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检索条件"任意字段=2015 2nd International Conference on Engineering Technology and Application"

共 27583 条记录，以下是241-250 订阅

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Investigation into a Short-term Electric Load Prediction Model Leveraging K-means Clustering Enhanced Convolutional Neural Networks 2

Investigation into a Short-term Electric Load Prediction Mod...

引用

2nd IEEE international conference on Sensors, Electronics and Computer engineering, ICSECE 2024

作者： Tang, Yizhong Cheng, Yueliang Wang, Chuanxiu Dong, Xue Deng, Xingyan Luo, Yingdong Nari Technology Nanjing Control System Co. Ltd. Nanjing China

ISBN: (纸本)9798350373646

The K-means clustering and convolutional neural network (CNN) integrated model for short-term power load forecasting is designed to enhance the precision of predicting electricity consumption in power systems through the application of state-of-the-art machine learning methodologies. In this paper, K-means algorithm is used to cluster the historical power load data, in order to excavate the inherent structure and regularity of load patterns, and divide them into multiple clusters with similar characteristics. Then, the deep convolutional neural network is designed and trained according to the different load features obtained from each cluster, so as to better capture the local features and time series dependencies in the time series data. The model combines unsupervised preprocessing technology and supervised learning method to improve the accuracy of short-term power load forecasting and reduce the forecasting errors caused by the diversity and uncertainty of load changes. The practical effectiveness and superiority of the proposed model in tackling real-world power system load forecasting tasks are experimentally substantiated. © 2024 IEEE.

关键词： Convolutional neural networks

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Urban Traffic Planning Analysis and Route Optimization 2

Urban Traffic Planning Analysis and Route Optimization

引用

2nd international conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Kumar, Agrima Tambe, Varad Raj, T.R.Aakash Thomas, Mariam Kiruthika, M. Agnel Charities Fr. C. Rodrigues Institute of Technology Department of Computer Engineering Vashi Navi Mumbai India

ISBN: (纸本)9798350368413

Traffic congestion is a persistent problem that plagues urban areas worldwide, causing frustration for commuters, significant economic losses, and adverse environmental impacts. As cities continue to grow and populations increase, traditional traffic management solutions alone are proving insufficient to tackle the mounting challenges. To address these issues, this work proposes to optimize urban traffic navigation. This work delivers optimized routing solutions for multiple stops in limited time frame which can be used significantly in logistics and journey planning i.e. Multi-point route optimization. By considering both real-time traffic information and historical data, the application ensures that deliveries and journeys, in general, are conducted with maximum efficiency in terms of distance and time. Additionally, the web application actively suggests alternative routes, diminishing congestion in specific areas and contributing to a more equitable distribution of traffic load. © 2024 IEEE.

关键词： Traffic congestion

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Predicting Epileptic Seizures with Precision A Comprehensive Study of ML Algorithms 2

Predicting Epileptic Seizures with Precision A Comprehensive...

引用

2nd IEEE international conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Ranjani, M. Ramya, G. Divya, G. Deepa, N. Lalitha, B. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Chennai India Kpr Institute of Engineering and Technology Department of Eee Coimbatore India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384277

Epilepsy, a complex neurological disorder, poses a significant challenge in the realm of healthcare. Encephalography (EEG) stands as a widely adopted clinical tool for recording the intricate electrical activity within the brain. This research delves into the application of machine learning techniques for the prediction of epileptic seizures using EEG signals obtained from the UCI Machine Learning Repository. Our study presents a comprehensive evaluation of various ML algorithms, including the SVM Classifier, RF Classifier, Gaussian Naive Bayes, K-Nearest Neighbors, and others. Significantly, the Support Vector Classifier and Random Forest Classifier achieved remarkable accuracies of 0.98, suggesting their potential for precise prediction. Meanwhile, Gaussian Naive Bayes and K-Nearest Neighbors demonstrated robust predictive capabilities, with accuracies of 0.96 and 0.93, respectively. This research contributes substantially to the field of epilepsy prediction, shedding light on the effectiveness of diverse machine-learning models for early seizure detection. These findings hold promise for enhancing patient care and improving their quality of life, marking the beginning of an exciting journey towards refined epilepsy management © 2024 IEEE.

关键词： Adversarial machine learning

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Review On Text Classification Using Improved Deep Learning Models 2

Review On Text Classification Using Improved Deep Learning M...

引用

2nd international conference on Computer, Communication and Control, IC4 2024

作者： Gaikwad, Rajni Mulchandani, Mona Thakur, Reena Jhulelal Institute of Technology Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350387933

Modern deep learning models have exhibited better performance than conventional machine learning methods across a range of text classification tasks, encompassing natural language inference, sentiment analysis, news categorization, and question answering. The application of text classification in natural language processing extends to tasks like spam detection, news categorization, information retrieval, sentiment analysis, and intent assessment. Traditional text classifiers utilizing machine learning techniques encounter challenges such as sparse data, dimensionality issues, and limited generalization capabilities. In contrast, classifiers based on deep learning effectively tackle these limitations, eliminating the need for intricate feature extraction processes, and providing improved learning capabilities along with enhanced prediction accuracy. One notable instance of Convolutional neural networks are used in this way (CNNs). This paper delineates the text classification process, emphasizing the application of deep learning methodologies for text classification. © 2024 IEEE.

关键词： Sentiment analysis

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Enhancing Payment Security: Face Recognition Integration with Razorpay 2

Enhancing Payment Security: Face Recognition Integration wit...

引用

2nd international conference on Artificial Intelligence and Machine Learning applications, AIMLA 2024

作者： Anitha, K. Vishal, J.S. Prabakar, B. Vaseekaran, R. M. Kumarasamy College of Engineering Department of Information Technology Karur India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

A financial institution that offers mobile banking enables its clients to use a mobile device and software that the organization provides to carry out financial activities, including electronic fund transfers and bill payments. Even though there are benefits to mobile banking, one of the most sensitive jobs that the average user does when using the internet is security of financial transactions. This is a matter that should be properly addressed. Today's mobile banking uses a range of authentication techniques, each with advantages and disadvantages. Banks are using a number of measures to improve client security and increase the security of their transactions. Increasing the use of biometrics for identity is one such tactic. Information assurance is the primary application of biometrics, which is defined as the amalgamation of scientific and technical elements utilized for biological authentication. We may implement the Android mobile banking application in this paper. Using the Grassmann algorithm approach, we can apply facial recognition technology and PIN based templates. The user can access the system and carry out banking operations after two factor authentication. According to experimental findings, the suggested application offers better efficiency in situations that occur in real time. © 2024 IEEE.

关键词： Biometrics

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Enhancing Predictive Accuracy with Machine Learning and IoT Integration in Energy Management 2

Enhancing Predictive Accuracy with Machine Learning and IoT ...

引用

2nd international conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Balaji, S. Bharathi, S. Prasanna Balaji, T.S. Hariharan, R. Subin, P. Glaret Faculty of Engineering and Technology Srm Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Vadapalani Campus Chennai India Vellore Institute of Technology School of Electronics Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electrical and Electronics Engineering Tamilnadu Chennai India Srm Institute of Science and Technology Department of Ece Vadapalani Campus Tamilnadu Chennai India

ISBN: (纸本)9798350364699

The combination of IoT and ML has the capacity to radically change the energy management as it would increase predictability measurements many folds. This study seeks to analyse the relationship between the use of IoT and ML in enhancing the prediction of energy consumption and the enhancement of the management strategies. Therefore, using the data obtained from the IoT sensors in real-time and applying different ML algorithms, the goal of the study is to design sound machine learning models for EMS. The method used entails data acquisition from smart meters and environmental sensors and the initial data cleaning process and applies regression, classification, and clustering algorithms. Findings show that energy consumption can be predicted more accurately through the machine learning models, especially the Polynomial Regression. Thus, the role of data quality and feature selection in the improvement of a model is shown in the study. The results show that the application of IoT together with the application of ML technologies make great sense and have significant advantages mainly in improving the productivity and efficiency, cutting costs and achieving better control of energy consumption. Thus, this work will be instrumental in the continual enhancement of energy management and contribute to the broader literature through the findings on suitable modeling approaches and the influence of IoT data on superior prognosis. In the subsequent studies, more efforts will be made on enhancing the aforementioned models and expanding the application fields on energy management practices. © 2024 IEEE.

关键词： Data accuracy

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Research on Real-Time Data Transmission and Signal Processing System of CIM Practical Training Teaching Platform Based on 5G Network 2

Research on Real-Time Data Transmission and Signal Processin...

引用

2nd IEEE international conference on Image Processing and Computer applications, ICIPCA 2024

作者： Cheng, Xuemei Zhang, Hao Shandong University of Engineering and Vocational Technology Jinan250000 China Poly Shandong Real Estate Group Corporation Limited Jinan250000 China

ISBN: (纸本)9798350360240

In order to solve the problems of 'digital signal processing', such as many common knowledge points, difficult teaching, difficult visual teaching, and so on, a software platform for 'signal processing' open teaching is built based on Visual C++6.0. This system is to cooperate with the teaching content of CIM, but also to improve the practical ability of students, so that they can more easily and quickly understand some of the more complex digital signal theory. The timing of the beam position signal is optimized based on the existing down sampling method, and the digital AGC and slow application signal processing modules are completed. We need to get turn-by-turn, quick application, and track closing data simultaneously. The experiment shows that the system can obtain the multi-frequency position information including the actual running state well. The advantages of this system are easy to control parameters, easy to use, and strong expansibility. This project plans to further improve the system in the future research work so as to achieve better teaching and practical application purposes. © 2024 IEEE.

关键词： 5G mobile communication systems

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Sports Training Data Analysis and Model Research Based on Artificial Intelligence 2

Sports Training Data Analysis and Model Research Based on Ar...

引用

2nd IEEE international conference on Electrical, Automation and Computer engineering, ICEACE 2024

作者： Zelong, Shao Chunlan, Fan Sichuan University of Science & Engineering School of Physical Education Zigong City China

ISBN: (纸本)9798350368208

This paper proposes a sports training data analysis and model research method based on artificial intelligence, focusing on the application of transfer learning algorithm in sports training. Through the collection and analysis of a large amount of sports training data, combined with time series analysis technology, a prediction model suitable for a variety of sports is designed and optimized. The model uses the transfer learning algorithm to transfer existing training experience between different sports, which improves the prediction accuracy and generalization ability of the model under data scarcity. The simulation results show that the prediction model using transfer learning can significantly improve the accuracy of training effect prediction in time series data analysis, and the error is controlled within 5%. In addition, by comparing different algorithms, the advantages of transfer learning algorithm in data sparse environment are proved. The research in this paper not only provides new data analysis and model optimization methods for sports training, but also provides theoretical support for the widespread application of artificial intelligence in the field of sports in the future. © 2024 IEEE.

关键词： Data accuracy

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2024 2nd international conference on Design Science, ICDS 2024

2024 2nd International Conference on Design Science, ICDS 20...

引用

2nd international conference on Design Science, ICDS 2024

ISBN: (纸本)9798350376289

The proceedings contain 22 papers. The topics discussed include: application of machine learning algorithms in enterprise supply chain demand forecasting;AIGC and future environmental design in the Anthropocene perspective;conceptual design of a device for evaluating the tribological performance of the high-speed elevator rolling guide shoe;a design knowledge transfer method based on cross-domain knowledge network;design of air-cooled heat dissipation system for lithium-ion batteries;vibration and noise reduction design of EHA pipeline based on Multiphysics coupling;research on the motion poster design based on eye tracking technology;usability evaluation of warning modalities based on flight environment;and mechanical system design for small lunar jumping robot based on prototyping.

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application of Blockchain technology in Ontology Management for Enhanced Data Security in Web Platforms 2

Application of Blockchain Technology in Ontology Management ...

引用

2nd IEEE international conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Aswini, Kilaru Rana, Ajay Reddy, Uma Nagpal, Amandeep Praveen Mohammed, Abdulkareem. M. Institute of Aeronautical Engineering Dundigal Hyderabad India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

Blockchain technology presents a transformative approach to ensuring data integrity, security, and trust in web platforms. This research explores the innovative application of blockchain as an underlying layer in ontology management systems to revolutionize data security and privacy. Ontologies, as structured frameworks for organizing information, are pivotal in enhancing understanding and interoperability among diverse web technologies. Integrating blockchain with ontology management involves creating immutable and verifiable records of ontological changes, access, and transactions. This ensures a transparent and secure mechanism for handling data, addressing the vulnerabilities inherent in centralized and less secure data management systems. The paper delves into architectural design, outlining how blockchain's decentralized nature and cryptographic algorithms contribute to robust ontology management. It presents a comparative analysis of existing data security mechanisms and illustrates the superior safeguards afforded by the blockchain-ontology model. Furthermore, the research highlights case studies from web platforms that have integrated this model, demonstrating notable improvements in data security, trust, and efficiency. The implications of this integration extend beyond enhanced security;they include improved data provenance, quality, and a reduction in fraudulent activities. This paper aims to catalyze further innovation and adoption of blockchain in web technologies, particularly in ontology management, to create a more secure and trustworthy digital ecosystem. © 2024 IEEE.

关键词： Data integrity

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