Archival Data Sets Research Articles

The Astrophysical Multimessenger Observatory Network: a Summary

The Astrophysical Multimessenger Observatory Network (AMON) aims to connect the world’s leading high-energy and multimessenger observatories. AMON’s objective are to evoke the discovery of new multimessenger phenomena, exploit these phenomena as tools for fundamental physics and astrophysics, and explore archival datasets in search of multimessenger activity. Present projects include distributing low-latency multimessenger alerts from the Neutrino-Electromagnetic (NuEM) channel, as well as distrubting alerts from individual detectors including IceCube and HAWC. Looking ahead, AMON will continue providing useful real-time analyses of a wide variety of high-energy and multimessenger data streams, including coincidences between gravitational-wave data and gamma-ray data. It will also start using the SCiMMA-standard cyberinfrastructure, and keep strengthening its ties with the theoretical and time domain astrophysics communities.

Temporal Structure in Sensorimotor Variability: A Stable Trait, But What For?

Human performance shows substantial endogenous variability over time, and this variability is a robust marker of individual differences. Of growing interest to psychologists is the realisation that variability is not fully random, but often exhibits temporal dependencies. However, their measurement and interpretation come with several controversies. Furthermore, their potential benefit for studying individual differences in healthy and clinical populations remains unclear. Here, we gather new and archival datasets featuring 11 sensorimotor and cognitive tasks across 526 participants, to examine individual differences in temporal structures. We first investigate intra-individual repeatability of the most common measures of temporal structures — to test their potential for capturing stable individual differences. Secondly, we examine inter-individual differences in these measures using: (1) task performance assessed from the same data, (2) meta-cognitive ratings of on-taskness from thought probes occasionally presented throughout the task, and (3) self-assessed attention-deficit related traits. Across all datasets, autocorrelation at lag 1 and Power Spectra Density slope showed high intra-individual repeatability across sessions and correlated with task performance. The Detrended Fluctuation Analysis slope showed the same pattern, but less reliably. The long-term component (d) of the ARFIMA(1,d,1) model showed poor repeatability and no correlation to performance. Overall, these measures failed to show external validity when correlated with either mean subjective attentional state or self-assessed traits between participants. Thus, some measures of serial dependencies may be stable individual traits, but their usefulness in capturing individual differences in other constructs typically associated with variability in performance seems limited. We conclude with comprehensive recommendations for researchers.

The ultraluminous X-ray source M 81 X-6: a weakly magnetised neutron star with a precessing accretion disc?

Context. Thanks to their proximity, ultraluminous X-ray sources (ULXs) represent a privileged astrophysical laboratory to study super-Eddington accretion. Current open questions concern the nature of the compact object, which is still hard to determine in those cases where pulsations are not directly detected, and the mechanisms responsible for the spectral changes observed in many ULXs. Aims. We investigate the nature of the ULX M 81 X-6, which has been suggested to harbour a neutron star (NS), by studying its long-term X-ray spectral and temporal evolution, with the goal of assessing the astrophysical phenomena responsible for its spectral changes. Methods. Using the rich set of available archival data from XMM-Newton, Chandra, NuSTAR, and Swift/XRT, we tracked the evolution of the source on the hardness-intensity diagram and inferred the different emitting regions of the system and their geometry, as well as the mechanisms responsible for the spectral transitions. Results. We find that the source oscillates between two main states: one characterised by a hard and luminous spectrum and the other at low hardness and luminosity. The properties of the soft component remain constant between the two states, suggesting that changes in the mass-transfer rate are not driving the spectral transitions. Instead, the bi-modal behaviour of the source and the known super-orbital period would point to the precession of the accretion disc. Here, we tested two theoretical models: (1) Lense-Thirring precession, which can explain the super-orbital period if the NS has a magnetic field B ≲ 1010 G, supporting the idea of M 81 X-6 as a weakly magnetised NS, and (2) precession due to the torque of the NS magnetic field, which leads to B ≳ 1011 G. However, the latter scenario, assuming M 81 X-6 shares similar properties with other NS-ULXs, is disfavoured because it would require magnetic field strengths (B > 1015 G) much higher than those known for other pulsating ULXs. We further show that the contribution from the hard component attributed to the putative accretion column sits just below the typical values found in pulsating ULXs, which, together with the low value of the pulsed fraction (≤10%) found for one XMM-Newton/pn observation, could explain the source’s lack of pulsations. Conclusions. The spectral properties and variability of M 81 X-6 can be accounted for if the accretor is a NS with a low magnetic field. Under the hypothesis of Lense-Thirring precession, we predict a spin period of the NS of a few seconds. We encourage future X-ray pointed observations to look for pulsations and/or spectral signatures of the magnetic field.

Music Genre Classification Using DenseNet and Data Augmentation

It can be said that the automatic classification of musical genres plays a very important role in the current digital technology world in which the creation, distribution, and enjoyment of musical works have undergone huge changes. As the number of music products increases daily and the music genres are extremely rich, storing, classifying, and searching these works manually becomes difficult, if not impossible. Automatic classification of musical genres will contribute to making this possible. The research presented in this paper proposes an appropriate deep learning model along with an effective data augmentation method to achieve high classification accuracy for music genre classification using Small Free Music Archive (FMA) data set. For Small FMA, it is more efficient to augment the data by generating an echo rather than pitch shifting. The research results show that the DenseNet121 model and data augmentation methods, such as noise addition and echo generation, have a classification accuracy of 98.97% for the Small FMA data set, while this data set lowered the sampling frequency to 16000 Hz. The classification accuracy of this study outperforms that of the majority of the previous results on the same Small FMA data set.

Changing criminal thinking: An examination of heterogeneity in treatment effects in a sample of justice-involved persons with dual diagnoses.

Recent studies have indicated variable reductions in criminal thinking for justice-involved persons with mental illness exposed to cognitive-behavioral treatments. To date, however, no studies have identified risk factors for limited response or modeled observed disparities in responsivity to interventions aimed at reducing criminal thinking. Using an archival data set of 162 probationers with a dual diagnosis who were exposed to changing lives and changing outcomes, a latent profile analysis modeled unobserved heterogeneity in treatment response per observed changes in criminal thinking. Most participants endorsed significant changes in self-reported reactive criminal thinking with minimal changes in proactive criminal thinking. Neither self-reported pretreatment severity of psychopathology nor self-reported compliance with psychotropic medication predicted response to treatment. Although diagnosis also did not predict responsiveness, more favorable views of treatment predicted greater decreases in criminal thinking. Moreover, those expressing fewer levels of criminal thinking after treatment were also found to express a decrease in attitudes supportive of violence. Limitations and treatment recommendations are discussed, including the need for correctional treatments to improve responsiveness to individual treatment needs. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Police decisions to found sexual assault cases: The influence of community-level factors and space.

Previous research has examined the influence of individual- and case-level factors on police decisions in sexual assault cases, with little attention paid to community-level factors. This study examined the association between community-level factors and police decisions to found sexual assault cases. Founding is the first decision officers make and determines whether a case is investigated. An archival data set of N = 8015 sexual assaults reported to a Midwestern city police department was used. Ordinary least squares (OLS) regression was first used to examine the relationship between six community-level factors and founding rates within the city's 77 communities. Geographically weighted regression (GWR) was then used to examine whether these relationships varied by space. OLS results revealed communities with a greater proportion of Black residents, Latinx residents, Asian residents, and higher income had significantly higher founding rates. GWR results replicated these findings indicating these relationships varied significantly by space throughout the city. In contrast to the OLS, GWR results indicated communities farther from a rape crisis center had higher founding rates. Findings underscore the importance of analyzing localized effects and examining individual-, case-, and community-level factors on sexual assault case outcomes in the criminal legal system.

Indirect effects of personality on high-intensity drinking: The role of drinking motives.

Researchers have long been interested in identifying risk factors for binge drinking behavior (4+/5+ drinks/occasion for females/males), but many studies have demonstrated that a substantial proportion of young adults are drinking at levels far beyond (often 2 to 3 times) the standard binge threshold. The consumption of such large quantities of alcohol, typically referred to as high-intensity drinking (HID), can cause severe alcohol-related problems, such as blackouts, unintended sexual experiences, and death. This study is the first to investigate whether personality is indirectly associated with the likelihood of HID via drinking motives in a large (N=999) sample of underage young adult drinkers. We hypothesized that trait neuroticism would be indirectly associated with the likelihood of HID via coping motives and that extraversion would be indirectly associated with the likelihood of HID via social and enhancement motives. To investigate these hypotheses, we used two archival data sets that recruited current underage (18- to 20-year-old) adult drinkers residing in the United States from online panel services. Participants completed self-report survey items assessing constructs of interest. To investigate the role of drinking motives in the association between personality and HID, both the direct and indirect effects were calculated via three path analyses. Findings revealed that neuroticism was partially indirectly associated with the likelihood of HID via coping motives (b= 0.02, SE=0.004, p< 0.01). In addition, extraversion was indirectly associated with the likelihood of HID via social (b= 0.031, SE=0.002, p< 0.01) and enhancement motives (b=0.01, SE=0.002, p=0.01). These findings are an initial step in examining the interplay among personality traits, drinking motives, and HID in underage drinkers and point to the need for longitudinal studies assessing these associations.

Molecular Gas Heating, Star Formation Rate Relations, and AGN Feedback in Infrared-Luminous Galaxy Mergers

We examine the origin of molecular gas heating in a sample of 42 infrared-luminous galaxies at z&lt;0.3 by combining two sets of archival data: first, integrated CO line luminosities in the 1–0 and 5–4 through 13–12 transitions; second, results from radiative transfer modelling that decompose their bolometric emission into starburst, AGN, and host galaxy components. We find that the CO 1–0 and 5–4 through 9–8 lines primarily arise via radiative heating in the starburst and the host galaxy. In contrast, the CO 10–9 through 13–12 lines may arise primarily in the starburst and AGN, with an increasing contribution from mechanical heating and shocks. For the sample as a whole, we find no evidence that AGN luminosity affects the heating of molecular gas by star formation. However, for starbursts with low initial optical depths, a more luminous AGN may reduce the efficiency of starburst heating of the CO 5–4 and above lines, consistent with negative AGN feedback.

Wisdom from the crowd: Can recommender systems predict employee turnover and its destinations?

AbstractCan algorithms that predict customer movie and shopping preferences also predict which employees are likely to leave and where they are likely to go, thus helping to retain talent? This study applies a type of machine learning (ML) technique, collaborative filtering (CF) recommender system algorithms, to investigate the comparison between satisfaction with the current job and potential satisfaction with job alternatives, which is inherent in theorizing about individual turnover decisions. The comparison of those anticipated ratings along with employee's current job satisfaction creates two operationalizations: the quantity of more desirable job alternatives and the quality (or extent of desirability) of job alternatives. To test the effectiveness of this novel approach, we applied recommender system algorithms to a longitudinal archival dataset of employees and had three main findings. First, the recommender system algorithms efficiently predicted job satisfaction based on just two sources of information (i.e., work history and job satisfaction in previous jobs), providing construct validity evidence for recommender systems. Second, both the quantity and the quality of more desirable job alternatives compared to the current job positively correlated with employees’ future turnover behavior. Finally, our CF recommender system algorithms predicted where employees moved to, and even more effectively if constraining the alternative jobs to the same occupation. We conclude with implications how recommender system algorithms can help scholars effectively test theoretical ideas and practitioners predict and reduce turnover.

Micronutrient Biosynthesis Potential of Spontaneous Grain Fermentation Microbiomes.

Fermented foods play an important role in the human diet and particularly so in under-resourced environments where cold preservation is not attainable due to irregular supply of electricity. Fermented foods are reported to support gut health by contributing probiotics. The purpose of this study was to investigate the microbial diversity and metabolic potential of spontaneous millet fermentation. The literature in the field was reviewed and analyses were conducted on publicly available Sequence Read Archive (SRA) datasets. Quality analysis was performed with FastQC, and operational taxonomic units (OTUs) were generated using Quantitative Insights Into Microbial Ecology (QIIME2) and Divisive Amplicon Denoising Algorithm (DADA2) pipelines with Greengenes as the reference database. Metagenomics and pathways analysis were performed with Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2). Statistical analysis and visualization were accomplished with Statistical Analysis of Metagenomic Profiles (STAMP). At the family taxonomic level, there were differences in the relative abundances of the dominant taxa of bacteria that are involved in the spontaneous fermentation of millet namely Lactobacillaceae, Burkholderiaceae, Streptococcaceae, Leuconostocaceae, and Acetobacteraceae. Clostridiaceae was the dominant family in one dataset. The incidence of Lactobacillaceae and Bifidobacteriaceae suggest the probiotic characteristics of fermented millet. The datasets were collected with fermentations that were mediated by autochthonous microorganisms and the presence of some potential pathogens such as Enterobacteriaceae, Clostridiaceae, Aeromonadaceae, Microbacteiaceae, Pseudomonadaceae, and Neisseriaceae which suggest the need for standardization of fermentation approaches. The genomes show the potential to synthesize metabolites such as essential amino acids and vitamins, suggesting that the respective fermented foods can be further optimized to enhance nutritional benefits.

Complexity in radiological morphology predicts worse prognosis and is associated with an increase in proteasome component levels in clear cell renal cell carcinoma.

We previously reported preoperative radiological morphology (RM) as an independent predictor for pathological upstaging after partial nephrectomy in patients with T1 renal cell carcinoma (RCC). To investigate the prognostic importance of RM in all stages and the molecular characteristics underlying the differences between each type of RM in patients with clear cell RCC (ccRCC). The Cancer Imaging Archive datasets (TCIA), comprising CT images and RNA-sequencing data, were used (n = 163). Specimens from 63 patients with ccRCC at our institution and their CT images were used. All images were divided into three types according to RMclassification. Relationships with outcome were analyzed using Cox regression analysis and log-rank test. The irregular type was a significant independent predictor of worse disease-free survival (odds ratio: 2.22, p = 0.037) compared to round and lobular types in TCIA datasets. The irregular type showed a significant increase in both mRNA and protein expression of proteasome components, PSMB1 and PSMB3. Moreover, high expression of their coding genes shortened the progression-free survival of the patients with ccRCC who received sunitinib or avelumab plus axitinib therapy. The study limitations include the qualitative classification of RM and the need for novel radiomics and texture analysis techniques. Investigating RM on pre-treatment CT scans can effectively predict worse prognosis. Increased RM complexity may indirectly predict drug sensitivity via increased expression of PSMB1 and PSMB3 in patients with ccRCC. Specific targeting of the ubiquitin-proteasome system might be a novel treatment strategy for ccRCC with increased RM complexity. The clinical and morphological characteristics of patients with ccRCC vary greatly according to cancer staging. In this study, we built upon our prior findings of the prognostic importance of RM in T1 RCC and expanded it to encompass all stages of RCC, using a series of patients from a Japanese hospital.

Through a Glass Cliff Darkly

Abstract: After breaking through the glass ceiling, women often obtain precarious or risky leadership positions in crisis-ridden organizations ( the glass cliff; Ryan &amp; Haslam, 2005 ). Due to women’s minority status, their appointment in a crisis can signal important changes to organizational stakeholders indicating the use of new strategies for overcoming the crisis (signaling theory; Spence, 1973 ). Our study examines whether the media visibility of organizations moderates these signaling effects in ways that either strengthen or weaken glass cliffs. We augmented the archival dataset used by Haslam et al. (2010 ) in which the glass cliff phenomenon was discovered by including data on the media coverage that the Financial Times Stock Exchange index 100 companies received between 2001 and 2005. Our analysis shows that glass cliffs were more pronounced in companies with low media visibility. This suggests that the media visibility of organizations can contribute to increased accountability regarding their personnel decisions in ways that expose women leaders to less discrimination.

Effects of Conversation Politeness on Hiring Decision in Online Labor Markets: An Inverted U-Shaped Relationship Exploration

This study examined the effect of politeness, as a key reflection of linguistic features of conversation in the online labor marketplace, on hiring behavior. Drawing on the politeness theory, a non-linear relationship was theorized. A hypothesis was put forward and examined against a large-scale archival dataset from a Chinese online labor market. Using an econometric model, the results demonstrated that there was an inverted U-shaped relationship between politeness and hiring decisions. The study offers theoretical implications to the online labor market literature and politeness theory by providing empirical insights on the role of politeness in hiring decision. In addition, our findings offer beneficial and practical contributions for vendors and platform operators.

Tried and True? A Psychometric Evaluation of the Psychological Inventory of Criminal Thinking Styles-Short Form.

The Psychological Inventory of Criminal Thinking Styles-Short Form (PICTS-SF) is an abbreviated 35-item version of the PICTS, a measure of cognitions that support a criminal lifestyle. Despite use in research and clinical work, the PICTS-SF's psychometric properties have not been tested. Using two archival datasets, we analyzed the PICTS-SF's reliability and structural validity in multiply imputed data from adult males and females on probation in a residential treatment facility (n = 514). We also tested the PICTS-SF's reliability and discriminant and postdictive validities among adult males in administrative segregation in prison (n = 95). We found evidence for the PICTS-SF's internal consistency (α and ω ≥ .89), structural validity (CFI = .90, RMSEA = .05), discriminant validity (.22 ≤ r ≤ .39), and postdictive validity for receiving disciplinary infractions (incident rate ratio = 1.04). These results support the PICTS-SF's use in research, and qualified use in clinical applications.

Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation.

Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, a novel self-supervised shallow learning network model exploiting the sophisticated three-level qutrit-inspired quantum information system, referred to as quantum fully self-supervised neural network (QFS-Net), is presented for automated segmentation of brain magnetic resonance (MR) images. The QFS-Net model comprises a trinity of a layered structure of qutrits interconnected through parametric Hadamard gates using an eight-connected second-order neighborhood-based topology. The nonlinear transformation of the qutrit states allows the underlying quantum neural network model to encode the quantum states, thereby enabling a faster self-organized counterpropagation of these states between the layers without supervision. The suggested QFS-Net model is tailored and extensively validated on the Cancer Imaging Archive (TCIA) dataset collected from the Nature repository. The experimental results are also compared with state-of-the-art supervised (U-Net and URes-Net architectures) and the self-supervised QIS-Net model and its classical counterpart. Results shed promising segmented outcomes in detecting tumors in terms of dice similarity and accuracy with minimum human intervention and computational resources. The proposed QFS-Net is also investigated on natural gray-scale images from the Berkeley segmentation dataset and yields promising outcomes in segmentation, thereby demonstrating the robustness of the QFS-Net model.

Hubble Asteroid Hunter

Context. The Hubble Space Telescope (HST) archives constitute a rich dataset of high-resolution images to mine for strong gravitational lenses. While many HST programmes specifically target strong lenses, they can also be present by coincidence in other HST observations. Aims. Our aim is to identify non-targeted strong gravitational lenses, without any prior selection on the lens properties, in almost two decades of images from the ESA HST archive (eHST). Methods. We used crowdsourcing on the Hubble Asteroid Hunter (HAH) citizen science project to identify strong lenses, along with asteroid trails, in publicly available large field-of-view HST images. We visually inspected 2354 objects tagged by citizen scientists as strong lenses to clean the sample and identify the genuine lenses. Results. We report the detection of 252 strong gravitational lens candidates, which were not the primary targets of the HST observations. A total of 198 of them are new, not previously reported by other studies, consisting of 45 A grades, 74 B grades and 79 C grades. The majority are galaxy-galaxy configurations. The newly detected lenses are, on average, 1.3 magnitudes fainter than previous HST searches. This sample of strong lenses with high-resolution HST imaging is ideal to follow up with spectroscopy for lens modelling and scientific analyses. Conclusions. This paper presents the unbiased search of lenses that enabled us to find a wide variety of lens configurations, including exotic lenses. We demonstrate the power of crowdsourcing in visually identifying strong lenses and the benefits of exploring large archival datasets. This study shows the potential of using crowdsourcing in combination with artificial intelligence for the detection and validation of strong lenses in future large-scale surveys such as ESA’s Euclid mission or in James Webb Space Telescope (JWST) archival images.

Oral contraceptives in the central nervous system: Basic pharmacology, methodological considerations, and current state of the field

Millions of women around the world use combined oral contraceptives (OCs), yet surprisingly little is known about their central nervous system (CNS) effects. This article provides a short overview of the basic pharmacology of OCs, emphasizing features that may be relevant to understanding their effects in the CNS. Historical and recent findings from studies of cognitive function, mood, and negative affect (depressive changes under OC use) are then reviewed. We also present data from an archival dataset from our own laboratory in which we explore dysphoric changes in women using four generations of contraceptive progestins. Current data in the field are consistent with a modest effect of OC use on CNS variables, but conclusions based on current findings must be made very cautiously because of multiple methodological issues in many published studies to date, and inconsistencies in the findings. Directions for future research over the next 10 years are suggested. (150 words)

Automated lung tumor delineation on positron emission tomography/computed tomography via a hybrid regional network.

Multimodality positron emission tomography/computed tomography (PET/CT) imaging combines the anatomical information of CT with the functional information of PET. In the diagnosis and treatment of many cancers, such as non-small cell lung cancer (NSCLC), PET/CT imaging allows more accurate delineation of tumor or involved lymph nodes for radiation planning. In this paper, we propose a hybrid regional network method of automatically segmenting lung tumors from PET/CT images. The hybrid regional network architecture synthesizes the functional and anatomical information from the two image modalities, whereas the mask regional convolutional neural network (R-CNN) and scoring fine-tune the regional location and quality of the output segmentation. This model consists of five major subnetworks, that is, a dual feature representation network (DFRN), a regional proposal network (RPN), a specific tumor-wise R-CNN, a mask-Net, and a score head. Given a PET/CT image as inputs, the DFRN extracts feature maps from the PET and CT images. Then, the RPN and R-CNN work together to localize lung tumors and reduce the image size and feature map size by removing irrelevant regions. The mask-Net is used to segment tumor within a volume-of-interest (VOI) with a score head evaluating the segmentation performed by the mask-Net. Finally, the segmented tumor within the VOI was mapped back to the volumetric coordinate system based on the location information derived via the RPN and R-CNN. We trained, validated, and tested the proposed neural network using 100 PET/CT images of patients with NSCLC. A fivefold cross-validation study was performed. The segmentation was evaluated with two indicators: (1) multiple metrics, including the Dice similarity coefficient, Jacard, 95th percentile Hausdorff distance, mean surface distance (MSD), residual mean square distance, and center-of-mass distance; (2) Bland-Altman analysis and volumetric Pearson correlation analysis. In fivefold cross-validation, this method achieved Dice and MSD of 0.84±0.15 and 1.38±2.2mm, respectively. A new PET/CT can be segmented in 1s by this model. External validation on The Cancer Imaging Archive dataset (63 PET/CT images) indicates that the proposed model has superior performance compared to other methods. The proposed method shows great promise to automatically delineate NSCLC tumors on PET/CT images, thereby allowing for a more streamlined clinical workflow that is faster and reduces physician effort.

Compensation structure impact on executive value judgment shift resulting in occurrence of fraud

PurposeThe purpose of this study is to examine whether executive compensation structure is a predictor of a value judgment shift facilitating fraud. The Raval (2018) disposition-based fraud model theorizes that in a fraud, a judgment shift occurs that results in an intentional action. Judgment shifts are influenced by intertemporal rewards, an executive compensation structure comprising salary (immediate reward) and delayed compensation in performance-based incentives.Design/methodology/approachUsing an archival data set consisting of frauds identified through Securities and Exchange Commission Accounting and Auditing Enforcement Releases, the compensation structure of executives involved in frauds is compared against the compensation structure of executives in a peer control group.FindingsThere was a significant difference in the intertemporal rewards of the compensation structures between the two groups, indicating that compensation structure presents intertemporal choices leading to a judgment shift that influences the deliberate action of fraud.Research limitations/implicationsThis study represents the first empirical test of the disposition-based fraud model using intertemporal rewards leading to judgment shift.Practical implicationsExecutive compensation structure should reduce intertemporal rewards for executives reducing judgment shifts that can result in risk of fraud.Originality/valueThis study addresses how executive compensation structure can result in fraud.

Improving lung cancer detection using faster region‐based convolutional neural network aided with fuzzy butterfly optimization algorithm

SummaryLung cancer is the most deadly type of cancer and it is caused by genetic variations in lung tissues. Other causes of lung cancer are alcohol, smoking, and threatening gas exposures. The diagnosis of lung cancer is an intricate task and early detection of lung cancer can help to get the exact treatment in advance. The application of a computer‐aided diagnosis process helps to predict lung cancer earlier, nonetheless, it does not provide better accuracy. The overfitting nature of features and dimensionality of lung cancer can prevent it from obtaining maximum accuracy. Hence, we proposed a novel faster region convolutional neural network (RCNN) based fuzzy butterfly optimization algorithm (FBOA) to achieve better prediction accuracy and effectiveness. The proposed Faster RCNN can provide better positioning of lung cancer swiftly and effectively and the FBOA approach can be used to perform two‐stage classification. The fuzzy rules used in the FBOA can be utilized to find the severity of the lung cancer and differentiate the Benign stage and Malignant stage effectively. The experimental analyses are performed in MATLAB simulation. The preprocessing of images is performed by different tools from MATLAB and is to format the images as required. The cancer imaging archive (TCIA) dataset is utilized to analyze the performance of the proposed method and compared with various state‐of‐art works. The performance of the proposed method is evaluated by using different evaluation metrics such as precision, recall, F‐measure, and accuracy and attained 99, 98, 99, and 97% respectively. Thus, our proposed method outperforms all the other approaches.