Classification Scheme Research Articles

SSO-CCNN: A Correlation-Based Optimized Deep CNN for Brain Tumor Classification Using Sampled PGGAN

Recently, new advancements in technologies have promoted the classification of brain tumors at the early stages to reduce mortality and disease severity. Hence, there is a need for an automatic classification model to automatically segment and classify the tumor regions, which supports researchers and medical practitioners without the need for any expert knowledge. Thus, this research proposes a novel framework called the scatter sharp optimization-based correlation-driven deep CNN model (SSO-CCNN) for classifying brain tumors. The implication of this research is based on the growth of the optimized correlation-enabled deep model, which classifies the tumors using the optimized segments acquired through the developed sampled progressively growing generative adversarial networks (sampled PGGANs). The hyperparameter training is initiated through the designed SSO optimization that is developed by combining the features of the global and local searching phase of flower pollination optimization as well as the adaptive automatic solution convergence of sunflower optimization for precise consequences. The recorded accuracy, sensitivity, and specificity of the SSO-CCNN classification scheme are 97.41%, 97.89%, and 96.93%, respectively, using the brain tumor dataset. In addition, the execution latency was found to be 1.6 s. Thus, the proposed framework can be beneficial to medical experts in tracking and assessing symptoms of brain tumors reliably.

Brief Review for Multi-Class Brain Tumor Diseases Schemes Using Machine Learning Techniques

Brain tumor diseases have had a considerable impact worldwide, affecting millions of individuals of different age groups, including both children and adults above 20 years old. Due to they are more needed in people’s lives, using the method based classifying brain tumors by machine learning schemes has become necessary. However, healthcare applications face challenges in identifying the most suitable classification-based metric, such as accuracy, due to the utilization of recent datasets. This study paper aims to provide a thorough evaluation of computational intelligence strategies used in tumor diagnosis. Several successful data mining techniques have been implemented, including wavelet analysis and spatial pixel modulation techniques. Furthermore, feature extraction and reduction techniques, such as the Grey Level Co-occurrence Matrix (GLCM), have been used to prepare the features for classification. Magnetic resonance imaging scan (MRI) is frequently utilized for the diagnosis of brain tumor diseases which is highly applied for classification-based machine learning, The review paper was focused on gliomas, meningiomas, and pituitary adenoma diseases. Technically, the usage of kernel principal component KPCA analysis with the proposed adaptive back propagation neural network scheme produced better performance-based classification metrics, (i.e:99.84%) for the accuracy metric. The aforementioned review articles have demonstrated that usage of the machine learning-based health care applications (brain diseases) classification widely assists the patient’s outcome and operations inside the hospitals. In summary, the paper has highlighted the importance of machine learning schemes for brain tumor detection and classification, and it also provided a comprehensive analysis and comparison of the state-of-the-art to show the methods such as ;(feature extraction, feature reduction), pros, cons, and the contributions for each of them. The paper's results are considered an advantageous starting point for future works.

Classification methods for hyperspectral remote sensing images with weak texture features

In order to provide method support for remote sensing image classification of weak texture region and improve the classification effect, a remote sensing image classification model for weak texture region was constructed. Firstly, the Gray Level Co-occurrence Matrix (GLCM) was used to extract texture features, then the correlation between texture features was reduced by dimensionality reduction method, and finally the spectral features were fused to classify remote sensing images. In the experimental part, neural network classifier was used to classify images based on texture feature, spectral feature and spectral features combined with texture features, and the results ware compared with object-oriented classification method. The analysis of texture extraction in the study area shows that the texture feature image obtained in the 5 × 5 window has higher resolution than that in the 7 × 7 window, and more robust texture features can be obtained in the range of 4∼8 pixel pairs. When using neural network classifier for classification, compared with images based on spectral features and texture features, the overall classification accuracy (OA) of the proposed classification model is increased by 16.72% and 11.16%, respectively, and the Kappa coefficient is increased by 0.2824 and 0.1943, respectively. Compared with the object-oriented classification method, the overall classification accuracy of the proposed classification scheme is increased by 1.15%, and the Kappa coefficient is increased by 0.0191. The constructed model has good classification effect and high classification accuracy for remote sensing images of weak texture region, and can provide scientific reference for remote sensing image classification of weak texture region.

Molecular taxonomy of myelodysplastic syndromes and its clinical implications

AbstractMyelodysplastic syndromes (MDS) are clonal hematologic disorders characterized by morphologic abnormalities of myeloid cells and peripheral cytopenias. Although genetic abnormalities underlie the pathogenesis of these disorders and their heterogeneity, current classifications of MDS rely predominantly on morphology. We performed genomic profiling of 3233 patients with MDS or related disorders to delineate molecular subtypes and define their clinical implications. Gene mutations, copy-number alterations, and copy-neutral loss of heterozygosity were derived from targeted sequencing of a 152-gene panel, with abnormalities identified in 91%, 43%, and 11% of patients, respectively. We characterized 16 molecular groups, encompassing 86% of patients, using information from 21 genes, 6 cytogenetic events, and loss of heterozygosity at the TP53 and TET2 loci. Two residual groups defined by negative findings (molecularly not otherwise specified, absence of recurrent drivers) comprised 14% of patients. The groups varied in size from 0.5% to 14% of patients and were associated with distinct clinical phenotypes and outcomes. The median bone marrow (BM) blast percentage across groups ranged from 1.5% to 10%, and the median overall survival ranged from 0.9 to 8.2 years. We validated 5 well-characterized entities, added further evidence to support 3 previously reported subsets, and described 8 novel groups. The prognostic influence of BM blasts depended on the genetic subtypes. Within genetic subgroups, therapy-related MDS and myelodysplastic/myeloproliferative neoplasms had comparable clinical and outcome profiles to primary MDS. In conclusion, genetically-derived subgroups of MDS are clinically relevant and might inform future classification schemas and translational therapeutic research.

The emergence of discrimination due to miscategorization

PurposeThis study explores the emergence of discrimination based on observable characteristics. In many instances, agents presume differences arising from traits such as race or gender, even when these parameters are irrelevant to the situation at hand. This paper intends to reveal an emergent behavior and a persistent culture of discrimination caused by miscategorization in strategic interactions.Design/methodology/approachWe assume that agents occasionally engage in conflicts modeled as asymmetric hawk and dove games, where boundedly rational agents may categorize their opponents based on observable traits to make effective decisions. Three categorization strategies are considered: fine-grained, regular, and coarse-grained. Subsequently, an evolutionary agent-based model is employed to examine the performance of these strategies in a dynamic environment.FindingsThe results demonstrate that fine-grained categorization provides an advantage when the cost of fighting is low, while coarse-grained categorizers exhibit more peaceful behavior, gaining an advantage when the cost of conflict is high. Our primary finding indicates the emergence of discrimination based on non-relevant traits, manifested through consistent aggressive behavior towards individuals possessing these traits.Originality/valueThis paper is the first to investigate the emergence of discrimination without assuming prior differences between groups. Previous studies have assumed either an initial population difference or a homophily-based approach. In contrast, we demonstrate that discrimination can emerge even in the absence of such assumptions. Discrimination between two groups may arise as long as there are agents who label these categories.

Uncertainties Assessment of Regional Aerosol Classification Schemes in South America

Uncertainties Assessment of Regional Aerosol Classification Schemes in South America

AsMODiTS: An application of surrogate models to optimize Time Series symbolic discretization through archive-based training set update strategy

The enhanced multi-objective symbolic discretization for time series (eMODiTS) uses an evolutionary process to identify the appropriate discretization scheme in the Time Series Classification (TSC) task. It discretizes using a unique alphabet cut for each word segment. However, this kind of scheme has a higher computational cost. Therefore, this study implemented surrogate models to minimize this cost. The general procedure is summarized below.•The K-nearest neighbor for regression, the support vector regression model, and the Ra- dial Basis Functions neural networks were implemented as surrogate models to estimate the objective values of eMODiTS, including the discretization process.•An archive-based update strategy was introduced to maintain diversity in the training set.•Finally, the model update process uses a hybrid (fixed and dynamic) approach for the surrogate model's evolution control.

Classification of classical spin liquids: Typology and resulting landscape

Classical spin liquids (CSL) lack long-range magnetic order and are characterized by an extensive ground-state degeneracy. We propose a classification scheme of CSLs based on the structure of the flat bands of their Hamiltonians. Depending on absence or presence of the gap from the flat band, the CSL are classified as algebraic or fragile topological, respectively. Each category is further classified: the algebraic case by the nature of the emergent Gauss's law at the gap-closing point(s), and the fragile topological case by the homotopy of the eigenvector winding around the Brillouin zone. Previously identified models of CSLs fit snugly into our scheme, on a landscape where algebraic CSLs are located at transitions between fragile topological ones. It also allows us to present new families of models illustrating this landscape, which hosts both fragile topological and algebraic CSLs, as well as transitions between them. Published by the American Physical Society 2024

Does synthetic data augmentation improve the performances of machine learning classifiers for identifying health problems in patient-nurse verbal communications in home healthcare settings?

Identifying health problems in audio-recorded patient-nurse communication is important to improve outcomes in home healthcare patients who have complex conditions with increased risks of hospital utilization. Training machine learning classifiers for identifying problems requires resource-intensive human annotation. To generate synthetic patient-nurse communication and to automatically annotate for common health problems encountered in home healthcare settings using GPT-4. We also examined whether augmenting real-world patient-nurse communication with synthetic data can improve the performance of machine learning to identify health problems. Secondary data analysis of patient-nurse verbal communication data in home healthcare settings. The data were collected from one of the largest home healthcare organizations in the United States. We used 23 audio recordings of patient-nurse communications from 15 patients. The audio recordings were transcribed verbatim and manually annotated for health problems (e.g., circulation, skin, pain) indicated in the Omaha System Classification scheme. Synthetic data of patient-nurse communication were generated using the in-context learning prompting method, enhanced by chain-of-thought prompting to improve the automatic annotation performance. Machine learning classifiers were applied to three training datasets: real-world communication, synthetic communication, and real-world communication augmented by synthetic communication. Average F1 scores improved from 0.62 to 0.63 after training data were augmented with synthetic communication. The largest increase was observed using the XGBoost classifier where F1 scores improved from 0.61 to 0.64 (about 5% improvement). When trained solely on either real-world communication or synthetic communication, the classifiers showed comparable F1 scores of 0.62-0.61, respectively. Integrating synthetic data improves machine learning classifiers' ability to identify health problems in home healthcare, with performance comparable to training on real-world data alone, highlighting the potential of synthetic data in healthcare analytics. This study demonstrates the clinical relevance of leveraging synthetic patient-nurse communication data to enhance machine learning classifier performances to identify health problems in home healthcare settings, which will contribute to more accurate and efficient problem identification and detection of home healthcare patients with complex health conditions.

Systematising the LCA approaches’ soup: a framework based on text mining

PurposeIt is challenging for practitioners to navigate through the multitude of life cycle assessment (LCA) approaches due to the rich literature and a lack of systematisation. The LCA flexibility allowed by standards results in a multitude of applications and, as referred to in previous works, as an “alphabet soup”. This paper proposes a scheme for a clearer classification of currently used LCA approaches, with consideration of the 4-stage framework coming from standards.MethodsThis systematisation was first established through literature research serving as a preliminary tentative framework. A text mining task was carried out in a second stage, involving 2044 published articles among 7558 of the last 10 years. For text mining, a dictionary collected keywords and synonyms of the LCA approaches. Such keywords were then extracted from the text together with their context (multiword). The final multiword analysis allowed the association of each keyword (i.e. each LCA approach) with a specific LCA stage (Goal and Scope, Life Cycle Inventory, Life Cycle Impact Assessment, Interpretation). The preliminary framework was adapted, further enriched and validated based on the text mining results.ResultsAs a result of the text mining activities, the preliminary tentative framework was partially confirmed and enriched with new insights, especially in the field of “explorative” LCA approaches, which also include “prospective” and “scenario-based” LCA. For most of the currently used LCA approaches, a link to a unique LCA stage was not recorded. However, clear trends were detected. The text mining task also highlighted a high number of works in which different approaches are compared or counterposed, especially in the field of attributional and consequential LCA. Some issues were found with the connotations of “traditional” approaches, which could be defined more specifically as “non-explorative”.ConclusionsUnlike other works focused on notions from selected literature, text mining activities can provide bottom-up feedback on a larger scale more automatically. In addition, this work brought out novel LCA approaches, for which future developments will confirm a final definition and systematisation. As an additional advantage, the presented methodology is easily replicable. Hence, the presented framework can be updated along with developments in LCA approaches.

HERisk and statistical clustering integrated for health risk modelling of PTEs in natural water resources for drinking and sanitary uses

Potentially toxic elements (PTEs) are well-known for exposing living organisms and humans to different levels of risk. The present study aimed to evaluate the extent of exposure to health risks sustained by the inhabitants of different suburbs across Southeastern Nigeria as a result of contaminated water sources. There are existing literatures on human health risk assessment in the study region. However, this is the first study to report a detailed breakdown of the risks faced by nine age groups in the study area. This was achieved by integrating a novel code (HERisk), statistical clustering, and water quality data. Laboratory analysis showed that the levels of PTEs (Fe2+, Ni2+, Cr3+, and Pb2+) in 53.6%, 17.8%, 3.5%, and 46% of the samples were found to be above the recommended limits. Aggregated human health risk scores for ingestion and dermal routes revealed that children aged 1 to 2 years are the most vulnerable to health hazards. According to cumulative carcinogenic health risk values (0.00E+00 to 2.41E-04), samples proximal to regions with significant human activities expose the locals to a high risk of developing cancer via ingestion. Q-mode hierarchical cluster analysis successfully validated the classification schemes used for the interpretation of the HERisk code values, captured the patterns in the dataset, and brought forth new perspectives. The comprehensive approach detailed in this study can be adopted as a framework for ongoing monitoring and assessment of water quality.

Drug-related problems in elective surgical inpatients: A retrospective study

Objectives: Patients with drug-related problems are at high risk for perioperative complications. The study aimed to determine the prevalence, number, characteristics, clinical significance and the involved drugs of drug-related problems in inpatients, who were admitted to elective surgery, as well as their burden of comorbidity. Methods: The study design was a retrospective, observational study across nine different surgical sites. Patients at admission for elective surgery with ≥ 1 drug-related problem, a hospital stay of ≥ 24 h and at age ≥ 18 years were included. The outcomes of interest were the prevalence and nature of drug-related problems, assessed by pharmacists at hospital admission. The Pharmaceutical Network Europe classification V9.1, the Hatoum scale of clinical significance, the Anatomical-Therapeutic-Chemical classification scheme of the World Health Organization were engaged to categorize drug-related problems and their clinical significance. The Charlson Comorbidity Index was applied to assess the comorbidity of participants. Results: The final data set included 11,176 elective surgical inpatients. Of these, a sample of 284 (2.54%) patients was analysed. It was found that 9.89% of the patients showed at least one drug-related problem (average 1.43, SD 0.7). Major causes were drug-drug interactions (30.3%) and supra-therapeutic doses (18.0%). Most drug-related problems were referred to a prescriber for intervention (61.3%). Eighty-two percent of drug-related problems were rated as clinically significant. Cardiovascular drugs were of major concern. Participants’ most common comorbidities were tumour diagnosis (34%), diabetes mellitus with end organ damage (26%) and peripheral vascular diseases (19%). Conclusions: Although the prevalence of drug-related problems in this diverse study population was low, drug-related problems were of great importance in terms of their cause and clinical significance. Patients with drug-related problems showed a moderate burden of physiological illness. Study results suggest a need to identify exposed patients with drug-related problems.

Spatiotemporal Analysis of Urban Forest in Chattanooga, Tennessee from 1984 to 2021 Using Landsat Satellite Imagery

Chattanooga, Tennessee is one of many cities in the Southeastern United States that is experiencing rapid urban growth. As these metropolitan areas continue to grow larger, more and more of Earth’s unique temperate forest, an ecosystem of enormous cultural, ecological, and recreational significance in the Southeastern United States, is destroyed to make way for new urban development. This research takes advantage of the extensive temporal archive of multispectral satellite imagery provided by the Landsat program to conduct a 37-year analysis of urban forest canopy cover across the City of Chattanooga. A time series of seven Landsat 5 scenes and three Landsat 8 scenes were acquired between 1984 and 2021 at an interval of five years or less. Each multispectral image was processed digitally and classified into a four-class thematic raster using a supervised hybrid classification scheme with a support vector machine (SVM) algorithm. The obtained results showed a loss of up to 43% of urban forest canopy and a gain of up to 134% urban land area in the city. Analyzing the multidecade spatiotemporal forest canopy in a rapidly expanding metropolitan center, such as Chattanooga, could help direct sustainable development efforts towards areas urbanizing at an above-average rate.

Classifying UK charities’ activities by charitable cause: a new classification system

This article presents a new system for classifying UK charities’ activities according to their charitable purposes. It also outlines our attempts to use keyword search rules to apply these classifications to the various UK charity registers. The classification results and code, which are made freely available online, help to address the limitations of existing classification schemes in the UK context. Depending on the scheme, these include a lack of detail and coverage of important subsectors, a lack of systematic data collection and limits on the number of classifications per charity. We discuss the pros and cons of different approaches and show that the keyword searching method provides a sufficiently accurate and transparent approach. We also present some preliminary results on how commonly each ‘tag’ is matched against UK charities, as well as exploring how the results compare to existing classifications in the register of charities for England and Wales.

RadioGraphics Update: 2023 FIGO Staging System for Endometrial Cancer.

Editor's Note.-RadioGraphics Update articles supplement or update information found in full-length articles previously published in RadioGraphics. These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.

Solar irradiance component separation benchmarking: The critical role of dynamically-constrained sky conditions

The decomposition of global horizontal irradiance into its direct and diffuse components is critical in many applications. To guarantee accurate results in practice, the existing separation techniques need to be validated against reference ground measurements from a variety of stations. Here, four versions of the recent GISPLIT model are compared to a strong benchmark constituted from nine leading models of the literature. The validation database includes ≈24 million data points and is constituted of one calendar year of 1-min high-quality data from 118 research-class world stations covering all continents and all five major Köppen-Geiger climates. The results are analyzed with various statistical metrics to be as generalizable and explicative as possible. It is found that even the simpler GISPLIT version reduces the mean site RMSE of the best benchmark model by ≈11 % for the direct component and ≈17 % for the diffuse component. The improvement reaches ≈17 % and ≈25 %, respectively, when using the best GISPLIT version. The improvements are more important in cases of highly variable sky cloudiness, per the CAELUS sky classification scheme. A ranking analysis shows that all four versions of GISPLIT ranked higher than the benchmark models, and that the use of machine learning significantly improves the separation performance. In contrast, only marginal improvements are obtained through preliminary conditioning by Köppen-Geiger climate class. Overall, it is concluded that GISPLITv3, which is not dependent on climate class but makes use of machine learning for the most challenging sky conditions, can be asserted as the new high-performance quasi-universal separation model.

Framing of Political News in Pakistani Newspapers: Comparative Analysis of Vote of No Confidence Campaign Issue 2022

Pakistan has always gone through political instability since its inception.(Zahid, A, et al.,2021).The study investigated the role of leading Pakistani newspapers during the vote on the issue of no-confidence. Two newspapers, Dawn News(English) and The Daily Jang(Urdu) were selected for comparative framing analysis. Content analysis using the Categorization Scheme, with pre-established categories developed with the Framing theory as a basis, is employed. Five different frames determined by Semetko and Volkenburg(2000) are observed in the selected newspapers. The result of this study showed that there was a significant difference between the coverage of Dawn News and The Daily Jang. The conflict frame was dominant in The Daily Jang, whereas the responsibility frame was dominant in Dawn News' coverage. Dawn News presented a more neutral Slant than The Daily Jang. Future studies will be done with a larger sample of newspapers and qualitative interviews with editors to gain insight into political issues.

Anterior Cervical Discectomy and Fusion Surgery: Results with Zero - Profile Spacer/Cage

OBJECTIVES This study aimed to evaluate the clinical outcomes of Anterior Cervical Discectomy and Fusion (ACDF) treatments for cervical disc degenerative disease (CDDD) using a Zero Profile cage. METHODOLOGY A retrospective study of 26 patients with cervical disc disease treated with a zero-profile cage was designed and followed up for an average of 12 months in descriptive research. For arm and neck pain, the Neck Disability Index (NDI) and Visual Analogue Scale (VAS) scores were used to assess function. Nurick’s myelopathy classification scheme based on gait impairments was also documented. RESULTSThe average age of the 26 patients was 48.96 13.13 years (mean SD), with 44 percent falling into the 40-60-year age bracket. The male/female gender distribution was 21 (84%) and 04 (16%) male/female. Radiculomyelopathic symptoms were detected in 11 (28%) of the patients, with radicular pain occurring bilaterally in seven (28%) of the patients and on the left side in four (16%). A radiological examination of fusion was performed at six months and one year. The fusion had a success rate of 95 percent at six months (19 patients) and 100 percent at one year. CONCLUSION A zero-profile device provides biomechanical stability and fusion rates with excellent outcomes for one- and two-level ACDFs. Advantages include low rates of dysphagia, decreased operative time, restoration of cervical lordosis and disc height, and lack of cage subsidence or screw back out.

Classification Of Bll Blazars By Optical Activity Types

We have carried out a spectral classification by the Activity Types for a subsample of Blazars from the BZCAT v.5 Catalogue, namely the BL Lac (BLL, Lacertids) candidates, designated in the Catalogue as BZB subtype objects. The classification is based on the Sloan Digital Sky Survey (SDSS) homogeneous medium-resolution optical spectroscopy and along with the standard BPTtype diagnostic diagrams, we have applied our newly introduced fine classification scheme with subtypes of Quasars and considering many more features. Out of 1151 BZB objects, 552 having SDSS spectra were classified. After new classification, 259 (46.1%) of 552 have not changed their optical class, and 293 (53.1%) of these objects have changed their optical class. Having this new information on the optical classification we suggest to change the classification of some objects in BZCAT: for 130 BZB objects to BZG, for 18 BZB objects to BZQ and for 145 BZB objects to BZU.

Characteristics of Patients Hospitalized in Rural and Urban ICUs From 2010 to 2019.

Rural hospitals are threatened by workforce shortages and financial strain. To optimize regional critical care delivery, it is essential to understand what types of patients receive intensive care in rural and urban hospitals. A retrospective cohort study. All fee-for-service Medicare beneficiaries in the United States who were 65 years old or older hospitalized in an ICU between 2010 and 2019 were included. Rural and urban hospitals were classified according to the 2013 National Center For Health Statistics Urban-Rural Classification Scheme for Counties. Patient comorbidities, primary diagnoses, organ dysfunction, and procedures were measured using the International Classification of Diseases , 9th and 10th revisions diagnosis and procedure codes. Standardized differences were used to compare rural and urban patient admission characteristics. None. There were 12,224,097 ICU admissions between 2010 and 2019, and 1,488,347 admissions (12.2%) were to rural hospitals. The most common diagnoses in rural hospitals were cardiac (30.3%), infectious (24.6%), and respiratory (10.9%). Patients in rural ICUs had similar organ dysfunction compared with urban hospitals (mean organ failures in rural ICUs 0.5, sd 0.8; mean organ failures in urban ICUs 0.6, sd 0.9, absolute standardized mean difference 0.096). Organ dysfunction among rural ICU admissions increased over time (0.4 mean organ failures in 2010 to 0.7 in 2019, p < 0.001). Rural hospitals care for an increasingly complex critically ill patient population with similar organ dysfunction as urban hospitals. There is a pressing need to develop policies at federal and regional healthcare system levels to support the continued provision of high-quality ICU care within rural hospitals.