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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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application of the Semantic Segmentation Method in the Assessment of Forest Carbon Balance 16

Application of the Semantic Segmentation Method in the Asses...

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16th international conference Management of Large-Scale System Development, MLSD 2023

作者： Tulokhonova, Inna Mikhaylova, Svetlana Danilova, Soelma East Siberia State University of Technology and Management Department of Software Engineering and Artificial Intelligence Ulan-Ude Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350337907

this article focuses on the problem of human impact on the natural environment and its solution by machine learning methods. the concept of carbon balance is key in assessing climate change on the planet, which is especially important in connection with global warming. the main causes of global warming are associated with anthropogenic emissions of carbon dioxide as a result of agricultural production, land use and burning of fossil fuels. One of the approaches to mitigate this environmental problem is to estimate the carbon balance based on neural network semantic segmentation of forests of terrestrial ecosystems. the proposed convolutional neural network (CNN) model is based on the U-Net architecture. this architecture surpasses traditional machine learning methods in solving computer vision and pattern recognition problems, and is also quite easy to learn. the input data of the model is a set of satellite images of the forest area. the images were obtained using the Sentinel-2 satellite, which provides high spatial resolution and a large number of spectral channels. A software prototype has been developed to implement the CNN model. Training experiments and calculations on changes in the carbon balance in forests led to the conclusion that the results of the study will contribute to the balanced management of reforestation and carbon deposition. © 2023 IEEE.

关键词： Global warming

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Cloud Stateless Server Failover Prediction Using machine learning on Proactive System Metrics 18

Cloud Stateless Server Failover Prediction Using Machine Lea...

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18th international Joint Symposium on Artificial Intelligence and Natural Language Processing (ISAI-NLP) / 4th international conference on Artificial Intelligence and Internet of things (AIoT)

作者： Chairatana, Nutt Chawuthai, Rathachai King Mongkuts Inst Technol Ladkrabang Dept Robot & AI Engn Sch Engn Bangkok Thailand King Mongkuts Inst Technol Ladkrabang Sch Engn Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9798350371215

Cloud computing, revered for its extraordinary scalability and elasticity, has revolutionized business operations by providing flexible resource options based on demand. However, this on-demand resource allocation poses distinct challenges. Due to the fluid nature of resource allocation and load distribution in the cloud, monitoring the health of servers using system metrics becomes problematic. this complexity can lead to unexpected server request failures and service interruptions due to resource insufficiencies, highlighting the need for more effective monitoring systems. Our research utilizes machine learning techniques to predict cloud server health based on resource usage and operational metrics, focusing specifically on stateless applications. Our study reveals that a Logistic Regression model trained on these system metrics delivers the most precise predictions. After hyperparameter tuning, the model exhibited robust performance, achieving a macro-averaged F1 Score of 97.7%. the paper outlines our methodology, findings, and the potential of this approach for cloud server health prediction.

关键词： cloud server failover stateless application machine learning non-functional testing predictive maintenance proactive system metrics software engineering

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the Personalized learning Path of Affective Computing in Enhancing Emotional Identification with Chinese Culture among international Students Studying in China 4

The Personalized Learning Path of Affective Computing in Enh...

引用

4th international conference on Educational Technology, ICET 2024

作者： Huang, Chao Liao, Xianglin Wuyi University College of Overseas Education Wuyishan China Huazhong Normal University College of Psychology Wuhan China

ISBN: (纸本)9798350376944

Traditional measurements of emotional identification with Chinese culture among international students studying in China focus on quantifiable individual factors, while artificial intelligence technology and affective computing expand the scope to non-quantifiable individual factors such as facial expressions, language, and body language. Based on personalized learning theory, OCC theory, and cultural adaptation theory, the application of affective computation in enhancing international students' emotional identification with Chinese culture can be approached from the three aspects of "personality + emotion + cultural adaptation". On the personalized level, affective computation using full-sample, whole-process, multimodal data in learning scenarios is employed to depict the personalized feature emotional portrait of implicit emotions of international students studying in China. By utilizing artificial intelligence technology, natural language processing technology, and adaptive learning technology, analyze and compute the emotions embedded in the non-quantitative individual factors during human-computer interaction. Furthermore, based on predicting the different stages of cultural adaptation of international students in China, customize personalized and precise learning programs to enhance emotional identification by pinpointing the emotional preferences and weaknesses of international students in China. Based on affective computation of artificial intelligence technology, a closed loop of affective computation is achieved through stages such as collection-recognition-computation-portrayal-analysis-positioning-recommendation, effectively enhancing international students' emotional identification with Chinese culture. © 2024 IEEE.

关键词： Contrastive learning

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Embedded Zero-shot learning Algorithm Based on Semantic Attributes 4

Embedded Zero-shot Learning Algorithm Based on Semantic Attr...

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4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Zhen, Zhibin Sun, Huadong Liu, Yinghui Zhang, Pengyi Harbin University of Commerce Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin China

ISBN: (纸本)9798350308082

Zero-shot learning is a technique capable of recognizing target categories even when labeled samples for these categories are completely absent. Traditional zero-shot learning methods based on embedding models usually have a low utilization rate of semantic attributes and exhibit a bias towards seen classes during testing. Addressing this, we propose an embedding-based ZSL method grounded on semantic attributes. this method uses a spatial attention mechanism during the construction of the semantic attribute embedding space, enabling the model to focus on more distinctive attribute features within the images. Consequently, it can utilize these distinctive features for similarity classification. Furthermore, a category calibration loss function is introduced to assign a greater weight to unseen classes and a lesser weight to seen classes, aiming to reduce the bias towards seen classes during testing. Extensive experiments were carried out on three mainstream ZSL benchmark datasets. Compared with some of the existing classical algorithms, our method demonstrated improved results. © 2023 IEEE.

关键词： Zero-shot learning

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A Methodology Based on Quality Gates for Certifiable AI in Medicine: Towards a Reliable application of Metrics in machine learning

A Methodology Based on Quality Gates for Certifiable AI in M...

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18th international conference on Software Technologies, ICSOFT 2023

作者： Elia, Miriam Bauer, Bernhard Faculty of Applied Computer Science University of Augsburg Germany

ISBN: (纸本)9789897586651

As of now, intelligent technologies experience a rapid growth. For a reliable adoption of those new and powerful systems into day-to-day life, especially with respect to high-risk settings such as medicine, technical means to realize legal requirements correctly, are indispensible. Our proposed methodology comprises an approach to translate such partly more abstract concepts into concrete instructions - it is based on Quality Gates along the intelligent system’s complete life cycle, which are composed of use-case adapted Criteria that need to be addressed with respect to certification. Also, the underlying philosophy regarding stakeholder inclusion, domain embedding and risk analysis is illustrated. In the present paper, the Quality Gate Metrics is outlined for the application of machine learning performance metrics focused on binary classification. © 2023 by SCITEPRESS-Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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Crop Recommendation Using Supervised learning Techniques 4

Crop Recommendation Using Supervised Learning Techniques

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Ramya, J. Sankar, M. Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350300888

the agricultural sector has a significant impact on the Indian economy. Researching crop production is crucial to enhance its contribution to the economy. Studies on crops, irrigation, and farm machinery are necessary to boost crop output. It is essential to select not only the most suitable crop for the area and environment but also high-yielding seeds. Farmers base their crop selection on consumer demand and previous season prices. Choosing an unsuitable crop for the soil type can be challenging and often leads to significant production setbacks, resulting in reduced output and lower returns for farmers. Crop recommendation plays a significant role in enhancing agricultural productivity and ensuring the optimal selection of crops based on specific land and environmental conditions. this study proposes a supervised learning approach utilizing the Random Forest algorithm to deliver precise and effective crop recommendations. the aim is to support farmers in making well-informed decisions regarding crop selection, seed varieties, and fertilizer quantities for cultivation. the importance of crop recommendation in enhancing agricultural productivity and selecting optimal crops based on specific land and environmental conditions. It introduces the proposed supervised learning approach using the Random Forest algorithm and highlights the objectives of supporting farmers in making informed decisions about crop selection. © 2023 IEEE.

关键词： Support vector machines

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An Approach to Blood Cell Classification Based on Object Segmentation and machine learning 4

An Approach to Blood Cell Classification Based on Object Seg...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Kovalenko, Sergii Kovalenko, Anton Kovalenko, Svitlana Pelikh, Daniil Mikhnova, Olena Severin, Valeriy Dept. of System Analysis and Information-Analytical Technologies National Technical University"KhPI" Kharkiv Ukraine Dept. of Software Engineering and Management Intelligent Technologies National Technical University"KhPI" Kharkiv Ukraine Dept. of Cyber Security and Information Technologies State Biotechnological University Kharkiv Ukraine

ISBN: (纸本)9798350395532

the study proposed an approach to cells classification in a blood smear image, consisting of two stages: object segmentation and application of machine learning methods to determine the cell type. To apply this approach, a dataset consisting of cell type labels and the corresponding cell area was created. Object segmentation was performed using image processing methods: noise removal, image binarization, contouring, and area determination using the Gaussian formula. the obtained data set was pre-processed: duplicate data and outliers were removed, and the data was divided into testing and training sets. Training for Gaussian Naive Bayes Classifier, Decision Tree Classifier, Random Forest, Support Vector Classifier models were performed. It was found that the accuracy of all classifiers is 97-98%. In addition, metrics were calculated to evaluate the effectiveness of the models. the Decision Tree Classifier method was chosen to apply the approach to cell classification on new images. All image processing, data collection and processing, model training and evaluation were performed using the Python programming language and relevant libraries. © 2023 IEEE.

关键词： Decision trees

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Intelligent Analysis Method of E-Commerce Data Based on Multiple machine learning Algorithms 4

Intelligent Analysis Method of E-Commerce Data Based on Mult...

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4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Jiang, Xiaotian Li, You Jilin Agricultural Science and Technology University Jilin Jilin China

machine learning algorithm is one of the most popular research fields in the 21st century, and it is widely used in various industries. this paper first discusses the development status, characteristics and intelligent classification technology of e-commerce based on various methods. then, through statistical analysis of multiple samples, this paper establishes a model to predict the quantity of goods and other time-related changes in the future period of time, and proposes an improved variance estimate to achieve the best effect under different data scenarios. Finally, the experiment proves that the response time of the linear regression algorithm fluctuates between 20ms and 25ms, and the overall fluctuation is small. For different samples, the response time of the linear regression algorithm is maintained in a relatively stable range, showing the stability of different input data. the response time of the decision tree algorithm fluctuates between 28ms and 33ms, showing a certain fluctuation. For different samples, the response time of decision tree algorithm fluctuates greatly, which may be affected by the characteristics of input data and show different processing efficiency. the response time of support vector machine algorithm fluctuates between 14ms and 18ms, and the change is relatively stable. For different samples, the response time of SVM algorithm is kept at a relatively stable level, showing the consistency of input data processing ability. © 2024 the Authors.

关键词： Adversarial machine learning

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application of computer Aided 5G Technology in Wireless Intelligent Network Communication System 4

Application of Computer Aided 5G Technology in Wireless Inte...

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4th IEEE Annual Flagship India Council international Subsections conference, INDISCON 2023

作者： Xu, Yunwu Li, Yan College of Guangdong Songshan Polytechnic Electrical Engineering Guangdong Shaoguan China

ISBN: (纸本)9798350333558

the application of computer-aided 5G technology in wireless intelligent network communication systems has completely changed our communication methods. 5G technology is the fifth generation mobile network, providing faster network speed, lower latency, and higher capacity. this technology has been integrated into wireless intelligent network communication systems to enhance communication between devices. the use of 5G technology in wireless intelligent network communication systems enables faster and more efficient data transmission, which is crucial for real-time applications such as video conferencing, online games and autonomous vehicle. In addition, this technology improves the reliability of wireless networks by reducing interference and increasing coverage. In addition, computer-aided 5G technology makes it possible to develop new applications that were previously impossible to implement due to connection limitations. For example, remote surgery can now be performed using augmented reality technology that requires high-speed internet connectivity. In summary, the application of computer-aided 5G technology in wireless intelligent network communication systems has changed our communication methods by providing faster network speeds, lower latency, higher capacity, and new applications. therefore, this technology will continue to shape our future by strengthening the connections between various industries. © 2023 IEEE.

关键词： Online systems

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An Empirical Comparison of Optimizers for Quantum machine learning with SPSA-Based Gradients 4

An Empirical Comparison of Optimizers for Quantum Machine Le...

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4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Wiedmann, Marco Hölle, Marc Periyasamy, Maniraman Meyer, Nico Ufrecht, Christian Scherer, Daniel D. Plinge, Axel Mutschler, Christopher Fraunhofer Institute for Integrated Circuits Iis Fraunhofer Iis Nuremberg Germany

ISBN: (纸本)9798350343236

Variational quantum algorithms (VQAs) have attracted a lot of attention from the quantum computing community for the last few years. their hybrid quantum-classical nature with relatively shallow quantum circuits makes them a promising platform for demonstrating the capabilities of noisy intermediate scale quantum (NISQ) devices. Although the classical machine learning community focuses on gradient-based parameter optimization, finding near-exact gradients for variational quantum circuits (VQCs) with the parameter-shift rule introduces a large sampling overhead. therefore, gradient-free optimizers have gained popularity in quantum machine learning circles. Among the most promising candidates is the simultaneous perturbation stochastic approximation (SPSA) algorithm, due to its low computational cost and inherent noise resilience. We introduce a novel approach that uses the approximated gradient from SPSA in combination with state-of-the-art gradient-based classical optimizers. We demonstrate numerically that this out-performs both standard SPSA and the parameter-shift rule in terms of convergence rate and absolute error in simple regression tasks. the improvement of our novel approach over SPSA with stochastic gradient descent (SGD) is even amplified when shot- and hardware-noise are taken into account. We also demonstrate that error mitigation does not significantly affect our results. © 2023 IEEE.

关键词： machine learning

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