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application of machine learning to Increase the Efficiency of the Global Search Algorithm for Solving Multicriterial Problems 4th

Application of Machine Learning to Increase the Efficienc...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Barkalov, Konstantin Grishagin, Vladimir A. Kozinov, Evgeny Lobachevsky State University of Nizhni Novgorod Nizhni Novgorod Russia

ISBN: (纸本)9783031812408

Decision making models described as problems of multicriterial optimization are very complicated for investigation, because the property of criteria contradictoriness leads to the notion of the solution as the set of non-dominated parameters (Pareto set). the complexity of these problems increases significantly in the case where criteria are multiextremal. In the paper a novel algorithm for multicriterial black-box optimization with multiextremal criteria is considered. this algorithm applies ideas of complexity reduction when the initial multicriterial problem is reduced to a set of univariate scalar subproblems on the base of Peano mapping and maximum convolution. For solving univariate problems an information-statistical global optimization algorithm with guaranteed convergence to global optimum is used. As a core of novelty, this algorithm includes in its computational scheme a machine learning procedure with combination of accumulating the information of solved subproblems that allows one to significantly accelerate building the Pareto set. Effectiveness of the proposed approach is estimated in the representative computational experiment on test sets of multicriterial problems with multiextremal criteria for different dimensions, in comparison with several nature-inspired multiobjective optimization algorithms. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization algorithms

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A Novel Architecture for a Medical Image Recognition System Using Deep learning-Based Multiple Regression Evaluation 4th

A Novel Architecture for a Medical Image Recognition System ...

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Parashar, Jyoti Upreti, Kamal Fatima, Iram Gangwar, Divya Saini, Ashok Kumar Vats, Prashant Department of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology & Management New Delhi India Department of Computer Science Christ University Delhi NCR Campus Ghaziabad India Department of AI & ML Dr. Akhilesh Das Gupta Institute of Technology & Management New Delhi India Department of Management Babu Banarasi Das Institute of Technology & Management New Delhi India Department of Computer Science & Engineering School of Computer Science & Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819751990

Models based on machine learning are optimization models that collect data, assess it, and deliver the reports required by specialists and management to make the best decisions. the application of contemporary machine learning allows the organization to quickly analyze photographs, differentiate voices assist in providing customer service, assess the information that is at hand, and uncover connections to aid in decision-making processes. the results of this investigation use quantitative methodologies to collect data and analyze it using mathematical procedures such as regression modeling as well as analysis of variance. Deep learning techniques applied to digital imaging, particularly in medical treatment, can increase picture quality, aid in modeling, aid in making the best possible diagnosis, and successfully address demands from patients. To analyze the hypothesis, investigators intend to utilize statistical approaches such as descriptive data analysis, regression evaluation, and analysis of variance (ANOVA). the authors employ the purposive sample approach to choose respondents from the healthcare industry. Purpose sampling is a non-probability sampling approach. Researchers collected data from 193 respondents working at hospitals that are privately owned in Southern Asia. As stated by the study, all factors, including efficiently meeting patient needs, have a probability value of under 0.05, indicating that they are statistically noteworthy. Following the study, the coefficient of variance (R squared) is 0.744, or 74.4%. According to the study, there is a high association between better image quality and ML-based digital picture identification systems. the recognition of patterns and the application of artificial intelligence to computerized recognition of pictures also have a close link. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Towards Reproducible Research in machine learning via Blockchain 4th

Towards Reproducible Research in Machine Learning via Bloc...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Filatovas, Ernestas Stripinis, Linas Orts, Francisco Paulavičius, Remigijus Vilnius University Institute of Data Science and Digital Technologies Akademijos 4 Vilnius08663 Lithuania

ISBN: (纸本)9783031812460

Artificial Intelligence, particularly in machine learning and related research areas such as Operational Research, currently faces a reproducibility crisis. Researchers encounter difficulties reproducing key results due to lacking critical details, including the disconnection between publications and the associated codes, data, and parameter settings. Solutions that improve code accessibility, data provenance tracking, research transparency, auditing of obtained results, and trust can significantly accelerate algorithm and model development, validation, and transition into real-world applications. Blockchain technology, with its features of decentralization, data immutability, cryptographic hash functions, and consensus algorithms, provides a promising avenue for developing such solutions. By leveraging the distributed ledger working over a peer-to-peer network, a secure and auditable infrastructure can be established for sharing and controlling data in a trusted manner. Our analysis examines the current state-of-the-art blockchain-based proposals that target reproducibility issues in the machine learning domain. Based on the analysis of existing solutions, we propose a high-level architecture and main modules for developing a blockchain-based platform that enhances reproducible research in machine learning and can be adapted to other Artificial Intelligence domains. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distributed ledger

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Research on instrument fault detection based on internet of things and machine learning 4

Research on instrument fault detection based on internet of ...

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4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Li, Shixi Zhao, Chenchen Yang, Benchen Peng, Zihang Bi, Jiayi School of Software Liaoning University of Technology Liaoning Huludao China Liaoning Provincial Second Women's Prison Liaoning Shenyang China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Modern industrial production, the industrial sector and daily life are inextricably linked to a plethora of chemical machinery and equipment, which serve as indispensable production apparatus. It is therefore paramount to guarantee their optimal functionality. It is therefore of great significance to the field of chemical machinery and equipment that fault monitoring is given due attention. the progressive advancement of computer and wireless communication technology has facilitated the maturation of expert diagnosis systems and the Internet of things. Consequently, fault monitoring systems based on these technologies are poised for widespread adoption. In this work, the instrument operation data collected by IoT devices is used to undergo preliminary cleaning and normalization to ensure the consistency and accuracy of the data. this preprocessed data is then fed into a multi-layered deep neural network (DNN) model. In the model training stage, the backpropagation algorithm is used to calculate the gradient of each layer, and the weights and biases in the network are adjusted by the stochastic gradient descent (SGD) optimization algorithm to minimize the loss function. through iterative training, the model gradually improves its ability to identify failure modes and predict accuracy. this approach enables deep neural networks to automatically learn complex fault signatures from large amounts of data and effectively detect and predict instrument failures in real-world applications. An experimental platform was set up in the LabView programming environment, equipped with 10 sensors, which were used to collect experimental data from the sensors and conduct fault monitoring experiments. the model shows high accuracy and robustness in different types of instrument fault detection, proving its effectiveness in practical industrial applications. © 2025 SPIE.

关键词： Deep neural networks

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A multi-source heterogeneous data fusion method based on time alignment and federated learning for the "dual carbon" digital intelligence monitoring center 4

A multi-source heterogeneous data fusion method based on tim...

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4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Yan, Zhenhua Han, Hongwei Zhu, Dongge Liu, Jia Ma, Rui Wang, Jianfu Liu, Chaochao Ningxia Electric Power Research Institute Yinchuan750002 China State Grid Ningxia Electric Power Co. Ltd. Yinchuan750001 China State Grid Xintong Yili Technology Co. Ltd. Xiamen361001 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Due to the complex composition of the "Dual Carbon" digital intelligence monitoring data, the error in using it to analyze carbon emission status is large. therefore, a multi-source heterogeneous "Dual Carbon" digital intelligence monitoring center based on time registration and federated learning is proposed. First, in terms of "double carbon" historical data accumulation, the composition of static basic data and carbon emission historical data is analyzed;in terms of "double carbon" real-time monitoring data, the composition of IoT sensing data and dynamic business data is analyzed. After adding timestamps to each data type, align the data to a unified timeline, build the "Double Carbon" digital intelligence monitoring center multi-source heterogeneous data high-order tensor in the federated learning framework, and perform Tucker decomposition and iterate to outputs the converged quadratic linear correlation function. In the test results, the method has the smallest difference between the analysis results of carbon emissions in region A and the actual situation. the corresponding error is always within 0.5*107 t, and the maximum error is only 0.46*107 t. © 2025 SPIE.

关键词： Federated learning

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Overview and Analysis of the application of Digital Twin-based Human-machine Collaboration in the Product Development Process 24

Overview and Analysis of the Application of Digital Twin-bas...

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4th international Joint conference on Robotics and Artificial Intelligence, JCRAI 2024

作者： Wang, Haoqi Lou, Gaopeng Lu, Xiaoping Dong, Liyang Zhengzhou University of Light Industry Zhengzhou China Co Ltd Qingdao China

ISBN: (纸本)9798400710100

In the era of Industry 5.0, the collaboration between humans and machines is vital for achieving smart manufacturing that is centered on human needs. this necessitates production services that are more efficient, precise, and safe. Digital twin (DT) technology plays a critical role in facilitating this collaboration by providing a virtual representation of physical systems, allowing for real-time data analysis and decision-making. this paper explores how DT-based human-machine collaboration (HMC) can significantly improve the product development process (PDP). By examining the advantages of DT-based HMC, the paper establishes correlations with key factors such as cost, time, and quality, grounded in the ten knowledge areas outlined in the Project Management Institute's Project Management Body of Knowledge. Additionally, the paper analyzes the practical applications of project management principles related to DT-based HMC within enterprises. through the application of these project management knowledge areas, the research assesses the likelihood of successful implementation of DT-based HMC in the PDP. the findings highlight that integrating DT technology with effective project management practices can lead to substantial enhancements in manufacturing processes, aligning them more closely with human-centric objectives. Ultimately, this study underscores the transformative potential of digital twins in fostering a collaborative environment that improves product development efficiency, quality, and overall competitiveness in the industry. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Advantages

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Non-negative Tensor Representation and Unsupervised Classification of Object Pose in Continuous Image Sequences 4th

Non-negative Tensor Representation and Unsupervised Classifi...

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4th international conference on Artificial Intelligence Logic and applications

作者： Ying, Zhihao Zhao, Yang Pei, Jihong Yang, Xuan Shenzhen Univ Coll Elect & Informat Engn Shenzhen Guangdong Peoples R China Guangdong Prov Key Lab Intelligent Informat Proc Shenzhen Guangdong Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Guangdong Peoples R China

ISBN: (纸本)9789819603534;9789819603541

the analysis of video data in machine learning classification tasks has become a significant topic in both research and application areas. the accurate classification of video data by frame sets, particularly when the content contains objects that are in a state of dynamic change, represents a significant and complex undertaking. With regard to the temporal phase of video, the thesis proposes an unsupervised classification method based on non-negative tensor factorization (NTF). In order to transform the video data into low-dimensional data that can be classified by unsupervised clustering, the non-negative tensor factorization method is used to reduce the dimension of the input tensor, resulting in the generation of a non-negative rank-1 tensor combination. Following dimensionality reduction, the extracted features are divided into clusters through Mean Shift, thereby facilitating classification and recognition of video sequences. Experiments on the Coil-100 dataset of item image sequences at different angles and other dynamic pose-changing videos demonstrate the effectiveness of the method.

关键词： Video timing division Non-negative tensor factorization Unsupervised learning

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Optimizing Deep learning Models to Address the Long Tail Problem and its application in Semantic Segmentation 24

Optimizing Deep Learning Models to Address the Long Tail Pro...

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4th international conference on Signal Processing and Communication Technology, SPCT 2024

作者： Sun, Jiarui Zhao, Yong School of Software and Microelectronics Peking University Beijing China School of Electronic and Computer Engineering Peking University Beijing China

ISBN: (纸本)9798400710636

In the semantic segmentation task, the long-tail problem causes the recognition performance of minority categories to be significantly lower than that of majority categories, affecting the overall effect of the model. this paper aims to optimize the FPN (Feature Pyramid Networks) model to better deal with the long-tail problem and improve the accuracy of semantic segmentation. First, by analyzing the category distribution of the dataset and identifying the severity of the long-tail phenomenon, a semantic segmentation dataset containing multiple categories is constructed. Secondly, for minority categories, data enhancement technology is used and resampling is performed to ensure that the model could be exposed to more minority category samples during training. A focus loss function is then integrated to prioritize hard-to-segment regions and underrepresented categories. Experimental results on standard benchmarks show that the proposed FPN model achieves a Dice coefficient of 88.1% and an F1-Score of 86.4%, significantly outperforming other models such as U-Net (Dice: 85.6%, F1-Score: 83.9%), DeepLab v3 (Dice: 87.4%, F1-Score: 85.6%), and DeepLab v1 (Dice: 84.3%, F1-Score: 82.8%). Notably, the minority class F1-Score for FPN reaches 72.1%, demonstrating its superior ability to tackle the long-tail problem. the results indicate that targeted methods, including dataset augmentation and loss function adjustment, can significantly enhance deep learning model performance, offering new insights for improving semantic segmentation tasks. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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Systematic Literature Review on Role of AI in IoT-Based Healthcare Solutions 4th

Systematic Literature Review on Role of AI in IoT-Based H...

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Manisha Sharma, Ashish Jain, Rekha Department of CSE IIIT Kota Kota India Department of CSE Manipal University Jaipur Jaipur India Department of Computer Application Manipal University Jaipur Jaipur India

ISBN: (纸本)9789819751990

there is wide applicability of IoT in many areas of life, one major are of them is Health Care IoT is showing potential application in patient health care. With the rapid advancements in technology and Internet, now conventional techniques of healthcare are vanishing and electronic health care systems are emerging very rapidly. this has increased patient safety, accessibility of health services at remote locations and reduced operational costs. this current study summarizes IoT-based health care solutions with introduction, technology used and application areas. By merging IoT and Artificial Intelligence, systems are becoming more accurate for monitoring and prediction tasks. AI has proven its potential in healthcare area. there is massive data which is continuously coming from various sensors attached to patients’ bodies. machine learning and Deep learning models can be used to get insight from this heap of data. Researchers are working on combining IoT and AI to develop more secure, more accurate, more efficient and less costly health care solutions. this paper focuses on AI-ML efforts in this area along with the key findings of various researches. Moreover, various research gaps and challenges has been also discussed to get future directions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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An empirical study on text based sentimental analysis of using various machine learning classifiers on newspaper 4th

An empirical study on text based sentimental analysis of usi...

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Singh, Harjender Garg, Hitendera Phogat, Ajay Kumar GLA University U.P Mathura India Maharaja Surajmal Institute Delhi India

ISBN: (纸本)9781032911571

Sentiment classification is a way to evaluate or analyse the important subjective information from the text. Analyzing the sentiments in newspaper is crucial for finding the pulse of public opinion i.e. parsing sentiments within newspaper is a complex task. We prioritize models that not only make accurate predictions but also provide clear explanations for their decisions. By comparing different models, we identify approaches that offer both accuracy and transparency and giving more insightful analyses of newspaper content. Our study involves a diverse collection of newspaper articles, and through rigorous evaluation, we highlight models that provide a balance between accuracy and interpretability. Text analysis methods for sentiment analysis typically work at a specific level, such as the pharse level, sentence or document level. this research paper aims to explore the application of TfidfVectorizer and counter vectorization techniques including Random Forest Classifier, Logistic Regression and Naïve Bayes methods, these techniques are evaluated on text based sentence-level sentiment analysis on newspaper headlines. In this paper the dataset used by me the target label is not provided, which should indicate whether the headlines are positive, negative, or neutral. To create the specified column, we can calculate the positive, negative, neutral, and compound scores for each headline. Based on the compound score, we will generate a ‘Sentiment’ column, which will serve as our target column during model training. © 2025 the Author(s).

关键词： Logistic regression

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