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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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Research on Optimization of Network Mass Event Preplanning Based on machine learning 3

Research on Optimization of Network Mass Event Preplanning B...

引用

2023 IEEE 3rd international conference on Data Science and computer application, ICDSCA 2023

作者： Xinran, Wei Lei, Pan Fengyun, Lu Tao, Zhou People's Police University of China Langfang065000 China

ISBN: (纸本)9798350341546

Effective management of 5G resources requires careful arranging of working numerology lists and range conveyance as per the necessities of the ongoing traffic. To lessen signal above, any reconfigurations made to oblige the ongoing traffic design should be kept to a base. This paper proposes the pre-arranging of numerology profiles as a solution for this issue and fosters a numerical improvement model for their pre-arranging. By looking at the prerequisites and limitations that are much of the time present in a specific remote correspondence climate, it is expected to make numerology profiles. One of the profiles will then, at that point, be utilized in accordance with the ongoing clients and traffic design. The model empowers the enhancement of blended numerologies in future 5G organizations under any remote correspondence situation, with explicit assistance necessities and weaknesses, and under any traffic situation. By involving attainable rate execution as one of the key qualities, we look at the blended numerology and small scale space methods in different settings. We employ Cyclic Prefix-Orthogonal Frequency Division Multiplexing (CP-OFDM) as a suitable benchmark and show that Universal Filtered Multicarrier (UFMC) outperforms CP-OFDM. When compared to a non-optimized technique, the proposed optimisation model can achieve satisfaction levels of 60-100% while reducing the overall number of numerology indexes in use. © 2023 IEEE.

关键词： Cyclic Prefix-Orthogonal Frequency Division Multiplexing (CP-OFDM) mixed numerologies Universal Filtered Multicarrier (UFMC) wireless communication

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Research on machine learning strategy based on voting model 3

Research on machine learning strategy based on voting model

引用

3rd international conference on computer Science and Communication Technology, ICCSCT 2022

作者： Du, Shipei Ding, Wenhui Yang, Dongjie Yang, Lei School of Finance and Economics Guangdong University of Science and Technology Dongguan China School of Computer Science Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9781510661240

In this research, machine learning algorithms such as decision tree, random forest, and BP neural network are used to predict a certain dataset, and then a voting prediction model is built based on the above three machine learning algorithms. To verify the performance of this voting model, we introduced confusion matrix and F1 score to evaluate the effectiveness of machine learning. The experimental results show that the performance of the machine learning strategy based on the voting model outperforms that of a single machine learning algorithm and that adjusting the voting weights of a single algorithm can also affect the performance of the whole model. This result is well worth further study. © 2022 SPIE.

关键词： Decision trees

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Exploring machine learning applications for Additive Manufacturing 3rd

Exploring Machine Learning Applications for Additive Manufac...

引用

3rd Biennial international conference on Future learning Aspects of Mechanical Engineering, FLAME 2022

作者： Chouhan, Kshitij Gupta, Sumit Chaudhary, Vijay Gupta, Pallav Kumar, Sundeep Department of Mechanical Engineering ASET Amity University Uttar Pradesh Noida201313 India Management Studies Engineering College Ajmer Rajasthan305025 India

ISBN: (纸本)9789819918935

The use of data and algorithms to open the ways that human learn, which gradually increase the accuracy, is all considered under a branch of computer science and machine learning known as machine learning (ML). This is a rapidly growing field of data science majorly includes the use of trained algorithms and statistical methods to make classifications, which uncovering basic insights of data mining projects. AM is a relatively new technology that has the potential to drastically alter the future of digital manufacturing. In recent years, machine learning (ML) has attracted a lot of attention in AM because of its amazing performance in data tasks including classification, regression, and clustering. One of the primary justifications is this very fact. The complexity of production systems leads to a variety of problems with regard to design principles, standardization, and quality control. There is a growing need, however, for more sophisticated and superior products. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： 3D printing

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machine learning Approaches for Prediction of Nutrition Deficiency among Women of Different Age Groups 3

Machine Learning Approaches for Prediction of Nutrition Defi...

引用

3rd international conference on Innovations in computer Science and Software Engineering, ICONICS 2022

作者： Ali, Javeria Waseemullah Khan, Masood Ahmed Khan, Najeed Ahmed NED University of Engineering and Technology Dept of Mechanical Engineering Karachi Pakistan NED University of Engineering and Technology Dept of Computer Science and Information Technology Karachi Pakistan

ISBN: (纸本)9781665461412

Malnutrition has been a major health concern widely especially when it comes to Asian women. Due to the lack of access to healthcare facilities for the women of underdeveloped and developing countries, the women of these regions suffer the most. For this purpose, an effort is made to predict the nutritional status of the women in one such country Pakistan. The research aims to build an intelligent model for the prediction of nutritional deficiency among women. machine learning algorithms namely Support vector machine, random forest, Logistic Regression, KNN, and Naïve Bayes have been used to build the nutritional predictor in relevance to Pakistani women. The result shows that the Random Forest Model has the highest accuracy and is the most suitable one for future predictions. The model would enable us to know the health condition in particular of Pakistani women. The testing of the model on a similar dataset has further confirmed the reliability factor. © 2022 IEEE.

关键词： Forecasting

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Solar Energy Prediction using machine learning with Support Vector Regression Algorithm 3rd

Solar Energy Prediction using Machine Learning with Support ...

引用

3rd EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022

作者： Kasireddy, Idamakanti Padmini, K. Ramarao, R.V.D. Seshagiri, B. Rani, B. Venkata Naga Vishnu Institute of Technology Bhimavaram India

ISBN: (纸本)9783031289743

machine learning is almost applied in every field such as engineering, science, medical etc. In this work, the concept of machine learning has been adopted for predicting solar energy. The solar Energy is widely known renewable energy due to its massive advantages. Solar energy prediction can help to determine the energy consumption beforehand and plays a major role in future planning. The grid operators are facing hardships because of unreliable weather conditions, which lead to the reduction in solar energy output. So, they are unable to satisfy the needs of consumers. Our proposed solution intends to make prediction models by using machine learning algorithms such as Linear Regression, Lasso Regression, Ridge Regression and Support Vector Regression (SVR). These algorithms use past weather data including temperature, dew, wind, cloud and visibility. Based on these data, analysis has been carried out in Jupyter Notebook. From the analysis, it has found that, SVR algorithm performed well when compared with other algorithms. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Solar energy

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Research on Traffic sign recognition based on CNN Deep learning Network 3

Research on Traffic sign recognition based on CNN Deep Learn...

引用

3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Qiao, Xiaoxuan Beijing University of Technology Beijing China

With the continuous development of social economy, the road traffic system as one of the basis of national economy is also increasingly developed, which is followed by the traffic safety problems are increasingly prominent. Among them, driving violations such as not obeying traffic signs are the main cause of traffic accidents. In this research, Traffic Sign Recognition (TSR) has become a hot research topic. The research status and application field of traffic sign detection and recognition technology are introduced and analyzed in detail, and the improvement of traffic sign database is introduced. After that, the application of CNN in image processing and traffic sign recognition is introduced, and the existing problems in traffic sign recognition are analyzed. The CNN deep learning network used in this paper can conduct training while identifying targets, effectively improve the recognition accuracy of traffic signs under complex natural conditions and reduce the time consumption. © 2023 Elsevier B.V.. All rights reserved.

关键词： Traffic signs

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application of Deep learning Based Longitudinal Tear Detection in Intelligent Main Transportation 3

Application of Deep Learning Based Longitudinal Tear Detecti...

引用

3rd international conference on computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2023

作者： Yu, Zhiqiang Automation Co. Ltd. Changzhou213003 China Middling Coal Science and Engineering Group Changzhou Research Institute Co. Ltd. Changzhou213003 China

ISBN: (纸本)9798350372052

The current traditional device for detecting longitudinal tearing of belts has a low level of intelligence, and the triggering mechanism is inherently prone to missed and false detections. This paper designs a longitudinal tear detection system based on deep learning, which takes YOLOv5s model as the deep learning model and low power consumption edge computing module as the carrier. The industrial intrinsic safety camera is used to obtain the image, which is transferred to the edge computing module for tear feature detection. The longitudinal tear length is calculated by fusing the belt speed sensor data, and only the external detection results are finally output. When the detected longitudinal tear length exceeds a certain threshold, the belt conveyor collaborative controller will control the sound and light alarm to give an alarm, and at the same time issue a shutdown command to the specific single controller. In addition, the edge computing module uploads the video stream and alarm data after image processing to the control platform for reference. This system deepens the intelligence level of longitudinal tear detection for the main conveyor belt in coal mines, ensures the safety of the coal production process, improves production efficiency, and reduces economic losses. © 2023 IEEE.

关键词： Belt conveyors

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application and Research of Artificial Intelligence Embedded Service 3

Application and Research of Artificial Intelligence Embedded...

引用

3rd international conference on Artificial Intelligence, Robotics and Control (AIRC)

作者： Li, Jingjing Li, Qingda Heilongjiang Bayi Agr Univ Lib Daqing Peoples R China Heilongjiang Bayi Agr Univ Daqing Peoples R China

ISBN: (数字)9781665459471

ISBN: (纸本)9781665459471

With the rapid development of artificial intelligence, the library relies on the massive knowledge data to integrate the user behavior data, and continuously upgrade the library industry chain, the new form, the new service will bring the new experience for the user. The artificial intelligence technology based on machine learning and deep learning will promote the change of embedded service, upgrade the intelligence of embedded service physical scene in library, and advance the intelligence of information resources and virtual space. In the embedded service dimension, the artificial intelligence technology will promote the intelligent service supply of the library, the intelligent library will provide the "A I cloud processing" resource service for the user, the whole domain, the interactive scene service, the human-machine high-efficiency Coordinated Joint Service;In the dimension of user experience, artificial intelligence technology will bring a variety of user experience to the library. Taking the Library of Agricultural University as an example, this paper probes into the characteristics and modes of embedded service, puts forward the application of artificial intelligence in embedded service, and provides beneficial exploration for exerting the value of artificial intelligence in library service.

关键词： cloud processing library embedded service SWOT model

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Data-Driven Intelligence Can Revolutionize Today's Cybersecurity World: A Position Paper 3rd

Data-Driven Intelligence Can Revolutionize Today's Cybersecu...

引用

3rd international conference on Advanced Research in Technologies, Information, Innovation and Sustainability (ARTIIS)

作者： Sarker, Iqbal H. Janicke, Helge Maglaras, Leandros Camtepe, Seyit Edith Cowan Univ Secur Res Inst Perth WA 6027 Australia Cyber Secur Cooperat Res Ctr Joondalup Australia Edinburgh Napier Univ Sch Comp Edinburgh EH10 5DT Midlothian Scotland CSIRO Data61 Sydney NSW 2122 Australia

ISBN: (纸本)9783031488542;9783031488559

As cyber threats evolve and grow progressively more sophisticated, cyber security is becoming a more significant concern in today's digital era. Traditional security measures tend to be insufficient to defend against these persistent and dynamic threats because they are mainly intuitional. One of the most promising ways to handle this ongoing problem is utilizing the potential of data-driven intelligence, by leveraging AI and machine learning techniques. It can improve operational efficiency and saves response times by automating repetitive operations, enabling real-time threat detection, and facilitating incident response. In addition, it augments human expertise with insightful information, predictive analytics, and enhanced decision-making, enabling them to better understand and address evolving problems. Thus, data-driven intelligence could significantly improve real-world cybersecurity solutions in a wide range of application areas like critical infrastructure, smart cities, digital twin, industrial control systems and so on. In this position paper, we argue that data-driven intelligence can revolutionize the realm of cybersecurity, offering not only large-scale task automation but also assist human experts for better situation awareness and decision-making in real-world scenarios.

关键词： Cybersecurity Data-Driven Intelligence Automation Human Assistance Augmenting Experts Knowledge AI machine learning

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Sentiment Analysis of company reviews using machine learning 3

Sentiment Analysis of company reviews using Machine Learning

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3rd international conference for Emerging Technology, INCET 2022

作者： Yadav, Hrithika Dwivedi, Kartik Abirami, G. Srm Institute of Science and Technology Department of Computer Science Chennai India

ISBN: (纸本)9781665494991

Employees and job seekers today evaluate their experiences through online reviews of the company. These reviews can also be used by companies to understand their employee's perception. This study aims to extract aspects of importance from the user reviews and the overall sentiment is classified. The suggested method entails pre-processing user reviews with a variety of methodologies, followed by sentiment score based on aspects and feature extraction. In the proposed system, sentiment is classified using Multinomial Nave Bayes (MNB), Decision Trees (DTs), Support Vector machines (SVM), and Random Forest (RF) classifiers. Practical implementation is done on a glassdoor review dataset containing reviews from current and former employees of Google company is used. Results implicate that Random Forest produces best results as compared to the other algorithms. © 2022 IEEE.

关键词： Sentiment analysis

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