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Investigations on machine learning, deep learning, and longitudinal regression methods for global greenhouse gases predictions

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international JOURNAL OF ENVIRONMENTAL science AND TECHNOLOGY 2025年第7期22卷 5249-5262页

作者： Yazd, S. D. Gharib, N. Derakhshandeh, J. F. Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait

Combating climate change is one of the key topics and concerns that our community is currently facing these days. Since a few decades ago, greenhouse gases emissions gradually started to increase. Thus, the researchers attempted to find a permanent solution for this challenge. In this paper, different methods of machine learning and deep learning models are applied to evaluate their effectiveness and accuracy in predicting greenhouse gases emissions. To increase the accuracy of the assessment, the data of 101 countries over a period of 31 years (1991-2021) from the official World Bank sources are considered. In this study, therefore, a range of matrices are analyzed including Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, p value, and correlation coefficient for each model. The results demonstrate that machine learning models typically overtake the deep learning models with the support vector regression polynomial model. Besides, the statistical findings of longitudinal regression analysis reveal that by increasing cereal yield, and permanent cropland areas the greenhouse gas emissions are significantly increase (p value = 0.000) and (p value = 0.06) respectively;however, increasing in renewable energy consumption and forest areas will lead to decreasing in greenhouse gas emissions (p value = 0.000) and (p value = 0.07) respectively.

关键词： Artificial Intelligent Deep learning Greenhouse gases Prediction Regression

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Material-Adaptive Anomaly Detection Using Property-Concatenated Transfer learning in Wire Arc Additive Manufacturing

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international JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2024年第2期25卷 383-408页

作者： Shin, Seung-Jun Lee, Ju-Hong Jadhav, Sainand Kim, Duck Bong Hanyang Univ Sch Interdisciplinary Ind Studies Seoul South Korea Hanyang Univ Dept Ind Data Engn Seoul South Korea Tennessee Technol Univ Dept Mech Engn Cookeville TN 38505 USA Tennessee Technol Univ Dept Mfg & Engn Technol Cookeville TN 38505 USA

Wire arc additive manufacturing is a promising additive manufacturing process because of its high deposition rate, and material diversity. However, the low quality of melted parts is a critical issue, owing to the difficulty in establishing design rules for process-structure-property-performance. Previous studies have resolved this challenge by deriving anomaly detection models for quality monitoring and have largely relied on machine learning by training melt pool image data. Acquiring sufficient data is a key to obtaining reliable models in machine learning;however, an issue arises from concerning the cost intensiveness in high-cost materials. We propose a material-adaptive anomaly detection method to detect balling defects in a target material using property-concatenated transfer learning. First, transfer learing is applied to derive convolutional neural network (CNN)-based models from a source material and transfer them to a target material, wherein data are insufficient and machine learning rarely achieves high performance. Second, material properties are concatenated on transfer learning as additional features onto image features, contrary to typical transfer learning where CNNs only extract image features. We perform experiments in a gas tungsten arc welding system with low-carbon steel (LCS), stainless steel (STS), and inconel (INC) materials. Our models achieve best classification accuracies of 82.95%, 89.47%, and 84.22% when transferring from LCS to STS, LCS to INC, and STS to INC, respectively, compared with 78.03%, 86.37%, and 73.63% obtained using typical transfer learning. The proposed method can effectively resolve the data scarcity by model transfer from sufficient datasets in low-cost materials to rare datasets in high-cost materials. Moreover, it outperforms typical transfer learning because material properties are learned as manufacturing-knowledge features, accounting for melting and hardening characteristics of materials.

关键词： Wire arc additive manufacturing Anomaly detection Transfer learning Convolutional neural network Quality monitoring Material property

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machine learning Method for Carbon Stock Classification with Drone and GEE data

Machine Learning Method for Carbon Stock Classification with...

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2025 international conference on Advancement in data science, E-learning and Information System, ICADEIS 2025

作者： Setiawan, Mohammad Daffa Setiawan, Erwin Budi School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331513320

Accurately classifying carbon stock is essential for tackling climate change, as it helps improve forest management and carbon storage efforts. However, traditional measurement methods are often costly, time-consuming, and require extensive fieldwork. To address these challenges, remote sensing combined with machine learning offers a more efficient and scalable solution. This study explores the use of XGBoost and Random Forest classifiers to classify carbon stock levels using drone and Google Earth Engine (GEE) imagery, with VGG16 extracting features from the images. The dataset, collected from field plots at Telkom University in Bandung, Indonesia, consists of 2,114 drone images and 2,526 GEE images, labeled into three categories: low, medium, and high carbon stock. The results show that XGBoost applied to drone imagery achieves the highest accuracy of 90.79%, outperforming Random Forest and GEE-based models. This study underscores the potential of deep learning and ensemble methods in improving carbon stock estimation, supporting better environmental conservation. © 2025 IEEE.

关键词： Carbon sequestration

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A Review on the Applications of machine learning and Deep learning Algorithms for Image Recognition 4

A Review on the Applications of Machine Learning and Deep Le...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of machine learning and Deep learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

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An Intelligent machine learning Approach to Detect Spam in Social Media 5

An Intelligent Machine Learning Approach to Detect Spam in S...

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5th international conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Mohandas, R. Prasanna, D.S. John Deva Meenakshi, N. Kaviyaraj, R. Nathiya, S. Arivazhagan, N. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798350389609

machine learning based intelligent approach is applied to finding spam in YouTube videos. Spam comments are ones that are promotional or unrelated. A growing number of users have been drawn to the idea of making money by placing advertisements on popular social networking platforms over time. These remarks damage a channel's reputation. YouTube has addressed the issue with a limited set of solutions that concentrate on preventing comments from including links. Such heuristics are now futile since spammers have found ways to get around them. Despite the fact that common machine learning classification techniques have demonstrated considerable success, new methods can still increase accuracy. Therefore, this research attempt to identify such statements using machine learning techniques like Random Forest, SVM, and Naive Bayes. In order to convey a clear concept, graphs are used to illustrate data cleaning, data analysis, text preparation, and model building in this research. © 2024 IEEE.

关键词： Adversarial machine learning

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The Utilization of machine learning Approaches in Predicting the Prominent Physicochemical Properties of Polychlorinated Biphenyl

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international JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2025年第5期39卷

作者： Zhang, Hongqin Zhang, Ying Xu, Lei Jiangsu Univ Technol Sch Elect & Informat Engn Changzhou 213001 Peoples R China

Given the difficulties in experimental measurement, machine learning offers a viable alternative method to reduce and minimize costs and time. machine learning enhances the effectiveness and efficiency of environmental pollutant monitoring, supporting better management and protection of ecosystems and public health. Random forest is indeed a significant machine learning method, widely used for various applications due to its robustness and versatility. Polychlorinated biphenyls (PCBs), a vital class of persistent organic pollutants, have garnered significant attention from the scientific community due to their detrimental impacts. In this study, a computational prediction model for octanol-water partition coefficient (logP) and bioconcentration factor (logBCF) of PCBs was developed by using random forest (RF), sparrow search algorithm random forest (SSA-RF), particle swarm optimization random forest (PSO-RF), and gray wolf optimization random forest (GWO-RF) methods. We performed a comprehensive validation, evaluation, and mechanistic explanation of the model for ensuring its reliability and applicability. Overall, the internal and external validation statistical parameters of the eight models have good robustness and predictive power. The Williams plots further show that all models are built in a wide range of application domains, and therefore they can be applied to unrecognized PCBs already in the environment to fill in the gaps in relevant experimental data. SSA-RF was superior to other methods, suggesting that it is more appropriate for computational studies of PCBs.

关键词： Polychlorinated biphenyls (PCBs) octanol-water partition coefficient bioconcentration factor machine learning random forest optimization algorithm

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Unveiling Graph Structures for machine learning: learning, Structuring, and Coarsening 24

Unveiling Graph Structures for Machine Learning: Learning, S...

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7th ACM India Joint international conference on data science and Management of data (CODS-COMAD) / 11th ACM IKDD CODS conference / 29th COMAD conference

作者： Kumar, Manoj Kumar, Sandeep Indian Inst Technol Delhi India

ISBN: (纸本)9798400716348

Graph structure is an important element in the realm of machine learning tasks. This tutorial centers on the art of deriving graph representations from data. It unfolds through three pivotal themes: learning Graphs from data: We delve into the process of extracting meaningful graph structures directly from data. learning Structured Graphs: The tutorial navigates the complex terrain of acquiring structured graph representations from raw data. Graph Coarsening: Addressing the challenge of managing large graphs, we delve into methods of graph coarsening that decrease the graph's dimensions while maintaining its important properties and characteristics. Each theme is dissected thoroughly, combining theory with practical application. The tutorial demonstrates how these graph representations drive various applications, including clustering, node and graph classification, and edge prediction. In essence, this tutorial arms participants with the tools to unleash the potential of graph structures in the realm of machine learning.

关键词： Graph learning structured graph learning graph coarsening node and classification

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A Comprehensive Analysis of machine learning Methods for Bug Prediction in Software Development 5th

A Comprehensive Analysis of Machine Learning Methods for Bug...

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5th international conference on data science, machine learning and Applications

作者： Ravikumar, Ch Kumar, Kotha Harish Sathish, Nandigama Suhasini, S. Nimmala, Satyanarayana Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad Telangana India CVR Coll Engn Dept CSE Data Sci Hyderabad Telangana India Priyadarshini Inst Sci & Technol Women Dept CSE Khammam Telangana India CVR Coll Engn Dept Informat Technol Hyderabad Telangana India CVR Coll Engn Dept Data Sci Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

In order to create software that is reliable, efficient, and of the highest quality, it is imperative to predict and address bugs during the development stage. Early detection of faults is crucial;yet developing a cost-effective and successful advanced bug prediction model presents challenges. This research endeavor aims to achieve precise bug identification by exploring the utilization of various machine learning techniques on training and testing datasets. Multiple machine learning methods have been devised to identify and learn from software defects. This study employs machine learning techniques to conduct a comprehensive examination of software bug detection, offering valuable insights to the software industry. It synthesizes existing research on bug prediction, detailing different methods and highlighting their effectiveness, advantages, and limitations. This comprehensive analysis offers valuable guidance to researchers and software developers seeking to enhance bug detection methods for the creation of higher-quality software.

关键词： Bug prediction Software development machine learning Accuracy Fault detection

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machine learning for the Analysis of Equipment Sensor data in Road Construction Projects 5th

Machine Learning for the Analysis of Equipment Sensor Data i...

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5th international conference on Transportation Geotechnics

作者： Silva, Raquel Fernandes, Hugo Neves, Jose Parente, Manuel Univ Minho Dept Civil Engn ISISE ARISE Guimaraes Portugal Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal Univ Lisbon Inst Super Tecn CERIS P-1049001 Lisbon Portugal

ISBN: (纸本)9789819782154;9789819782130;9789819782123

New trends in digitalization in construction have created opportunities for research and informed decision-making. Concepts like digital twins and sensorization have successfully enabled the direct collection of data from construction processes and equipment. For instance, integrating sensors into trucks transporting construction materials facilitates gathering valuable information about the equipment and the surrounding environment. This previously unattainable data can now be utilized to provide pertinent insights into the decision-making process. On one hand, accurate fuel consumption estimations are required to help optimization in construction and transportation infrastructure projects as they represent a major expense. On the other hand, despite the numerous studies conducted to detect cracks and potholes in road pavements, the classification of road types is frequently overlooked. This study aims to bridge this gap by developing a methodological framework that utilizes vibration data from sensors installed in construction trucks to predict the fuel consumption of heavy vehicles and the road category based on the pavement surface quality through which it is circulated. Given their promising results in prior research, the models Random Forest, Neural Network, and Support Vector machine were applied to the database. The results demonstrate that vibration-based data acquisition methods combined with machine learning algorithms can accurately predict fuel consumption, identify different road categories, and can be successfully applied on a larger scale.

关键词： Fuel consumption machine learning Road classification Sensors Supervised learning

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Clustering Mixed data: Bridging the Gap with Deep learning 5th

Clustering Mixed Data: Bridging the Gap with Deep Learning

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5th international conference on data science, machine learning and Applications

作者： Yerra, Harini Kommu, Siddartha Kumar, B. Vijay Sudam, Rachana JB Inst Engneering & Technol Dept Informat Technol Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

In the realm of data clustering, the Deep Embedded Clustering (DEC) algorithm has earned a reputation for efficiently grouping data points. Its limitation is that it only deals with numerical data. In real-world scenarios, data is often a mixture of numerical and categorical attributes, posing a more intricate challenge. This project presents an enhanced version of the DEC framework, tailored to address the complexities of mixed data clustering. It incorporates embedded layers and soft-target updates to ensure seamless handling of both numerical and categorical attributes, maintaining convergence stability throughout the process. It also uses the concept of a "deep reinforcement learning" In the evaluation process, the proposed approach performed better than standard metrics.

关键词： data Clustering Mixed data soft target updates Deep learning Framework

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