检索结果-内蒙古大学图书馆

Improving Smart Agriculture Through the Use of machine learning to Offer Tailored Crop Forecasts

Remote Sensing in Earth Systems Sciences 2024年第1期8卷 74-83页

作者： Patil, R.N. Maroor, Jnaneshwar Pai Dhanke, Jyoti A. Chunduru, Madhava Rao Singh, Mangal Jangir, Pradeep Upadhyay, Shrikant Department of Mechanical Engineering Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to Be University) Karnataka Mangaluru India Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department Of Computer Science And Engineering Koneru Lakshmaiah Educational Foundation Vaddeswaram AP Guntur District India Department of Electronics and Telecommunication Engg. Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

It is not possible to identify crop illnesses by examining each disease independently. Users may only get forecasts for the most anticipated illnesses with the use of a robust analytic methodology. This study presents a method that can collect, analyze, and identify crop health information all on one platform using an IoT and machine learning-based approach. Food security and the provision of essential raw materials for several industries are both supported by agriculture, which is the most important and basic vocation. Improved crop yields, thanks to new innovative agricultural practices, mean less water wasted and more money in the bank. An intelligent irrigation system that uses a machine learning algorithm to forecast a crop’s water needs is the suggested model. One such rapidly evolving invention is the Internet of Things (IoT), which is now spreading its wings across all geographies. Technological innovation is reaching a new level of practicality in agriculture with the rise of embedded PCs like Arduino. Soil quality monitoring using Arduino, several sensors, and an Android app is shown and accomplished in this study. Temperature, soil moisture level, ammonia, and carbon content limit soil quality in this study. Arduino is used as both a worker and an information processing device, and it is in charge of sensor security. The terminal device is an Android phone. Producers may improve agricultural yields by collecting and analyzing data on weather, soil, seed, and yields of crops, moisture, and gusts of wind in specific areas. Furthermore, a flask-built GUI was used to present crop forecasts derived from a bespoke recommender system. Because of its adaptable design, the system may one day be able to discover the suggested items of other areas. This article uses targeted agricultural data to create five machine-learning approaches that are comparable to one another and to compare and contrast them. © The Author(s), under exclusive licence to Springer Nature Swit

关键词： Decision navigation systems Internet of Things (IoT) Irrigation systems machine learning algorithms Sensors

来源：评论

学校读者我要写书评

暂无评论

Enhancing the prediction accuracy of greenhouse temperatures through hybrid machine learning models: an in-depth comparative analysis of RBF, RF, and XGBoost techniques combined with SMA and MFO optimization strategies

引用

MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第7期8卷 1-24页

作者： Dai, Zhenhua Huang, Tangsen Hunan Univ Sci & Engn Sch Informat Engn Yongzhou 425199 Hunan Peoples R China

The precise prediction of greenhouse temperatures is crucial for the optimization of crop cultivation and resource allocation. Yet, the dynamic nature of greenhouse climates challenges conventional predictive models. This research overcomes the difficulties by creating hybrid machine learning (ML) models-namely, Radial Basis Function (RBF), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)-that have been optimized with the Slime Mold Algorithm (SMA) and Moth-Flame Optimization (MFO) for improved predictive accuracy. The results show that models optimized by MFO methods outperform models optimized by SMA, with XGBoost-MFO achieving the best accuracy (R2 = 0.974861, MSE = 0.797552). These results highlight the advantages of using intelligent optimization strategies to improve model flexibility and performance. In addition to predictive improvements, this study examines the practical implementation of these models in real-world greenhouse operations. Artificial intelligence-controlled temperature can improve energy efficiency, automate climate control, and enhance resource utilization. However, issues such as sensor reliability, data acquisition costs, and computational demands must be addressed to make application mainstream. This study highlights the significance of hybrid machine-learning models in sustainable agriculture by connecting predictive analytics with real-world applications. The results presented herein facilitate the development of automated decision-making systems for the control of greenhouses that are climate change resilient.

关键词： Greenhouses Agricultural production Environmental factors Temperature regulation Predictive modeling machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Sustainable energy management in the AI era: a comprehensive analysis of ML and DL approaches

引用

COMPUTING 2025年第6期107卷 1-64页

作者： Javed, Haseeb Eid, Fatma El-Sappagh, Shaker Abuhmed, Tamer Sungkyunkwan Univ Coll Comp & Informat Dept Comp Sci & Engn Suwon South Korea SUNY Stony Brook Technol Management New York NY 11794 USA Sungkyunkwan Univ Coll Comp & Informat Appl Artificial Intelligence Suwon South Korea Galala Univ Fac Comp Sci & Engn Suez 435611 Egypt Benha Univ Fac Comp & Artificial Intelligence Informat Syst Dept Banha 13518 Egypt

This study comprehensively analyzes the application of innovative deep learning (DL) and machine learning (ML) techniques in smart energy management systems (EMSs), with an emphasis on load forecasting, demand response, and the development of smart energy sectors. The application of various ML and DL models were examined in over 200 studies from 2014 to 2024 in an electrical network's EMS to highlight the key benefits and advances made by each technology for the sustainable management systems in energy sector. The findings emphasize DL and ML models' enhanced precision and predictive capabilities in load forecasting, their efficacy in enabling efficient demand response mechanisms, and their significance in supporting the development of smart energy sectors. Furthermore, recommendations are made based on the survey results to assist in incorporating these techniques into EMS frameworks, such as investment in data infrastructure, model training and validation, and collaboration between researchers, industry experts, and policymakers. The study also discusses the limitations identified in the literature, such as limited real-world implementations, challenges regarding quality and data availability, and the need for enhanced ML and DL model interpretability. Addressing these limitations can assist in increasing the application and efficacy of ML and DL techniques in EMSs, enabling a more efficient and sustainable energy landscape. Finally, this study facilitates researchers' exploration of ML and DL in energy management, highlighting relevant limitations, strengths, and alternative approaches associated with sustainable energy management. It also indicates potential future research directions for further investigation.

关键词： machine learning algorithms Sustainable energy Prediction model Energy management

来源：评论

学校读者我要写书评

暂无评论

PermGuard: A Scalable Framework for Android Malware Detection Using Permission-to-Exploitation Mapping

引用

IEEE ACCESS 2025年 13卷 507-528页

作者： Prasad, Arvind Chandra, Shalini Uddin, Mueen Al-Shehari, Taher Alsadhan, Nasser A. Ullah, Syed Sajid GLA Univ Dept Comp Engn & Applicat Mathura 281406 India BBA Univ Dept Comp Sci Lucknow 226025 India Univ Doha Sci & Technol Coll Comp & Informat Technol Doha Qatar King Saud Univ Dept Self Dev Skill Comp Skills Common First Year Deanship Riyadh 11451 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Comp Sci Dept Riyadh 11451 Saudi Arabia Univ Agder Dept Informat & Commun Technol N-4879 Grimstad Norway

Android, the world's most widely used mobile operating system, is increasingly targeted by malware due to its open-source nature, high customizability, and integration with Google services. The increasing reliance on mobile devices significantly raises the risk of malware attacks, especially for non-technical users who often grant permissions without thorough evaluation, leading to potentially devastating effects. This paper introduces PermGuard, a scalable framework for Android malware detection that maps permissions into exploitation techniques and employs incremental learning to detect malicious apps. It presents a novel technique for constructing the PermGuard dataset by mapping Android permissions to exploitation techniques, providing a comprehensive understanding of how permissions can be misused by malware. The dataset consists of 55,911 benign and 55,911 malware apps, providing a balanced and comprehensive foundation for analysis. Additionally, a new strategy using similarity-based selective training reduces the amount of data required for the training of an incremental learning-based model, focusing on the most relevant data to improve efficiency. To ensure robustness and accuracy, the model adopts a test-then-train approach, initially testing on application data to identify weaknesses and refine the training process. The framework's resilience is tested against adversarial attacks, demonstrating its ability to withstand attempts to bypass or deceive detection mechanisms and enhance overall security. Designed for scalability, PermGuard can handle large and continuously growing datasets, making it suitable for real-world applications. Empirical results indicate that the model achieved an accuracy of 0.9933 on real datasets and 0.9828 on synthetic datasets, demonstrating strong resilience against both real and adversarial attacks.

关键词： Malware Operating systems Security Feature extraction Accuracy Internet Training machine learning machine learning algorithms Support vector machines Android malware detection machine learning permissions exploitation cybersecurity mobile security

来源：评论

学校读者我要写书评

暂无评论

A Design Framework for Scalable and Adaptive Multi-Agent Coordination in Dynamic Environments: Addressing Concurrent Agent and Environment Interactions

引用

IEEE ACCESS 2025年 13卷 67029-67055页

作者： Kazim, Raza Muhammad Wang, Guoxin Ming, Zhenjun Cao, Jinhui Allen, Janet K. Mistree, Farrokh Beijing Inst Technol Sch Mech Engn Beijing 100811 Peoples R China Beijing Inst Technol Yangtze Delta Reg Acad Jiaxing 314019 Zhejiang Peoples R China Univ Oklahoma Syst Realizat Lab Norman OK 73019 USA

In dynamic environments, such as box-pushing tasks, multi-agent systems (MAS) face significant challenges in coordinating agents within high-density settings while managing uncertainties arising from fluctuations in agent configurations and environmental dynamics. In this study, we explore the integration of surrogate response surface modeling (SRSM) with optimization algorithms, comparing Stochastic Gradient Descent (SGD) with a fixed learning rate, and adaptive learning rate (ALR) optimizers-including Adaptive Moment Estimation (ADAM) and Adaptive Approximate Direction Method Algorithm (AADMA)-to enhance MAS performance metrics, such as Agent Collision Rate (ACR), Agent Movement Frequency (AMF), and Task Completion Time (TCT). Through systematic experimentation across five scenarios, SRSM is employed to uncover key trends in MAS performance and identify configurations that improve scalability and adaptability. From the analysis of simulation data, it has been observed that SGD struggles significantly in dynamic environments, while ADAM demonstrates moderate improvements. However, AADMA consistently outperforms both by reducing loss, lowering collision rates, increasing movement efficiency, and achieving shorter task completion times. Performance comparison charts and loss function graphs emphasize AADMA's superiority in addressing the complexities of real-time coordination and adaptability. Through this study, we highlight the critical role of combining SRSM with ARL to design an MAS that is capable of thriving in complex, dynamic, and high-density environments. By addressing key scalability and adaptability challenges, the proposed framework significantly advances MAS design, paving the way for improved multi-agent coordination in real-world applications.

关键词： Multi-agent systems Scalability Heuristic algorithms Vehicle dynamics Adaptive learning Reinforcement learning Adaptation models Response surface methodology machine learning algorithms Robot sensing systems Multi-agent reinforcement learning adaptive learning rate box-pushing task multi-agent system dynamic environments scalability adaptability

来源：评论

学校读者我要写书评

暂无评论

FWCEC: An Enhanced Feature Weighting Method via Causal Effect for Clustering

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第2期37卷 685-697页

作者： Cao, Fuyuan Jing, Xuechun Yu, Kui Liang, Jiye Shanxi Univ Sch Comp & Informat Technol Key Lab Computat Intelligence & Chinese Informat P Minist Educ Taiyuan 030006 Shanxi Peoples R China Hefei Univ Technol Sch Comp & Informat Hefei 230009 Peoples R China

Feature weighting aims to assign different weights to features based on their importance in machine learning tasks. In clustering tasks, the existing methods learn feature importance based on the clustering results derived from the collaborative contribution of all features, which overlooks the independent effect of each feature. In fact, there are underlying causal relationships between features and the clustering results, and the features with high causal effects are always more crucial for clustering. Therefore, we propose an enhanced Feature Weighting method via Causal Effect for Clustering, calculating the causal effect of each feature on the clustering results for obtaining the independent contribution of each feature. Specifically, we start by identifying the causal relationships among the features and utilizing the causal relationships to generate a reasonable treatment group. Next, we compare the changes in the data distribution between the treatment and control groups to determine the causal effect of each feature. Finally, the causal effects of features are used for enhancing the clustering-driven weight learning. Moreover, we present a theory of relative order consistency in causal effect. Experimental results demonstrate that utilizing causal effect in weight learning facilitates efficient convergence and achieves superior accuracy compared to state-of-the-art clustering algorithms.

关键词： Clustering algorithms machine learning algorithms machine learning Partitioning algorithms Noise measurement Collaboration Linear programming Convergence Technological innovation Reviews Feature weights clustering causal effect feature importance

来源：评论

学校读者我要写书评

暂无评论

Continual learning for Human-machine Collaboration in VUCA Environments 25th

Continual Learning for Human-Machine Collaboration in VUCA E...

引用

25th IFIP WG 5.5 Working Conference on Virtual Enterprises (PRO-VE)

作者： Fan, Yuchen Antonelli, Dario Simeone, Alessandro Politecn Torino Dept Management & Prod Engn Corso Duca Abruzzi 24 Turin Italy

ISBN: (纸本)9783031717413;9783031717390;9783031717383

This study presents a novel approach to enhancing human-machine collaboration (HMC) in volatile, uncertain, complex, and ambiguous (VUCA) environments by emphasizing the importance of continual learning. Addressing the limitations of traditional static systems, the proposed HMC system integrates continual learning and object detection algorithms to enhance error handling, operational efficiency, and resilience. The research aims to establish a new standard for intelligent HMC systems, emphasizing ongoing reciprocal learning between humans and machines to improve decision-making and performance. Practical implementation demonstrates the system's effectiveness in reducing downtime and increasing adaptability. By integrating human expertise and machine intelligence, the system fosters improved problem-solving capabilities and operational efficiency, making it highly suitable for dynamic and unpredictable industrial settings. This study addresses critical gaps in current methodologies, providing a comprehensive framework for the future of HMC in complex manufacturing environments.

关键词： Human-machine Collaboration Continual learning VUCA Environments Adaptive Error Handling machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Leveraging Multilingual Transformer for Multiclass Sentiment Analysis in Code-Mixed Data of Low-Resource Languages

引用

IEEE ACCESS 2025年 13卷 7538-7554页

作者： Nazir, Muhammad Kashif Faisal, Cm Nadeem Habib, Muhammad Asif Ahmad, Haseeb Natl Text Univ Dept Comp Sci Faisalabad 37610 Pakistan Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Comp & Informat Sci Riyadh 11432 Saudi Arabia

The widespread use of online social media has enabled users to express their thoughts, feelings, opinions, and sentiments in their preferred languages. These diverse perspectives offer valuable insights for data-driven decision-making. While extensive sentiment analysis approaches have been developed for resource-rich languages like English and Chinese, low-resource languages such as Roman Urdu and Roman Punjabi, especially in code-mixed contexts, have been largely neglected due to the lack of datasets and limited research on their unique morphological structures and grammatical complexities. This study aims to present a novel approach for multiclass sentiment analysis of low-resource, code-mixed datasets using multilingual transformers. Specifically, a dataset comprising Roman Urdu, Roman Punjabi, and English comments was collected. After applying traditional natural language preprocessing techniques, transformer-based libraries were used for tokenization and embedding. Subsequently, the Multilingual Bidirectional Encoder Representations from Transformers (mBERT) model was optimized and trained for multiclass sentiment analysis on the code-mixed data. The evaluation results showed a significant improvement in accuracy (+22.55%), precision (+21.06%), recall (+22.55%), and F-measure (+25.50%) compared to benchmark algorithms. Additionally, the proposed model outperformed other transformer-based models, as well as deep learning and machine learning algorithms in sentiment extraction from code-mixed data. These findings highlight the potential of the proposed approach for sentiment analysis in low-resource, code-mixed languages.

关键词： Sentiment analysis Transformers Accuracy machine learning algorithms machine learning Deep learning Analytical models Data mining Support vector machines Social networking (online) Code-mixed dataset classification low resource languages mBERT sentiment analysis transformer

来源：评论

学校读者我要写书评

暂无评论

Shape Penalized Decision Forests for Imbalanced Data Classification

引用

IEEE ACCESS 2025年 13卷 86380-86395页

作者： Goswami, Rahul Garai, Aindrila Sadhukhan, Payel Ghosh, Palash Chakraborty, Tanujit Indian Inst Technol Dept Math Gauhati 781039 India Sorbonne Univ Abu Dhabi SAFIR Abu Dhabi U Arab Emirates Univ Bristol Sch Math Bristol BS8 1TR England Techno Main Dept Comp Sci & Engn IoT Kolkata 700091 India Sorbonne Univ Sorbonne Ctr Artificial Intelligence F-75005 Paris France

Class imbalance poses a critical challenge in binary classification problems, particularly when rare but significant events are underrepresented in the training set. While traditional machine learning models and modern deep learning techniques struggle with such imbalances, decision trees and random forests combined with data sampling strategies have shown effectiveness, especially for tabular datasets. However, undersampling and oversampling approaches often introduce complexity and risk the loss of valuable information. This paper introduces Shape Penalized Decision Forests, a novel classifier tailored for imbalanced binary classification. Our method integrates a penalty on the surface-to-volume ratio of decision sets within decision tree construction, thereby inherently addressing class imbalance without additional sampling. The proposed approach enhances predictive performance and generalization by leveraging ensemble learning strategies such as bagging and adaptive boosting. We evaluate the method on twenty benchmark tabular imbalanced datasets, spanning diverse sample sizes and imbalance ratios, and demonstrate its superiority over several state-of-the-art data-level and algorithmic-level methods. Furthermore, simulated datasets with visually interpretable structures showcase the model's generalization capacity. Statistical significance tests validate the robustness of our approach. Finally, we provide a Python package, 'imbalanced-spdf', offering an accessible implementation for practitioners and researchers.

关键词： Forests Shape Training Classification algorithms machine learning algorithms Ensemble learning Boosting Robustness Computational modeling Adaptation models Imbalanced data ensemble learning surface-to-volume ratio tabular data computational complexity

来源：评论

学校读者我要写书评

暂无评论

Adaptive Broad Network With Graph-Fuzzy Embedding for Imbalanced Noise Data

引用

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2025年第6期33卷 1949-1962页

作者： Chen, Wuxing Yang, Kaixiang Yu, Zhiwen Nie, Feiping Chen, C. L. Philip South China Univ Technol Sch Future Technol Guangzhou 510650 Guangdong Peoples R China Pengcheng Lab Shenzhen 518066 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Guangdong Peoples R China Northwestern Polytech Univ Sch Comp Sci Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

Broad learning system (BLS) is renowned for its excellent generalization and high efficiency in data classification. However, when confronted with class imbalance problems, BLS treats all samples as equally important, resulting in performance degradation. In addition, the presence of noise and outliers in imbalanced data further complicates BLS's ability to handle real-world classification problems. To address these challenges, this article proposes a graph-embedding intuitionistic fuzzy adaptive broad learning system (GEIB). The graph embedding strategy proposed by GEIB leverages the geometric topology of the data and class-specific information, effectively capturing variability among imbalanced samples and improving class separability. Furthermore, we introduce intuitionistic fuzzy theory. The BLS integrated with it considers both the homogeneity and heterogeneity of sample neighborhoods, enabling it to address uncertainty and imprecision in the data. It further differentiates clean samples from noisy ones in imbalanced datasets, thereby enhancing model robustness. To further investigate the prior distribution information of imbalanced data, we design an adaptive class-specific penalty mechanism based on global distribution and local density information. This mechanism accounts for both intraclass and interclass density information and class global distribution. We verify the superiority of our method by conducting a comparison with current approaches using real-world datasets that include both Gaussian noise and noise-free versions.

关键词： Noise learning systems Data models Training Noise measurement Fuzzy systems Costs Adaptation models Accuracy machine learning algorithms Broad learning system (BLS) graph embedding (GE) imbalance learning intuitionistic fuzzy (IF)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：