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检索条件"任意字段=4th International Conference on Machine Learning and Computer Application, ICMLCA 2023"

共 2348 条记录，以下是31-40 订阅

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Deep learning approach for weather classification using pre-trained convolutional neural networks 4

Deep learning approach for weather classification using pre-...

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4th international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Tarwani, Harit Patel, Shivang Goel, Parth Anand Changa388421 India Anand Changa388421 India

machine learning has had significant uses in weather prediction, and is especially useful in areas of agriculture and livestock, and outdoor vision. While various studies have been done on weather forecasting, limited work has yet been undertaken on its classification. Automating weather classification can free and save human resources. While simultaneously enhancing decision-making in the fields discussed. As such, the researchers have implemented transfer learning on different convolutional neural network (CNN) models, like ResNet50V2, EfficientNetB5, and EfficientNetV2S, for classification across 11 fields. the models were fine-tuned to improve performance and trained on a dataset that included diverse conditions like snow, lightning, and haze. Among the considered models, EfficientNetV2S showed the best accuracy (92.35%). this outcome indicates the effective application of machine learning, mainly transfer learning, in the future for weather classification and thus, its subsequent uses. ©2025 the Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Enhancing Sustainable Supply Chain Forecasting Using machine learning for Sales Prediction 4

Enhancing Sustainable Supply Chain Forecasting Using Machine...

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4th international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Rahman Mahin, Md. Parvezur Shahriar, Munem Das, Ritu Rani Roy, Anuradha Reza, Ahmed Wasif Department of Computer Science East West University Dhaka1219 Bangladesh

Managing the supply chain is crucial to success in the competitive business sector. Demand forecasting using sales data is one of the major things in supply chain management because it is directly connected to profit margins, inventory levels, sales, and customer satisfaction. this research tried to provide an innovative approach to sales prediction using advanced machine learning methods to enhance supply chain operations and boost the predictive accuracy of supply chain models after analyzing historical sales data and considering different factors like seasonality, trends, and stock. Various machine learning algorithms were applied, including Linear Regression, Elastic Net Regression, KNN, Random Forest, and the ensemble Voting Regressor. the performance of Random Forest and KNN is very well but the Voting Regressor is better than other models for its strength of multiple algorithms. the Voting Regressor provides the lowest RMSE of 1.54 and the highest R2 of 0.9999. this ensemble method improves sales forecasting accuracy by reducing errors and ensuring computational efficiency. It also provides more reliable tools to manage inventory, prevent overstocks, and minimize holding costs. this research presents the importance of machine learning integration in supply chain management. It shows the Voting Regressor as the most effective approach for demand forecast. Future research could explore the model's application in broader markets, integrating other key factors and deep learning algorithms to refine predictive capabilities later. © 2025 the Authors.

关键词： Adversarial machine learning

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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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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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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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4th international conference on IoT and Big Data Technologies for Health Care, IotCare 2023

4th International Conference on IoT and Big Data Technologie...

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4th international conference on IoT and Big Data Technologies for Health Care, IotCare 2023

ISBN: (纸本)9783031900839

the proceedings contain 29 papers. the special focus in this conference is on IoT and Big Data Technologies for Health Care. the topics include: Research on Intelligent Mental Health Diagnosis and Treatment Big Data Anomaly Early Warning Algorithm;research on Nursing Service Quality Evaluation Model Based on Internet of things and Big Data Technology;research on Artificial Intelligence Medical Rapid Diagnosis Method Based on Sports Injury Data;research on Hierarchical Sharing Method of Athletes’ Physical Health Data Under the Background of Mobile Medicine;medical and Health Information Retrieval Method Based on Natural Language Processing;secure Storage Method of Students’ Mental Health Privacy Data Based on machine learning;Personalized Recommendation of TCM Pharmacy Distance Teaching Resources Based on Data Mining;research on the Method of Integrating Teaching Resources of Traditional Chinese Medicine Pharmacology Based on Deep learning Algorithm;research on Patients’ Choice Behavior in Online Health Community Based on Multi Source Data Analysis;multi Perspective Recommendation Method Based on Clustering Algorithm for Pharmaceutical Specialty Integration of Ideological and Political Teaching Materials;intelligent Evaluation Method of Students’ Mental Health Based on Internet of things Technology;monitoring and Warning Method of Students’ Mental Health Data Based on Internet of things;design of Low Delay Communication System for Emergency Medical Rescue Vehicle Based on Narrowband Internet of things;research on Intelligent Monitoring Method of Medical Financial Information Privacy Based on Internet of things;a Method of Classifying and Querying Student Mental Health Data Based on Internet of things Technology.

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Apriori Analysis of Deep learning Models on a Multi-class Data Set 4th

Apriori Analysis of Deep Learning Models on a Multi-class Da...

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

作者： Kosuru, Sai Sathwik Padmanabha Reddy, Y.C.A. Akanksha, Perala Venkata Chaitanya, Kotagiri Parsi, Snehaja Chunduri, Madhu Babu Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Medak Narsapur502313 India Department of Artificial Intelligence & Machine Learning Chaitanya Bharathi Institute of Technology Ganidept Hyderabad500075 India

ISBN: (纸本)9789819751990

Deep learning techniques have large scopes in terms of applications and resource requirements, hence calling for a deep analysis of their behavior. Many studies focused on the application part of these models and there is a relatively lesser discussion about the unit-wise behavior. A multi-class dataset was taken and three different models were trained where their performances of training processes are described in terms of runtime characteristics like learning rate, times, etc. An apriori analysis was conducted where the runtime characteristics are investigated for their similarity with pre-defined knowledge of their architectures, as opposed to the posterior analysis which particularly focuses on observations. the work also serves as a framework for defining the logistics of the model training processes and fine-tunes the parameters devised in future for the maximum accuracy of the model. the results were further converted into graphical representations for enhanced delivery of insights. the study is also novel where, as opposed to previous works, in which performances are only tested, the model is evaluated in a design-specific manner dealing with only architectures and the parameters defining them. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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application of artificial intelligence online monitoring technology in fault diagnosis of cigarette packaging equipment 4

Application of artificial intelligence online monitoring tec...

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

作者： Wang, Haijiao Jilin Tobacco Industry Co. Ltd. Yanji Cigarette Factory Jilin Yanji133000 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

the production process of modern tobacco machinery is highly precise and complex, and when unpredictable failures occur in the production line, they can lead to economic losses for tobacco companies. Artificial Intelligence online monitoring technology can provide real-time fault alarms for tobacco production lines in production, and at the same time, without affecting the overall operation of the production line, it can improve the overall operational efficiency of the tobacco enterprises, which in turn can generate economic benefits. the fundamental process of visual inspection of cigarette package appearance quality is to classify the inspection image to distinguish qualified and unqualified packages, how to accurately judge the "qualified" and "unqualified" packages is the key to improve the accuracy and reduce the false reject rate. How to make accurate judgment on "qualified" and "unqualified" cigarette packs is the key to improve accuracy and reduce false reject rate. By analyzing the collected images of strip cigarettes, there are certain feature differences between qualified and unqualified images, in order to effectively determine the abnormal strip cigarettes, machine learning-based detection methods can be used to analyze the image feature information, extract the feature parameters, establish the cigarette packaging classification model, judge the defective strip cigarette products and reject them. the main content of the research in this chapter is to extract the image data features by combining wavelet transform and grayscale covariance matrix, and then use support vector machine and BP neural network algorithms respectively to sample and learn the training sample set of cigarette, and then classify and predict the test sample set to verify the classification performance and compare the classification effect of the two classifiers. © 2025 SPIE.

关键词： Wavelet transforms

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