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Adaptive particle swarm optimization model for resource leveling

Evolving Systems 2023年第4期14卷 593-604页

作者： Lin, Jerry Chun-Wei Lv, Qing Yu, Dehu Srivastava, Gautam Chen, Chun-Hao School of Information and Control Engineering Qingdao University of Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway School of Civil Engineering Qingdao University of Technology Qingdao China School of Civil Engineering Shandong Jianzhu University Jinan China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan

In the process of engineering project construction, the balanced allocation of resources has an important impact on the purchase of actual materials, the progress of the site construction and the arrangement of temporary facilities. Although there have been many studies on the use of evolutionary computation (EC) to solve the fixed duration of the resource-leveling problem, the optimization effect of standard EC to solve the resource-balancing problem in large-scale network design planning is reduced due to the phenomenon of premature convergence of PSO and the complexity of large-scale network planning. Therefore, in this paper, an adaptive dissipative particle swarm optimization (ADPSO) algorithm is used to solve the resource balancing optimization problem for different network-plans scales, and the feasibility of the developed model is verified using four different scaled network plans (databases). At the same time, a solution to the dynamic resource-leveling problem with optimization for a fixed duration is proposed. The results show that the proposed models are suitable for solving the problem of resource-leveling with a fixed duration of optimization of different scale network plans, and their accuracy and stability are significantly improved compared to the state-of-the-art approaches. Moreover, the proposed models are accurate and suitable for combining the calculation method of network planning time parameters with the ADPSO algorithm to realize dynamic resource balancing optimization. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Particle swarm optimization (PSO)

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Examining Unsupervised Learning Techniques for Economic Forecasting 5th

Examining Unsupervised Learning Techniques for Economic Fore...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Negi, Bichitra Singh Minish, T. N. Sterlin Mishra, Awakash Karthikeyan, M. Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This newsletter examines a ramification of unsupervised mastering strategies for financial forecasting. The number one consciousness has been using strategies inclusive of k-suggest clustering and principal thing evaluation (PCA) to derive meaning from facts and generate forecasts for macroeconomic indicators consisting of gross home product (GDP) and stability of bills. We also discuss using neural networks (NNs) as software for unsupervised learning for monetary forecasting. Via a sequence of empirical experiments, we analyze and examine the forecasts generated through every unsupervised, gaining knowledge of methods for various macroeconomic indicators. The outcomes of the experiments suggest that NNs and PCA are most effective for monetary forecasting and discover that PCA is a promising tool for making forecasts with more excellent accurate effects than okay-manner. Moreover, the results offer insights on a way to use unsupervised learning for financial forecasting and suggest ability ways in which to enhance the accuracy of those forecasts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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An Ensemble Fake News Detection Model Using fakenewslingual dataset with Feature Selection 12

An Ensemble Fake News Detection Model Using fakenewslingual ...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Choong, Hoi Chun Chua, Hui Na Jasser, Muhammed Basheer Wong, Richard T.K. Aldharhani, Ghassan Saleh Sunway University No. 5 School of Engineering and Technology Department of Computing and Information Systems Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia

ISBN: (纸本)9798350391398

The rapid dissemination of information on social media acts as a double-edged sword, promoting the spread of accurate information and fake news, which can disrupt social cohesion and undermine trust in media and institutions. Existing fake news detection methods often lack feature comprehensiveness and struggle to generalize across different fake news types and sources. This study aims to enhance feature comprehensiveness by incorporating headline features into existing models and improve model generalization through ensemble techniques and hyperparameter tuning. The study expanded an existing dataset with headline, sentiment, and readability features. We assessed the impact of these additional features on model performance and explored various ensemble learning techniques (Bagging, AdaBoost, Gradient Boost) with tailored hyperparameters. An optimal model was then used to build a prototype for real and fake news classification. Integrating headline features showed marginally improved accuracy from 98.63% to 98.66%. While using ensemble learning models and hyperparameter tuning, accuracy increased significantly to 99.34%. This research enriched the dataset with headline features, enhancing the detection model's accuracy and generalization. The enhanced dataset, which we named fakenewslingual, has been made publicly available for further research. The study underscores the effectiveness of headline features and ensemble models in fake news detection. © 2024 IEEE.

关键词： Data accuracy

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Identifying Acute Disseminated Encephalomyelitis with Time Series Analysis 5th

Identifying Acute Disseminated Encephalomyelitis with Time S...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Pallavi, M. Mishra, Awakash Kudari, Jayashree M. Belwal, Ankit School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Acute disseminated encephalomyelitis (ADEM) is an extraordinary immune-mediated disease that influences the mind and spinal cord, central to the unexpected onset of neurologic symptoms. Time series evaluation can be used to become aware of ADEM in patients. Mainly, statistical analysis of the temporal styles of mind imaging and immunological parameters has proven effective for diagnosing ADEM in medical settings. Such material styles offer essential clues about disease development, which can be leveraged to diagnose ADEM patients appropriately. Acute disseminated encephalomyelitis (ADEM) is a critical degenerative sickness of the imperative nervous device, characterized by a rapid onset of neurologic signs and significant infection of the mind and spinal twine. Early analysis of ADEM is critical to initiate appropriate remedies and reduce long-term incapacity. In this look, we advocate the usage of time collection analysis to discover neurological patterns related to ADEM from the brain indicators of sufferers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Time series

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Topic identification of text-based expert stock comments using multi-level information fusion

Topic identification of text-based expert stock comments usi...

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作者： Zhao, Feng Zhang, Jiahui Chen, Zhiyuan Zhang, Xiaofeng Xie, Qingsong School of Computer Science and Technology Shandong Technology and Business University Yantai China Shandong Co-Innovation Center of Future Intelligent Computing Yantai China School of Information and Electrical Engineering Ludong University Yantai China

Stock investment is an important mode of asset allocation and a crucial means of financial management. How to grasp the movement of stock price and predict its trend have been the focus of investors and investment companies. Since expert stock comments contain abundant essential information for investment decisions, how to identify the topic of expert stock comments with high precision and efficiency is an important research topic. However, the existing methods usually employ single feature selection strategies for topic identification of stock comments, which may lead to low accuracy. Thus, to deal with this limitation, we propose a multi-level information fusion method to construct a topic identification system of stock comments. Specifically, we firstly fuse various complementary feature selection methods via a multi-view learning framework which can comprehensively represent text-based topics. In addition, regarding the decision process, we propose a fusion strategy based on belief value which can further improve the classification performance. The experimental results indicate that the proposed multi-level information fusion method is not only superior to other methods in terms of classification, it is also able to accurately capture topics of expert stock comments. © 2020 John Wiley & Sons Ltd.

关键词： information fusion

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Investigating the Advantages and Limitations of Math Modeling for Wireless Sensor Networks 5th

Investigating the Advantages and Limitations of Math Modelin...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Zaidi, Taskeen Ansari, Tabish Sindhuri, Bonda Prema Alam, Intekhab Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper investigates the blessings and obstacles of math modeling for wireless Sensor Networks (WSNs). WSNs may be immediately evaluated on a performance basis or analyzed to address protection and reliability troubles. However, mathematical models ignore the sensors’ heterogeneity and the networks’ distributed nature. Similarly, the mission is to validate the results to ensure actual performance. Other WSNs that need attention and mathematical modeling include their management algorithms, optimizations, MAC protocols, power management, and *** comparison, sure advantages exist in math modeling, understanding complex problems, coming across new solutions and situations, and the capacity to expect effects. This paper aims to review the advantages and obstacles of math modeling for WSNs and concludes by providing hints on how WSNs can gain from mathematical modeling. Math modeling holds the splendid capacity for wireless Sensor Networks (WSNs) because it comprehensively clarifies complex actual-global troubles. Math modeling offers an established framework to conceptualize, examine, and endorse answers for diverse WSN deployments. Its relative predictability and simulation accuracy can assist in perceiving hidden insights and generate reliable outcomes. However, it has some critical boundaries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： modeling Sensor simulation

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Enhancement of High Angular Resolution Diffusion-Weighted Imaging Through Parallel Imaging and Motion Correction 5th

Enhancement of High Angular Resolution Diffusion-Weighted Im...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Sandhya, Prajapati Sreejith, V. Alam, Intekhab Haripriya Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Excessive Angular decision Diffusion-Weighted Imaging (HARDI) is an effective imaging modality that produces high decision pictures of fiber tracts within the brain. Reconstruction of those pictures calls for a huge quantity of computational power and reminiscence, because it acquires appreciably extra diffusion weighted photographs than the conventional diffusion-weighted imaging (DWI) strategies. It makes use of multiple receiver coils in parallel, with every coil measuring the same records set. MC improves the accuracy of HARDI reconstruction by using correcting for motion artifacts. The application of PI and MC along with HARDI allows high-decision reconstruction of fiber tracts inside the mind, with stepped forward sign-to-noise ratio and decreased test instances. When used together, PI and MC also can improve the accuracy of HARDI tractography, providing extra designated records on fiber tract micros. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： measuring movement reconstruction

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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Assessments of Channel Coding Performance in Data Compression and Source Coding 5th

Assessments of Channel Coding Performance in Data Compressio...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Savita Bargavi, Manju Panwar, Rahul Shyam, Gopal Krishna Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Channel coding is a technique used to boom the performance and reliability of records transmission. It is able to be used for facts compression and source coding, that are the procedures of reducing the amount of records that desires to be despatched across a *** of channel coding performance can be performed with the aid of analyzing the performance of the records compression and source coding techniques. This can be executed thru diverse measures, such as compression ratio, supply coding charge, mistakes detection and correction, and bit blunders rate. With the intention to appropriately verify the performance of the channel coding approach, all of these measures must be considered. The aim of channel coding is to reap the highest viable fee of data compression with minimal errors detection and correction, and bit blunders rates as little as possible. This guarantees that the records is transmitted securely and efficaciously. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Coding errors

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Investigating the Role of Applied Cryptography in Digital Forensics 5th

Investigating the Role of Applied Cryptography in Digital Fo...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Nachappa, M.N. Negi, Bichitra Singh Dakhole, Dipali Khushalrao Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper examines the position of cryptography in virtual forensic evaluation. It aims to discover the one-of-a-kind sorts of cryptographic techniques available to investigators and to talk about how they may be applied to beautify the accuracy of digital forensic work. It also considers the character of cryptographic algorithms and their relative strengths and weaknesses, as well as the numerous applications of cryptographic software and devices in digital forensics. The paper then argues that using cryptography in digital forensic analysis can toughen virtual forensic effects by introducing additional safety features, allowing encrypted facts to be reliably recovered and investigated, and presenting a further degree of warranty while proof is given. It additionally addresses the ethical concerns that must be taken into consideration by virtual forensic investigators during the usage of cryptography in their work. Eventually, it identifies numerous avenues for future studies into using cryptography and its ability applications in virtual forensics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital forensics

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