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Challenge and Analysis of System Simulation Platform Construction for Product Resilient Design 42

Challenge and Analysis of System Simulation Platform Constru...

引用

42nd Chinese Control Conference, CCC 2023

作者： An, Baoran Zhou, Qinghe Wu, Huai Zeng, Sifeng Huang, Weijian Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9789887581543

The traditional and relatively static rigid product design mode is difficult to meet the diversified needs of users. Facing all levels of products in the whole equipment system, the development of resilient design mode will realize the rapid generation of equipment mission capability. The resilient design mode of high-value products needs to seamlessly carry the design rules of continuous evolution, and output the improvement, modification and change schemes rapidly. The construction of system simulation platform for product resilient design is discussed and analyzed in this paper from three aspects: challenges, countermeasures and ecology. A platform based software R&D (research and development) solution is presented, which can realize independent R&D of high value product resilient design software. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Simulation platform

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A Comparison of Machine Learning Methods for Forecasting Solar Energy Production

A Comparison of Machine Learning Methods for Forecasting Sol...

引用

2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Prajapati, Dharmistha K. Joshi, Ritesh A M Patel Institute of Computer Studies Ganpat University Master of Computer Application Mehsana India Prestige Institute of Engineering Management and Research Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Fundamentally, the study's findings show how important machine learning algorithms are to improving solar power forecasts and system optimization. The study illustrates how well the ANN algorithm predicts solar energy generation by utilizing large datasets and advanced computational methods. This insight opens the door for proactive maintenance plans and increased PV installation reliability in addition to helping energy firms and decision-makers make well-informed judgments. Looking ahead, the conclusions of this study provide insightful advice for industrial solar energy forecasting procedures and further research. Through the identification of ideal model configurations and performance measures, interested parties can enhance their forecasting abilities and optimize the solar resource usage. Additionally, the incorporation of machine learning algorithms into real-time monitoring systems facilitates quick decisions, enabling communities to maximize energy production and adjust to shifting environmental conditions. The coming together of data-driven insights, technical innovation, and sustainability imperatives offers a once-in-a-lifetime chance to quicken the shift to renewable energy. We can fully utilize solar energy and lessen the negative consequences of climate change by utilizing machine learning and predictive analytics. In addition to fostering economic growth and energy security, this revolutionary change also builds a more resilient, just, and sustainable society for coming generations. In conclusion, a major advancement in the global search for clean and sustainable energy solutions is the use of machine learning algorithms into solar energy forecasts. We can take advantage of these developments and work together to create a more innovative and environmentally friendly future for everybody. © 2024 IEEE.

关键词： Clean energy

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A value index calculation framework for value quantification 3

A value index calculation framework for value quantification

引用

3rd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Liang, Yan Xie, Xin Wan, Rui Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9781665490788

Along with the rapid development of digital economy, value-based management has attracted more and more attention from enterprises with its diversified advantages. Value-based management is based on value quantification, aims at value growth, and pursues long-term sustainable maximization of organizational value. The value quantitative evaluation needs to carry out full-scale data collection and calculation of element flowing in the process. When the data collection and calculation is scattered in digital applications of various businesses, there are often some problems such as irregular data collection, low calculation efficiency and difficult monitoring of indicators. Therefore, this paper exploits the value quantitative evaluation technology and designs a value index calculation framework, which includes digital element warehouse, digital component warehouse, and digital index warehouse. The proposed framework can provide the ability of element association management, process execution data collection and value index calculation and analysis. It can effectively improve the standardization, accuracy and efficiency of value quantitative evaluation and provide support for process optimization and innovation. © 2023 IEEE.

关键词： Warehouses

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Classification of Foods based on Ingredients

Classification of Foods based on Ingredients

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Guria, Sudipa Banerjee, Sudarshan Bhattacharjee, Suddhasattwa Paul, Swarup Halder, Sobhan Das, Priyanka Institute of Engineering and Management Dept. of Computer Application Kolkata India

ISBN: (纸本)9798350348217

Due to the rise of recipe sharing websites, it is now quite easy to find online cooking instructions that list ingredients and cooking techniques. The development of recipe suggestion systems has also been a focus of several recipe sharing websites. Little has been done to analyse the relationship between cuisines and ingredients, while the majority of contemporary food-related research works concentrate on recipe suggestion systems. The work presented here offers an insight into which cuisines are the most related and what are a cuisine's important elements because we know that each cuisine is unique and has a separate list of ingredients. While the cuisine of food has been regarded as the class label, the ingredients have been seen as the features. In order to categorise the cuisine of unlabeled data, this work is focused on selecting the most important ingredients using various feature selection strategies and classification approaches. This has been done to investigate the fundamental connection between ingredients and cuisine. © 2023 IEEE.

关键词： Classification (of information)

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Detection Approach of Malicious JavaScript Code Based on deep learning 3

Detection Approach of Malicious JavaScript Code Based on dee...

引用

3rd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Zheng, Liyuan Zhang, Dongcheng Xie, Xin Wang, Chen Hou, Boyuan Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9781665490788

Traditional machine learning methods for detecting JavaScript malicious code have the problems of complex feature extraction process, extensive computation, and difficult detection due to malicious code confusion, which are not conducive to the current requirements of JavaScript malicious code detection accuracy and real-time. This paper proposes a bi-directional long and short-time neural network (BiLSTM) based on an attention mechanism for JavaScript malicious code detection. Firstly, the obtained sample data will be deconfused, disambiguated, and vectorized to obtain the normalized data adapted to the neural network input. Second, the proposed algorithm is used to train the vectorized data and learn the abstract features of the JavaScript malicious code. Finally, the learned features are used to classify the code. The method is compared with deep learning methods and mainstream machine learning methods, and the results show that the method has a high accuracy and low false alarm rate. © 2023 IEEE.

关键词： Feature extraction

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Face Classification & Recognition Using Stacking Ensemble Learning with Principal Component Analysis Approach 3

Face Classification & Recognition Using Stacking Ensemble Le...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Devi, Shivali Saini, Jyoti Kaur, Harjinder Kaur, Harmanpreet Chandigarh University Computer Science & Engineering Punjab India Ssiet Dinanagar Computer Science & Engineering Punjab India Rayat Bahra University Computer Application Department Punjab India

ISBN: (纸本)9798350369083

Developing a high-level automated security system for identification/authentication has long been an active research subject in practically all fields. Traditional security systems utilize a key or a password to secure a process/product, whereas biometric security systems use a person's physical or behavioral attributes. Face patterns play a significant part in several potential recognition/authentication applications because of their uniqueness, universality, dependability, and stability. This study provides a face identification system that starts with eye detection and ends with face image recognition. AN ensemble stacking approach for face classification is presented in this study that combines various Machine learning (ML) algorithms with and without Principal Component Analysis (PCA). The proposed system is trained and tested on a well-known publically available Olivetti Face Dataset. The dataset is split into 75% train and 25% test dataset which is also transformed using PCA. The results show that the accuracy of each base model having PCA data is on par or better than the one without PCA. The paper also proves that the performance of the new stacking ensemble system results in an average or better performance than the other base models. For Olivetti face dataset, the proposed Stacked Ensemble Learning model's accuracy with PCA is 94%, which is on average than the accuracy of the other base models, including Neural Network (NN) (100%), Random Forest (92%), Support Vector Machine (SVM)(96%), Gaussian Naives Bayes(81%), K-Nearest Neighbour (KNN) (89%), and Logistic Regression (LR) (97%) respectively. © 2024 IEEE.

关键词： Authentication

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Urban Growth Estimation of Moradabad City Using Sentinel-2 Images

Urban Growth Estimation of Moradabad City Using Sentinel-2 I...

引用

2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Srivastava, Saurabh Ahmed, Tasneem Kumar, Rajeev Moradabad Institute of Technology Department of Computer Science & Engineering Moradabad India Integral University Department of Computer Application Lucknow India

ISBN: (纸本)9798350379716

Accurate monitoring and assessment are necessary due to the enormous challenges posed by rapid urbanization to sustainable development and resource management. This study utilizes Sentinel-2 satellite images of Moradabad City, located in Uttar Pradesh, India, to analyze urban growth through a machine learning approach. Sentinel-2 imagery is widely used for land cover monitoring, and images from 2017 and 2023 were selected to assess the rapid expansion of urbanization. Various techniques, including SVM, ANN and minimum distance (MD), were employed for land surface classification. Among these, SVM produced the most reliable results for estimating urban areas, based on both the Kappa coefficient (Kc) and overall accuracy (O.A.). The SVM estimates that in 2017 about 8.308% of the region was classified as an urban area, and from 2023 that number had increased to about 11.64%, which could be a serious concern for Moradabad city's residents. The motivation behind this study to explore the potential applications of machine learning methods for small-scale urban dynamics monitoring and analysis through satellite image analysis. The research advances knowledge of urbanization processes and offers insightful advice for sustainable urban growth in quickly expanding places. © 2024 IEEE.

关键词： ANN MD Sentinel-2 SVM Urban Area Estimation Urban Growth estimation

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Research on Performance Optimization of MySQL Database 3

Research on Performance Optimization of MySQL Database

引用

3rd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Gao, Pengxiang Chen, Qi Xie, Xin Wang, Chen Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9781665490788

The application system based on the MySQL database has gradually penetrated into various fields, and the performance of the MYSQL database has attracted more and more attention. This paper studies the performance optimization of MySQL databases from five aspects: SQL statement optimization, index optimization, table structure optimization, schema optimization, and server-side optimization. © 2023 IEEE.

关键词： Database systems

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An Ensemble Method for Data Classification Using State-of-the-art Methodologies 4

An Ensemble Method for Data Classification Using State-of-th...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Ray, Sujata Pradhan, Debasmita Ray, Niranjan Kumar Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Kiit Deemed to Be University School of Computer Application Bhubaneswar India

ISBN: (纸本)9798350349856

Over the past few decades, classification has consistently posed a significant computational challenge. This study presents an innovative ensemble classification model designed for data classification, drawing inspiration from Radial Basis Function, Extreme Learning Machine, Functional Linked Artificial Neural Network, and Artificial Neural Network. The study involved experimenting with various combinations of ensemble methods to construct an ensemble classifier. Remarkably, implementing the ensemble model using Radial Basis Function Network (RBFN), Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Functional Linked Artificial Neural Network (FLANN) yielded superior results when tested on benchmark datasets. The accuracy range of the ensemble method varies from 80% to 98% which is a good performance considering the diverge data sets used. © 2024 IEEE.

关键词： Classification (of information)

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