A dagging‐based deep learning framework for transmission line flexibility assessment

Morteza, Azita; Sadipour, Masod; Fard, Reza Saadati; Taheri, Saman; Ahmadi, Amirhossein

doi:10.1049/rpg2.12663

Cited by 22 publications

(7 citation statements)

References 49 publications

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“…Deep learning models can directly classify images, sounds, or texts with high accuracy, outperforming humans [20]. The most popular ML algorithms for prediction include artificial neural networks (ANNs), k-nearest neighbors (k-NN), eXtreme gradient boosting (XGBoost), logistic regression (LR), support vector machine (SVR), random forest (RF), and decision tree [24].…”

Section: Machine Learningmentioning

confidence: 99%

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Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint

Al-Sinan,

Bubshait,

Aljaroudi

2024

Buildings

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Recent advancements in machine learning (ML) applications have set the stage for the development of autonomous construction project scheduling systems. This study presents a blueprint to demonstrate how construction project schedules can be generated automatically by employing machine learning (ML) and building information modeling (BIM). The proposed solution should utilize building information modeling (BIM) international foundation class (IFC) 3D files of previous projects to train the ML model. The training schedules (the dependent variable) are intended to be prepared by an experienced scheduler, and the 3D BIM files should be used as the source of the scheduled activities. Using the ML model can enhance the generalization of model application to different construction projects. Furthermore, the cost and required resources for each activity could be generated. Accordingly, unlike other solutions, the proposed solution could sequence activities based on an ML model instead of manually developed constraint matrices. The proposed solution is intended to generate the duration, cost, and required resources for each activity.

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Section: Machine Learningmentioning

confidence: 99%

“…These subsets serve as distinct training sets for the base learners. These learners' predictions are then synthesized by a meta-learner to produce a final prediction that typically has higher reliability and performance than any single learner's output [24].…”

Section: Model Trainingmentioning

confidence: 99%

Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint

Al-Sinan,

Bubshait,

Aljaroudi

2024

Buildings

View full text Add to dashboard Cite

show abstract

“…The SVM method has also been compared to the dagging‐based deep learning technique, which provides grid‐side stability by predicting the overall current carrying potential of overhead transmission lines. This method outperforms the average prediction accuracy of SVM by 6.7% [115].…”

Section: Algorithmsmentioning

confidence: 99%

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Kucova,

Prauzek,

Konecny

et al. 2023

CAAI Trans on Intel Tech

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Initiatives to minimise battery use, address sustainability, and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things (IoT) networks. As a key pillar of fifth generation (5G) and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025. Thermoelectric generators (TEGs) are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy. These devices are able to recover lost thermal energy, produce energy in extreme environments, generate electric power in remote areas, and power micro‐sensors. Applying the state of the art, the authorspresent a comprehensive review of machine learning (ML) approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy. The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment, biological structures, machines, and other technologies are summarised. Based on detailed research of the state of the art in TEG‐powered devices, the authors investigated the research challenges, applied algorithms and application areas of this technology. The aims of the research were to devise new energy prediction and energy management systems based on ML methods, create supervised algorithms which better estimate incoming energy, and develop unsupervised and semi‐supervised approaches which provide adaptive and dynamic operation. The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability, usability in ubiquitous temperature difference scenarios, and long operating lifetime. However, TEGs also have low energy efficiency (around 10%) and require a relatively constant heat source.

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“…The timely identification and effective treatment of cancer are paramount for enhancing patient outcomes and curbing disease progression (Mohtasebi et al, 2023). As the landscape of cancer prediction evolves, artificial intelligence (AI) technologies have emerged as powerful tools to streamline this process (Monjezi et al, 2023;Morteza et al, 2023;Rezaei et al, 2023;Zeinali-Rafsanjani et al, 2023). However, challenges persist, particularly in accurately categorizing cancer stages based on gene sets.…”

Section: Introductionmentioning

confidence: 99%

Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study

Amanzholova,

Coşkun

2024

Front. Big Data

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Efficiently detecting and treating cancer at an early stage is crucial to improve the overall treatment process and mitigate the risk of disease progression. In the realm of research, the utilization of artificial intelligence technologies holds significant promise for enhancing advanced cancer diagnosis. Nonetheless, a notable hurdle arises when striving for precise cancer-stage diagnoses through the analysis of gene sets. Issues such as limited sample volumes, data dispersion, overfitting, and the use of linear classifiers with simple parameters hinder prediction performance. This study introduces an innovative approach for predicting early and late-stage cancers by integrating hybrid deep neural networks. A deep neural network classifier, developed using the open-source TensorFlow library and Keras network, incorporates a novel method that combines genetic algorithms, Extreme Learning Machines (ELM), and Deep Belief Networks (DBN). Specifically, two evolutionary techniques, DBN-ELM-BP and DBN-ELM-ELM, are proposed and evaluated using data from The Cancer Genome Atlas (TCGA), encompassing mRNA expression, miRNA levels, DNA methylation, and clinical information. The models demonstrate outstanding prediction accuracy (89.35%−98.75%) in distinguishing between early- and late-stage cancers. Comparative analysis against existing methods in the literature using the same cancer dataset reveals the superiority of the proposed hybrid method, highlighting its enhanced accuracy in cancer stage prediction.

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A dagging‐based deep learning framework for transmission line flexibility assessment

Cited by 22 publications

References 49 publications

Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint

Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study

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