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aryan-ila/Search-algorithm: Search-algorithm

aryan-ila · Zenodo (CERN European Organization for Nuclear Research) · 2024

Search-algorithm

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Search algorithm.

Andre L. Mihaljevic (8544627), Colette Doerr-Harim (6813947), Eva Kalkum (4787631) et al. · Figshare · 2022

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Search Algorithm.

Sharon S. Nakhimovsky (3138477), Andrea B. Feigl (3072699), Carlos Avila (361477) et al. · Figshare · 2016

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Search algorithm.

Pieter W. Smit (458975), Kimberly A. Sollis (319691), Susan Fiscus (243736) et al. · Figshare · 2015

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Search algorithm.

Alexandra Garcia-Rosales (18017671), Samuele Cortese (9154963), Silia Vitoratou (5825897) · Figshare · 2024

<div><p>This systematic review aimed to establish the extent to which each Attention Deficit/Hyperactivity Disorder (ADHD) symptom criterion is being assessed without being influenced (biased) by factors such as informant, sex/gender, and age. Measurement invariance (MI) testing using confirmatory factor analysis (CFA) is the prime statistical method to ascertain how these factors may affect the measurement and colour the perception or interpretation of symptom criteria. Such effects (non-invariance) can be operationalised in the form of altered association of a symptom criterion with the measured trait (expressed via variations in CFA loadings which represent the weight of each symptom criterion) due to the factor(s) and/or artificially alter the probability of endorsement of a particular symptom criterion (expressed via variations in the CFA threshold(s) representing how mild or severe a given symptom is). Based on a pre-registered protocol (CRD42022276105), we searched PubMed, Global Health, Embase and PsycInfo up to 21-02-23 for studies that included MI assessments on specific ADHD symptom criteria in individuals aged 0–18 years old, using parental and/or teacher report. Self-reports were excluded, given the poor reliability of self-report in ADHD. All included studies met specific COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) criteria. Results were synthesised in tabular form, grouping results by factors (e.g. informant) from 44 studies retained. Most comparisons indicated both metric (same loadings) and scalar invariance (same thresholds) with regard to informant, gender, age, temporal (repeated assessments) and co-morbidity. Therefore, the available evidence supports the current diagnostic criteria. However, findings could have been improved by systematic reporting of the direction of bias and its effect size. There appears to be a bias towards reporting MI instead of non-invariance. More studies in the literature are needed where the amalgamation of information provided by different informs and the association of specific symptoms with comorbidity are analysed.</p></div>

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Search Algorithm

Tao Benjamin Schardl, Charles E. Leiserson, Tao Benjamin Schardl · 2010

I have developed a multithreaded implementation of breadth-first search (BFS) of a sparse graph using the Cilk++ extensions to C++. My PBFS program on a single processor runs as quickly as a standard C++ breadth-first search implementation. PBFS achieves high work-efficiency by using a novel implementation of a multiset data structure, called a “bag, ” in place of the FIFO queue usually employed in serial breadth-first search algorithms. For a variety of benchmark input graphs whose diameters are significantly smaller than the number of vertices — a condition met by many real-world graphs — PBFS demonstrates good speedup with the number of processing cores. Since PBFS employs a nonconstant-time “reducer ” — a “hyperobject ” feature of Cilk++ — the work inherent in a PBFS execution depends nondeterministically on how the underlying work-stealing scheduler load-balances the computation. I provide a general method for analyzing nondeterministic programs that use reducers. PBFS also is nondeter-ministic in that it contains benign races which affect its performance but not its correctness.

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Search algorithm and algorithm outputs.

Claire Chambers (777152), Gaiqing Kong (6808118), Kunlin Wei (284229) et al. · Figshare · 2019

<p>(<b>A</b>) Flowchart describing stages of search algorithm used to find segments of videos containing pairs of walkers. We (i) extracted frames from subsampled videos, (ii) fit pose to the frames using OpenPose, (iii) detected pairs from pose estimates, then (iv) identified sequences of frames that contained pairs of people, resulting in a set of pose estimates from frame sequences. (<b>B</b>) Example video frame containing a walking pair with overlaid pose estimates. A bounding box highlights the persons identified as a pair. (<b>C</b>) Examples of walking pairs with overlaid pose estimates.</p>

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Cuckoo Search Algorithm

Kaushik Kumar, Divya Zindani, J. Paulo Davim · 2019

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Cuckoo Search Algorithm

Swarm Intelligence · 2018

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Gravity Search Algorithm

Omid Bozorg‐Haddad, Mohammad Solgi, Hugo A. Loáiciga · 2017

This chapter describes the gravity search algorithm (GSA). The GSA is an evolutionary optimization algorithm based on the law of gravity and mass interactions. The GSA designates a particle as a solution of an optimization problem. Particles exhibit simple behavior, and they follow intelligent pathways towards the near-optimal solution. This chapter presents a literature review of the GSA and its applications, explains the GSA's analogy to the law of gravity and the GSA in detail, and closes with a pseudo code of the GSA.

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Viral Search algorithm

Matteo Gardini · arXiv (Cornell University) · 2016

The article, after a brief introduction on genetic algorithms and their functioning, presents a kind of genetic algorithm called Viral Search. We present the key concepts, we formally derive the algorithm and we perform numerical tests designed to illustrate the potential and limits.

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Literature search algorithm.

Yohwan Yeo (577750), Seung-Hyun Ma (577751), Yunji Hwang (577752) et al. · Figshare · 2015

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Binary search algorithm

Oskirko, A. V., Pogonya, S. S. · Repository of Belarusian National Technical University (Belarusian National Technical University) · 2025

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Quantum search algorithm

Lukáš Mejdrech · Digital Repository (National Repository of Grey Literature) · 2007

In this work we study Grover's algorithm for quantum computers. This algorithm promises to search in an unstructured database in time comparable with a square root of the number of objects. A description of basic quantum computer principles is followed by the original Grover's formulation and a proof of the algorithm, as well as a later geometrical description of the algorithm. We also mention their suitability for educational purposes. We also include a description of the Grover's algorithm in a programming language, which makes it possible to simulate a quantum computing on classical computers.

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Gravitational Search Algorithm

Amin Hashemi, Mohammad Bagher Dowlatshahi, Hossein Nezamabadi–pour · 2021

Today, many metaheuristics algorithms have been developed are inspired by the physical phenomena or behaviors of natural creatures that are very effective in solving complex engineering optimization problems. These algorithms seek to find optimal solutions in various engineering problems such as machine learning, image processing, pattern matching, decision making, and so on. These algorithms can be implemented in both single-objective and multi-objective optimization problems. Among the most famous techniques in this category include the ant colony optimization (ACO) algorithm, which is designed based on the navigation of ants; particle swarm optimization (PSO), which is based on the movement of organisms in a bird flock; and gravitational search algorithm (GSA). GSA is one of the most popular metaheuristics methods. It is inspired by the law of gravity and motion and creating the appropriate balance between exploration and exploitation capabilities. In this chapter, we are going to introduce the basic GSA and review the newer versions. First, we will introduce the original GSA that is proposed for continuous problems. Then we will discuss other versions of GSA for various optimization problems and analyze the convergence properties of this algorithm. We will also review the GSA in various engineering applications.

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Binary search algorithm

Anthony Lin · WikiJournal of Science · 2019

In In computer science, binary search, also known as half-interval search, Binary search compares the target value to an element in the middle of the array. If they are not equal, the half in which the target cannot lie is eliminated and the search continues on the remaining half, again taking the middle element to compare to the target value, and repeating this until the target value is found. If the search ends with the remaining half being empty, the target is not in the array. Binary search runs in logarithmic time in the worst case, making (log ) comparisons, where is the number of elements in the array, the is 'Big O' notation, and is the logarithm. Binary search is faster than linear search except for small arrays. However, the array must be sorted first to be able to apply binary search. There are specialized data structures designed for fast searching, such as hash tables, that can be searched more efficiently than binary search. However, binary search can be used to solve a wider range of problems, such as finding the nextsmallest or next-largest element in the array relative to the target even if it is absent from the array.

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Search strategy algorithm.

Marian Showell (105767), Sam Buckman (14334231), Slavica Berber (14334234) et al. · Figshare · 2023

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MEDLINE search algorithm.

Georgios Tsivgoulis (665231), Aristeidis H. Katsanos (700374), Nikolaos Grigoriadis (261798) et al. · Figshare · 2015

<p><b>Table A</b>. List of the full-text excluded articles with the reasons for exclusion. <b>Supplemental references in S1 File. Fig. A.</b> Risk of bias summary: review authors’ judgments about each risk of bias item for each included study. <b>Fig. B.</b> Risk of bias graph: review authors’ judgments about each risk of bias item presented as percentages across all included studies. <b>Fig. C.</b> Subgroup analysis according to the imaging protocol that was used to measure the changes in brain volume. <b>Figs. D&E.</b> Funnel plots of the included studies.</p> <p>(RTF)</p>

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Literature search algorithm.

Florian Clatot (6948161), Caroline Even (10090524), Amaury Daste (8317491) et al. · Figshare · 2025

<div> Background Recurrent and metastatic squamous cell carcinoma of the head and neck (R/M SCCHN) presents substantial treatment challenges due to its aggressive and heterogeneous nature. While evidence-based guidelines provide useful recommendations, they may not offer sufficient support for real-time clinical decision-making. Objective This Delphi consensus aimed to establish expert agreement on clinical parameters guiding first- and second-line treatment decisions and the management of specific clinical scenarios in R/M SCCHN in France. Methods A two-round Delphi consensus was conducted between December 2023 and June 2024. A scientific committee of six national experts developed consensus statements based on a literature review and supervised the process. An expert panel of 25 oncologists from across France participated in both rounds. Results All respondents agreed that the need for a rapid therapeutic response and the presence of comorbidities were critical factors influencing first-line treatment choice. Seven major indicators of rapid response were identified: rapidly progressive relapse, partial airway obstruction, respiratory compromise, tumour-related bleeding, uncontrolled symptoms, carotid axis lesions, and pain resistant to third-step analgesics. For patients requiring rapid response, 76% supported using cetuximab with chemotherapy as a first-line option in cases with a combined positive score (CPS) between 1 and 19. Furthermore, 84% recommended immunotherapy (alone or combined with chemotherapy) as first-line treatment for CPS ≥ 20 tumours, while 41% would consider it for CPS 1–19 cases. A strong consensus (80%) held that first-line treatment should not restrict second-line options. Notably, 100% of respondents agreed that all patients should receive immunotherapy, cetuximab, taxanes, and platinum salts within the first two treatment lines. Conclusions This Delphi consensus offers valuable, expert-based perspectives on the management of R/M SCCHN in France. It highlights a progressive shift from standardized European guideline frameworks toward a more personalized approach that accounts for individual patient profiles and tumour characteristics. The insights generated provide practical guidance for clinical decision-making and lay the groundwork for future research and optimisation of treatment strategies in real-world settings. </div>

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FuzzyGravitational Search Algorithm

Fatemeh-sadat Saeidi-Khabisi, Esmat Rashedi, Young ResearchersSociety · 2012

Abstract—One of the noticeable topics in fuzzy logic controllers is parameter controllingof heuristic search algorithms. In this paper,one of the parameters of Gravitational Search Algorithm, GSA,is controlledusing fuzzy logic controller to achieve better optimization results and to increase convergence rate. Several experiments are performed and results are compared with the results of the original GSA. Experimental results confirm the efficiency of the proposed method. Keywords-Gravitational search algorithm;Fuzzy logic controler;Fuzzy rules; Exploration; Exploitation. I. I NTRODUCTION Inrecent years, many complicated nonlinear controllerssuch as adaptive control [1], neural control [2], and fuzzy control [3]have been proposed for controlling systems. Fuzzy logic controller (FLC) is one of the common tools used for parametercontrolling. Fuzzy theory was proposed by Zadeh in 1965 [4]. Major reasons for development of fuzzy theory can be briefly stated by two reasons. Firstly, fuzzy logic can make the difficulties of developing and analyzing complex systems easier. Secondly, fuzzy logic is capable of making human inference from not well-defined concepts/knowledge. The basic parts of a fuzzy logic controller are knowledge base, fuzzy rules, and inference engine [5]. In this paper, FLC is used to control the parameterof aheuristic search algorithm. Today, there is a great interest to control heuristic search algorithms using FLC. For example,Ref [6] proposes an improved genetic algorithm(GA) with two fuzzy controllers based on some heuristics to adaptively adjust the crossover probability and mutation rate during theoptimization process.In[7]a fuzzy control method is mixed withGA to adjust the probabilities of crossover and mutation.In [8], Fuzzy logic based controllers areutilizedfor tuning the crossover and mutationprobability of GA toimprove the algorithm performance.In [9], an FLC is used to control the values of cross over rateinGA. In[10],an adaptive fuzzy particle swarm optimization (AFPSO) algorithm utilizes fuzzy set theoryto adjust PSO acceleration coefficients adaptively.In [11], afuzzy ant colony optimization(FACO) is proposed and an FLC is used to adapt the evaporated and deposited value of pheromone trailbasedon the ant's fitness and pheromone trail age.In[12], fuzzy logic isused to combine the results of the PSO and GA inthe best possible way. Today, with enlargement issues and the importance of responding quickly, classical methods are not able to resolve many issues.Therefore,heuristic random-based search algorithms are used instead. Heuristic search algorithms are inspired fromnature orphysicalprocesses.The use of these algorithms has grown dramatically in recent years.Gravitational Search Algorithm (GSA) is proposed motivated by gravitational law and laws of motion [13]. This algorithm has high performances in solving various optimization problems and examined in various problems like filter modeling [14],web services composition[15], parameters identification of hydraulic turbine [16], solving Wessinger's Equation [17], synthesis of ring array antenna [18], classifier design [19],[20], and Multi-objective optimization [21]. By controlling the parameters of GSA, we can control the balance between the power of exploration and exploitation to get better results with less iterations of the algorithm. If there is no well-established balance between exploration and exploitation, the algorithm is not enough efficient. In this paper, fuzzy logic controller is utilized to increase convergence rate and ensure of finding optimum solution. In the next section, gravitational search algorithm is reviewed. In Section 3, the proposed FGSA is described in detail. Experimental results are in section 4and finally the paper is concluded in section 5. II. G

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