Search Approach Research Articles

THE IMPACT OF PREOPERATIVE STEROIDS ADMINISTRATION ON ENDOSCOPIC SINUS SURGERY: A COMPREHENSIVE SYSTEMATIC REVIEW

Background: Chronic rhinosinusitis (CRS) is one of the most common diseases in the world, affecting approximately 14 of people in the USA. It is defined as chronic inflammation of nasal and sinus cavities lasting longer than 12 weeks, it can be with or without nasal polyposis (CRSwNP or CRSsNP), primary and secondary, localized or diffuse disease regarding anatomic distribution, either type 2 or non-type 2, considering the endotype dominance, eCRS, and non-eCRS for eosinophilic and non-eosinophilic CRS. The aim: This study aims to show about the impact of preoperative steroids administration on endoscopic sinus surgery. Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as up-to-date as it was possible to be. For this search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed and SagePub, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: In the PubMed database, the results of our search brought up 51 articles, whereas the results of our search on SagePub brought up 74 articles. The results of the search conducted for the last year of 2014 yielded a total 24 articles for PubMed and 48 articles for SagePub. The result from title screening, a total 5 articles for PubMed and 12 articles for SagePub. In the end, we compiled a total of 8 papers. We included five research that met the criteria. Conclusion: Low dose preoperative systemic steroid (PSS) does not seem to have an effect on the long term outcome after endoscopic sinus surgery (ESS) in chronic rhinosinusitis (CRS) with nasal polyposis (NP). Eosinophilic infiltration and presence of asthma are important predictors of surgical outcome in such cases.

ARTIFICIAL INTELLIGENCE IN EYE DISEASE: AN UPDATE SYSTEMATIC REVIEW

Background: In recent years, AI has significantly revolutionized the healthcare industry, with deep learning applications being used to identify various illnesses, evaluate cancerous lesions, and determine stroke onset. AI-based systems also have been applied in ophthalmology to address leading eye diseases. The aim: This study aims to determine the role of artificial intelligence (AI) in eye disease. Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as up-to-date as it was possible to be. For this search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed and ScienceDirect, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Results: In the PubMed database, the results of our search brought up 157 articles, whereas the results of our search on ScienceDirect brought up 256 articles. The results of the search conducted by title screening yielded a total of 34 articles for PubMed and 28 articles for ScienceDirect. We compiled a total of 16 papers, 10 of which came from PubMed and 6 of which came from ScienceDirect. We excluded 4 review articles, 2 non-full text articles, 3 articles having insufficient outcomes, and 1 article having ineligible subjects. In the end, we included six research that met the criteria. Conclusion: Our systematic study suggests that AI has a role in the diagnosis or screening of eye disease. AI can be a valuable tool for diabetic retinopathy (DR) screening, glaucoma screening, myopia screening, and diagnosis of dry eye syndrome.

Два метода локального поиска в планировании открытых горных работ

This paper aims to investigate ways of planning open pit mining using local search (genetic algorithms are not disscussed in the paper). Open pit mining is a complex and multifaceted problem that requires efficient planning to optimize mineral extraction. The Local search methods are a powerful optimization tool to search for local optima in the space of lage size. This paper reviews two of the most commonly used local search approaches for open pit mining planning.

MULTIMODALITY IMAGING OF COVID-19 PNEUMONIA : A SYSTEMATIC REVIEW

Background: Reversible transcription polymerase chain reaction (RT-PCR) nasopharyngeal or oropharyngeal swab tests that yield a positive result can establish the diagnosis of COVID-19. Due to the high prevalence of false-negative findings, especially in the early stages of the disease, and the patchy availability of testing, a methodical approach to diagnosis that incorporates radiologic imaging is required. Aims : This systematic review is to review the multimodality imaging of patients with COVID-19 pneumonia. Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as up-to-date as it was possible to be. For this search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed and SAGEPUB, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: In the PubMed database, the results of our search brought up 22.043 articles, whereas the results of our search on SAGEPUB brought up 19.007 articles. The results of the search conducted for the last year of 2014 yielded a total 173 articles for PubMed and 98 articles for SAGEPUB. In the end, we compiled a total of 6 papers, 5 of which came from PubMed and 1 of which came from SAGEPUB. We included five research that met the criteria. Conclusion: In summary, owing to the SARS-CoV-2 pandemic, it is critical to understand the typical and atypical imaging features of COVID-19 pneumonia as well as how they change over time on CXR and HRCT. When evaluating hospitalized and critically sick patients in a serial fashion, as well as in places with high levels of contagion, computed tomography (CXR) may be the initial imaging modality employed

Narrowing and heuristic search for symbolic reachability analysis of concurrent object-oriented systems

A concurrent system specified as a rewrite theory can be analyzed symbolically using narrowing-based reachability analysis. Narrowing-based approaches have been applied to formally analyze cryptographic protocols and parameterized protocols. However, existing narrowing-based analysis methods, based on a breadth-first-search strategy, cannot deal with generic distributed systems with objects and messages due to the symbolic state-space explosion problem and implicit constraints imposed on object-oriented systems. This paper proposes a heuristic search approach for narrowing-based reachability analysis to guide the search for counterexamples with a small number of objects. As a result, our method can effectively find a counterexample if an error state is reachable. In addition, this paper also shows how to encode implicit object-oriented constraints using order-sorted signatures and equational constraints. We demonstrate the effectiveness of our technique using a nontrivial distributed consensus algorithm.

ASSOCIATION BETWEEN MEDICATION USE AND BULLOUS PEMPHIGOID: A TEN-YEAR SYSTEMATIC REVIEW

Background: Bullous pemphigoid is an autoimmune subepithelial disease. It was associated with autoantibodies targeting hemidesmosome protein BPAG1 and BPAG2. The incidence of bullous pemphigoid has increased 1.9-4.3 times in the past two decades, possibly due to increased life expectancy, medication use, and improved diagnostic methods. Trigger factors of BP included drugs, physical factors, radiotherapy, trauma, and thermal exposure. The aim: This study aims to determine association between medication use and bullous pemphigoid. Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as up-to-date as it was possible to be. For this search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed and ScienceDirect, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Results: In the PubMed database, the results of our search brought up 1,662 articles, whereas the results of our search on ScienceDirect brought up 1,143 articles. The results of the search conducted by title screening yielded a total of 13 articles for PubMed and 32 articles for ScienceDirect. We compiled a total of 26 papers, 8 of which came from PubMed and 18 of which came from ScienceDirect. We excluded 2 review articles, 1 duplicate article, 3 non-full text articles, and 3 articles having insufficient outcomes. In the end, we included seventeen research that met the criteria. Conclusion: Our study suggests that dipeptidyl peptidase-4 inhibitors and aldosterone antagonists are associated with BP. Other medications, such as immunomodulatory agents, anticonvulsants, tyrosine kinase inhibitors, antibiotics, anticoagulants, diuretics, and mRNA COVID-19 vaccine, may be associated with BP. Regarding these results, clinicians should pay close attention to prescribing the medicine to their patients.

COMPUTED-ASSISTED DIAGNOSIS IN DIAGNOSTIC CERVICAL CANCER IMAGING : A TEN YEARS SYSTEMATIC REVIEW

Background: Cervical cancer is one of the most common malignant tumors in the world, and it is the fourth leading cause of cancer in women. The morbidity and mortality of cervical cancer in the developing countries are distinctly higher than those in the developed countries. Computer-assisted diagnosis is key for scaling up cervical cancer screening, but current algorithms perform poorly on whole slide image analysis and generalization. The aim: This study aims to show about computed-assisted diagnosis in diagnostic cervical cancer imaging. Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as up-to-date as it was possible to be. For this search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed and SagePub, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: In the PubMed database, the results of our search brought up 153 articles, whereas the results of our search on SagePub brought up 180 articles. The results of the search conducted for the last year of 2014 yielded a total 53 articles for PubMed and 72 articles for SagePub. The result from title screening, a total 18 articles for PubMed and 27 articles for SagePub. In the end, we compiled a total of 10 papers. We included five research that met the criteria. Conclusion: Computed-assisted medical diagnosis can successfully complete a variety of medical tasks by efficiently exploring the essence of a large amount of clinical data. The colposcopy-guided cervical biopsy is essential for detecting CIN in cervical cancer screening, but there are difficulties with increasing sensitivity globally.

THE ASSOCIATION OF CARDIOVASCULAR DISEASE AND TYPE 2 DIABETES IN ADULTS WITH ATOPIC DERMATITIS: A COMPREHENSIVE SYSTEMATIC REVIEW

Background: Atopic dermatitis (AD) is one of the most common inflammatory diseases in medicine, associated with a broad patient burden of skin lesions, pruritus, and both allergic and non-allergic comorbidities. The aim: This study aims to show about the association of cardiovascular disease and type 2 diabetes in adults with atopic dermatitis. Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as up-to-date as it was possible to be. For this search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed and SagePub, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: In the PubMed database, the results of our search brought up 169 articles, whereas the results of our search on SagePub brought up 60 articles. The results of the search conducted for the last year of 2014 yielded a total 41 articles for PubMed and 46 articles for SagePub. The result from title screening, a total 5 articles for PubMed and 20 articles for SagePub. In the end, we compiled a total of 10 papers. We included five research that met the criteria. Conclusion: The link between AD and other allergic diseases such as food allergies, allergic rhinitis, and asthma is well known. Recently, in addition to allergic diseases, research has been conducted on the relationship between AD and other diseases such as depression, alopecia areata, inflammatory bowel disease, and cardiovascular disease (CVD) and metabolic syndrome like type 2 diabetes.

Annotating microbial functions with ProkFunFind.

Analyzing microbial genomes has become an essential part of microbiology research, giving valuable insights into the functions and evolution of microbial species. Identifying genes of interest and assigning putative annotations to those genes is a central task in genome analysis, and a plethora of tools and approaches have been developed for this task. The ProkFunFind tool was developed to bridge the gap between these various annotation approaches, providing a flexible and customizable search approach to annotate microbial functions. ProkFunFind is designed around hierarchical definitions of biological functions, where individual genes can be identified using heterogeneous search terms consisting of sequences, profile hidden Markov models, protein domains, and orthology groups. This flexible and customizable search approach allows for searches to be tailored to specific biological functions, and the search results are output in multiple formats to facilitate downstream analyses. The utility of the ProkFunFind search tool was demonstrated through its application in searching for bacterial flagella, which are complex organelles composed of multiple genes. Overall, ProkFunFind provides an accessible and flexible way to integrate multiple types of annotation and sequence data while annotating biological functions in microbial genomes.IMPORTANCEGenome sequencing and analysis are increasingly important parts of microbiology, providing a way to predict metabolic functions, identify virulence factors, and understand the evolution of microbes. The expanded use of genome sequencing has also brought an abundance of search and annotation methods, but integrating the information from these different methods can be challenging and is often done through ad hoc approaches. To bridge the gap between different types of annotations, we developed ProkFunFind, a flexible and customizable search tool incorporating multiple search approaches and annotation types to annotate microbial functions. We demonstrated the utility of ProkFunFind by searching for gene clusters encoding flagellar genes using a combination of different annotation types and searches. Overall, ProkFunFind provides a reproducible and flexible way to identify gene clusters of interest, facilitating the meaningful analysis of new and existing microbial genomes.

Robust machine learning models: linear and nonlinear

AbstractArtificial Intelligence relies on the application of machine learning models which, while reaching high predictive accuracy, lack explainability and robustness. This is a problem in regulated industries, as authorities aimed at monitoring the risks arising from the application of Artificial Intelligence methods may not validate them. No measurement methodologies are yet available to jointly assess accuracy, explainability and robustness of machine learning models. We propose a methodology which fills the gap, extending the Forward Search approach, employed in robust statistical learning, to machine learning models. Doing so, we will be able to evaluate, by means of interpretable statistical tests, whether a specific Artificial Intelligence application is accurate, explainable and robust, through a unified methodology. We apply our proposal to the context of Bitcoin price prediction, comparing a linear regression model against a nonlinear neural network model.

Generalized planning as heuristic search: A new planning search-space that leverages pointers over objects

Planning as heuristic search is one of the most successful approaches to classical planning but unfortunately, it does not trivially extend to Generalized Planning (GP); GP aims to compute algorithmic solutions that are valid for a set of classical planning instances from a given domain, even if these instances differ in their number of objects, the initial and goal configuration of these objects and hence, in the number (and possible values) of the state variables. State-space search, as it is implemented by heuristic planners, becomes then impractical for GP. In this paper we adapt the planning as heuristic search paradigm to the generalization requirements of GP, and present the first native heuristic search approach to GP. First, the paper introduces a new pointer-based solution space for GP that is independent of the number of classical planning instances in a GP problem and the size of those instances (i.e. the number of objects, state variables and their domain sizes). Second, the paper defines an upgraded version of our GP algorithm, called Best-First Generalized Planning (BFGP), that implements a best-first search in our pointer-based solution space for GP. Lastly, the paper defines a set of evaluation and heuristic functions for BFGP that assess the structural complexity of the candidate GP solutions, as well as their fitness to a given input set of classical planning instances. The computation of these evaluation and heuristic functions does not require grounding states or actions in advance. Therefore our GP as heuristic search approach can handle large sets of state variables with large numerical domains, e.g. integers.

Derivative-free search approaches for optimization of well inflow control valves and controls

Decisions regarding problem conceptualization, search approach, and how best to parametrize optimization methods for practical application are key to successful implementation of optimization approaches within georesources field development projects. This work provides decision support regarding the application of derivative-free search approaches for concurrent optimization of inflow control valves (ICVs) and well controls. A set of state-of-the-art approaches possessing different search features is implemented over two reference cases, and their performance, resource requirements, and specific method configurations are compared across multiple problem formulations for completion design. In this study, problem formulations to optimize completion design comprise fixed ICVs and piecewise-constant well controls. The design is optimized by several derivative-free methodologies relying on parallel pattern-search (tAPPS), population-based stochastic sampling (tPSO) and trust-region interpolation-based models (tDFTR). These methodologies are tested on a heterogeneous two-dimensional case and on a realistic case based on a section of the Olympus benchmark model. Three problem formulations are applied in both cases, i.e., one formulation optimizes ICV settings only, while two joint configurations also treat producer and injector controls as variables. Various method parametrizations across the range of cases and problem formulations exploit the different search features to improve convergence, achieve final objectives and infer response surface features. The scope of this particular study treats only deterministic problem formulations. Results outline performance trade-offs between parallelizable algorithms (tAPPS, tPSO) with high total runtime search efficiency and the local-search trust-region approach (tDFTR) providing effective objective gains for a low number of cost function evaluations. tAPPS demonstrates robust performance across different problem formulations that can support exploration efforts, e.g., during a pre-drill design phase while multiple independent tDFTR runs can provide local tuning capability around established solutions in a time-constrained post-drill setting. Additional remarks regarding joint completion design optimization, comparison metrics, and relative algorithm performance given the varying problem formulations are also made.

THE SYSTEMATIC REVIEW OF ADULTS AIRWAY DEVICES IN AWAKE TRACHEAL INTUBATION

Background: Although flexible bronchoscopes are often used for awake tracheal intubation, other airway instruments including videolaryngoscopes, direct laryngoscopes, and optical stylets are becoming more and more useful. Aims : This systematic review is to review the the effects for airway devices on patients with awake tracheal intubation. Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as up-to-date as it was possible to be. For this search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed and SCIENCE DIRECT, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: In the PubMed database, the results of our search brought up 431 articles, whereas the results of our search on SCIENCE DIRECT brought up 289 articles. The results of the search conducted for the last year of 2014 yielded a total 23 articles for PubMed and 115 articles for SCIENCE DIRECT. In the end, we compiled a total of 6 papers, 5 of which came from PubMed and 1 of which came from SCIENCE DIRECT. We included six research that met the criteria. Conclusion: In summary, in the context of awake tracheal intubation, flexible bronchoscopes, optical stylets, and channelled and unchannelled videolaryngoscopes were clinically equivalent airway devices. The time to tracheal intubation was longest with flexible bronchoscopes and shortest with optical stylets.

THE SYSTEMATIC REVIEW OF EXAMINING THE CONNECTION BETWEEN NUTRITION AND VERTIGO

Aim : This study examined the association and connections between the uptake of nutrition and vertigo Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as uptodate as it was possible to be. For this search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed and Science Direct, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: In the PubMed database, the results of our search brought up 93 articles, whereas the results of our search on Science Direct brought up 163 articles. The results of the search conducted for the last year of 2014 yielded a total 67 articles for PubMed and 12 articles for Science Direct. In the end, we compiled a total of 4 papers, 2 of which came from PubMed and 2 of which came from Science Direct. We included four research that met the criteria. Conclusion: In summary, nutrient intake and consumption frequency of certain food categories varied significantly.

Intent-Oriented Dynamic Interest Modeling for Personalized Web Search

Given a user, a personalized search model relies on her historical behaviors, such as issued queries and their clicked documents, to generate an interest profile and personalize search results accordingly. In interest profiling, most existing personalized search approaches use “static” document representations as the inputs, which do not change with the current search. However, a document is usually long and contains multiple pieces of information, a static fix-length document vector is usually insufficient to represent the important information related to the original query or the current query, and makes the profile noisy and ambiguous. To tackle this problem, we propose building dynamic and intent-oriented document representations which highlight important parts of a document rather than simply encode the entire text. Specifically, we divide each document into multiple passages, and then separately use the original query and the current query to interact with the passages. Thereafter we generate two “dynamic” document representations containing the key information around the historical and the current user intent, respectively. We then profile interest by capturing the interactions between these document representations, the historical queries, and the current query. Experimental results on a real-world search log dataset demonstrate that our model significantly outperforms state-of-the-art personalization methods.

Ethical considerations for artificial intelligence indermatology: a scoping review.

The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications (apps) for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aimed to identify the applications of AI to the field of dermatology and to understand their ethical implications. We used a multifaceted search approach, searching PubMed, MEDLINE, Cochrane Library and Google Scholar for primary literature, following the PRISMA Extension for Scoping Reviews guidance. Our advanced query included terms related to dermatology, AI and ethical considerations. Our search yielded 202 papers. After initial screening, 68 studies were included. Thirty-two were related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, privacy violations and replacement of dermatologist jobs. Seventeen discussed limited skin of colour representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use and privacy challenges. Seven addressed inaccuracies in the responses of large language models. Seven examined attitudes toward and trust in AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of AI integration into clinical practice include increased patient access, improved clinical decision-making, efficiency and many others. However, safeguards must be put in place to ensure the ethical application of AI.

Encrypted Video Search with Single/Multiple Writers

Video-based services have become popular. Clients often outsource their videos to the cloud to relieve local maintenance. However, privacy has become a major concern since many videos contain sensitive information. Although retrieving (unencrypted) videos has been extensively investigated, retrieving encrypted multimedia has received relatively rare attention, at best in a limitation of image-based similarity searches. We initiate the study of scalable encrypted video search, enabling clients to query videos similar to an image search. Our modular framework leverages intrinsic attributes of videos, such as semantics and visuals, to effectively capture their contents. We propose a two-step approach whereby lightweight searchable encryption techniques are used for pre-screening, followed by an interactive approach for fine-grained search. Furthermore, we present three instantiations, including one centralized-writer instantiation and two distributed-writer instantiations, to effectively cater to varying needs and scenarios– 1) The centralized one employs forward and backward private searchable encryption [CCS 2017] over deep hashing [CVPR 2020]. 2) Motivated by distributed computing, the multi-writer instantiations building atop HSE [Usenix Security 2022] allows searching the relevant videos contributed by multiple intuitions collaboratively. Our experimental results illustrate their practical performance over multiple real-world datasets, whether in a centralized setting or distributed setting.

A Population-Based Search Approach to Solve Continuous Distributed Constraint Optimization Problems

Distributed Constraint Optimization Problems (DCOPs) are an efficient framework widely used in multi-agent collaborative modeling. The traditional DCOP framework assumes that variables are discrete and constraint utilities are represented in tabular forms. However, the variables are continuous and constraint utilities are in functional forms in many practical applications. To overcome this limitation, researchers have proposed Continuous DCOPs (C-DCOPs), which can model DCOPs with continuous variables. However, most of the existing C-DCOP algorithms rely on gradient information for optimization, which means that they are unable to solve the situation where the utility function is a non-differentiable function. Although the Particle Swarm-Based C-DCOP (PCD) and Particle Swarm with Local Decision-Based C-DCOP (PCD-LD) algorithms can solve the situation with non-differentiable utility functions, they need to implement Breadth First Search (BFS) pseudo-trees for message passing. Unfortunately, employing the BFS pseudo-tree results in expensive computational overheads and agent privacy leakage, as messages are aggregated to the root node of the BFS pseudo-tree. Therefore, this paper aims to propose a fully distributed C-DCOP algorithm to solve the utility function form problem and avoid the disadvantages caused by the BFS pseudo-tree. Inspired by the population-based algorithms, we propose a fully decentralized local search algorithm, named Population-based Local Search Algorithm (PLSA), for solving C-DCOPs with three-fold advantages: (i) PLSA adopts a heuristic method to guide the local search to achieve a fast search for high-quality solutions; (ii) in contrast to the conventional C-DCOP algorithm, PLSA can solve utility functions of any form; and (iii) compared to PCD and PCD-LD, PLSA avoids complex message passing to achieve efficient computation and agent privacy protection. In addition, we implement an extended version of PLSA, named Population-based Global Search Algorithm (PGSA), and empirically show that our algorithms outperform the state-of-the-art C-DCOP algorithms on three types of benchmark problems.

SecEdge: Secure Edge-Computing-Based Hybrid Approach for Data Collection and Searching in IoV

<i>SecEdge</i>: Secure Edge-Computing-Based Hybrid Approach for Data Collection and Searching in IoV

A Hybrid Fuzzy Predictive DVR Model for Voltage Estimation Using Intelligent Learning

A self-constructing intelligence-based learning law is proposed for the prediction of power quality mitigation through a dynamic voltage restorer (DVR). To tackle voltage-based power quality concerns in the distribution network, a fuzzy neural-based hybridization control approach using an evolving Takagi–Sugeno–Kang (e-TSK) and radial basis function (RBFN) is developed. The estimated value of fundamental weight components is achieved by the eTSK predictive model. The evaluated weight values are used to update the parameters for the generation of the reference load voltage components. The RBFN is used to achieve the desired task of dc and ac link voltage regulation. The self-tuned control algorithm is developed based on an intelligence technique that avoids the trapping of local optima, and early convergence, and reduces the voltage oscillation to obtain an optimal solution. A meta-heuristic optimization approach of grey wolf optimization (GWO) and grey wolf-cuckoo search (GWOCS) is selected to optimize the parameters of eTKS and RBFN to find the best-fitted predictive model under the dynamic state. The hybridization of the model with GWO and GWOCS is implemented for improving the training and generalization capability. The performance of VSC-based DVR is validated using the d-SPACE Micro lab box under the various power quality scenario.