Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Coronavirus herd immunity optimizer to solve classification problems.

ABSTRACT: Classification is a technique in data mining that is used to predict the value of a categorical variable and to produce input data and datasets of varying values. The classification algorithm makes use of the training datasets to build a model which can be used for allocating unclassified records to a defined class. In this paper, the coronavirus herd immunity optimizer (CHIO) algorithm is used to boost the efficiency of the probabilistic neural network (PNN) when solving classification problems. First, the PNN produces a random initial solution and submits it to the CHIO, which then attempts to refine the PNN weights. This is accomplished by the management of random phases and the effective identification of a search space that can probably decide the optimal value. The proposed CHIO-PNN approach was applied to 11 benchmark datasets to assess its classification accuracy, and its results were compared with those of the PNN and three methods in the literature, the firefly algorithm, African buffalo algorithm, and β-hill climbing. The results showed that the CHIO-PNN achieved an overall classification rate of 90.3% on all datasets, at a faster convergence speed as compared outperforming all the methods in the literature.

Supplementary information

The online version contains supplementary material available at 10.1007/s00500-022-06917-z.

SUBMITTER: Alweshah M

PROVIDER: S-EPMC8922087 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

How chimpanzees solve collective action problems.

Project description:We presented small groups of chimpanzees with two collective action situations, in which action was necessary for reward but there was a disincentive for individuals to act owing to the possibility of free-riding on the efforts of others. We found that in simpler scenarios (experiment 1) in which group size was small, there was a positive relationship between rank and action with more dominant individuals volunteering to act more often, particularly when the reward was less dispersed. Social tolerance also seemed to mediate action whereby higher tolerance levels within a group resulted in individuals of lower ranks sometimes acting and appropriating more of the reward. In more complex scenarios, when group size was larger and cooperation was necessary (experiment 2), overcoming the problem was more challenging. There was highly significant variability in the action rates of different individuals as well as between dyads, suggesting success was more greatly influenced by the individual personalities and personal relationships present in the group.

| S-EPMC3497239 | biostudies-literature

Herd Immunity against Severe Acute Respiratory Syndrome Coronavirus 2 Infection in 10 Communities, Qatar.

Project description:We investigated what proportion of the population acquired severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whether the herd immunity threshold has been reached in 10 communities in Qatar. The study included 4,970 participants during June 21-September 9, 2020. Antibodies against SARS-CoV-2 were detected by using an electrochemiluminescence immunoassay. Seropositivity ranged from 54.9% (95% CI 50.2%-59.4%) to 83.8% (95% CI 79.1%-87.7%) across communities and showed a pooled mean of 66.1% (95% CI 61.5%-70.6%). A range of other epidemiologic measures indicated that active infection is rare, with limited if any sustainable infection transmission for clusters to occur. Only 5 infections were ever severe and 1 was critical in these young communities; infection severity rate of 0.2% (95% CI 0.1%-0.4%). Specific communities in Qatar have or nearly reached herd immunity for SARS-CoV-2 infection: 65%-70% of the population has been infected.

| S-EPMC8084480 | biostudies-literature

Neural activity when people solve verbal problems with insight.

Project description:People sometimes solve problems with a unique process called insight, accompanied by an "Aha!" experience. It has long been unclear whether different cognitive and neural processes lead to insight versus noninsight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and noninsight solutions. Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight. We observed two objective neural correlates of insight. Functional magnetic resonance imaging (Experiment 1) revealed increased activity in the right hemisphere anterior superior temporal gyrus for insight relative to noninsight solutions. The same region was active during initial solving efforts. Scalp electroencephalogram recordings (Experiment 2) revealed a sudden burst of high-frequency (gamma-band) neural activity in the same area beginning 0.3 s prior to insight solutions. This right anterior temporal area is associated with making connections across distantly related information during comprehension. Although all problem solving relies on a largely shared cortical network, the sudden flash of insight occurs when solvers engage distinct neural and cognitive processes that allow them to see connections that previously eluded them.

| S-EPMC387268 | biostudies-other

An integrated cuckoo search optimizer for single and multi-objective optimization problems.

Project description:Integrating heterogeneous biological-inspired strategies and mechanisms into one algorithm can avoid the shortcomings of single algorithm. This article proposes an integrated cuckoo search optimizer (ICSO) for single objective optimization problems, which incorporates the multiple strategies into the cuckoo search (CS) algorithm. The paper also considers the proposal of multi-objective versions of ICSO called MOICSO. The two algorithms presented in this paper are benchmarked by a set of benchmark functions. The comprehensive analysis of the experimental results based on the considered test problems and comparisons with other recent methods illustrate the effectiveness of the proposed integrated mechanism of different search strategies and demonstrate the performance superiority of the proposed algorithm.

| S-EPMC7959647 | biostudies-literature

Use of electron paramagnetic resonance to solve biochemical problems.

Project description:Electron paramagnetic resonance (EPR) spectroscopy is a very powerful biophysical tool that can provide valuable structural and dynamic information about a wide variety of biological systems. The intent of this review is to provide a general overview for biochemists and biological researchers of the most commonly used EPR methods and how these techniques can be used to answer important biological questions. The topics discussed could easily fill one or more textbooks; thus, we present a brief background on several important biological EPR techniques and an overview of several interesting studies that have successfully used EPR to solve pertinent biological problems. The review consists of the following sections: an introduction to EPR techniques, spin-labeling methods, and studies of naturally occurring organic radicals and EPR active transition metal systems that are presented as a series of case studies in which EPR spectroscopy has been used to greatly further our understanding of several important biological systems.

| S-EPMC3839053 | biostudies-literature

An integrated coupled oscillator network to solve optimization problems

Project description: Not available

| S-EPMC11343741 | biostudies-literature

The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems.

Project description:Motivated by the hunting strategies of cheetahs, this paper proposes a nature-inspired algorithm called the cheetah optimizer (CO). Cheetahs generally utilize three main strategies for hunting prey, i.e., searching, sitting-and-waiting, and attacking. These strategies are adopted in this work. Additionally, the leave the pray and go back home strategy is also incorporated in the hunting process to improve the proposed framework's population diversification, convergence performance, and robustness. We perform intensive testing over 14 shifted-rotated CEC-2005 benchmark functions to evaluate the performance of the proposed CO in comparison to state-of-the-art algorithms. Moreover, to test the power of the proposed CO algorithm over large-scale optimization problems, the CEC2010 and the CEC2013 benchmarks are considered. The proposed algorithm is also tested in solving one of the well-known and complex engineering problems, i.e., the economic load dispatch problem. For all considered problems, the results are shown to outperform those obtained using other conventional and improved algorithms. The simulation results demonstrate that the CO algorithm can successfully solve large-scale and challenging optimization problems and offers a significant advantage over different standards and improved and hybrid existing algorithms. Note that the source code of the CO algorithm is publicly available at https://www.optim-app.com/projects/co .

| S-EPMC9243145 | biostudies-literature

Human opinion dynamics: an inspiration to solve complex optimization problems.

Project description:Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making.

| S-EPMC3801113 | biostudies-literature

New Caledonian Crows Use Mental Representations to Solve Metatool Problems.

Project description:One of the mysteries of animal problem-solving is the extent to which animals mentally represent problems in their minds. Humans can imagine both the solution to a problem and the stages along the way [1-3], such as when we plan one or two moves ahead in chess. The extent to which other animals can do the same is far less clear [4-25]. Here, we presented New Caledonian crows with a series of metatool problems where each stage was out of sight of the others and the crows had to avoid either a distractor apparatus containing a non-functional tool or a non-functional apparatus containing a functional tool. Crows were able to mentally represent the sub-goals and goals of metatool problems: crows kept in mind the location and identities of out-of-sight tools and apparatuses while planning and performing a sequence of tool behaviors. This provides the first conclusive evidence that birds can plan several moves ahead while using tools.

| S-EPMC6384166 | biostudies-literature

Combining FMEA with DEMATEL models to solve production process problems.

Project description:Failure mode and effects analysis (FMEA) is an analysis tool for identifying and preventing flaws or defects in products during the design and process planning stage, preventing the repeated occurrence of problems, reducing the effects of these problems, enhancing product quality and reliability, saving costs, and improving competitiveness. However, FMEA can only analyze one influence factor according to its priority, rendering this method ineffective for systems containing multiple FMs whose effects are simultaneous or interact with one another. Accordingly, when FMEA fails to identify the influence factors and the factors being influenced, the most crucial problems may be placed in lower priority or remain unresolved. Decision-Making Trial and Evaluation Laboratory (DEMATEL) facilitates the determination of cause and effect factors; by identifying the causal factors that should be prioritized, prompt and effective solutions to core problems can be derived, thereby enhancing performance. Using the photovoltaic cell manufacturing industry in China as the research target, the present study combined FMEA with DEMATEL to amend the flaws of FMEA and enhance its effectiveness. First, FMEA was used to identify items requiring improvement. Then, DEMATEL was employed to examine the interactive effects and causal relationships of these items. Finally, the solutions to the problems were prioritized. The proposed method effectively combined the advantages of FMEA and DEMATEL to facilitate the identification of core problems and prioritization of solutions in the Chinese photovoltaic cell industry.

| S-EPMC5570359 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data