Categorization Method Research Articles

Exploring the Relationship Between Socioeconomic Attributes and Resilience Capacity of Murrah Buffalo-based Livestock Production System in Changing Climatic Scenario

Background: The present study was conducted to study the resilience capacity towards changing climate of a farming community who reared specifically Murrah buffaloes in their livestock production system for their livelihood. As the study area was breeding tract of Murrah buffalo, hence in-depth study of resilience was performed in frequent changing climate. Methods: In order to assess the resilience capacity and their relation with socioeconomic characteristic, total 320 Murrah buffalo farmers across four districts in India’s Trans-Gangetic Plain, data were gathered using focused group discussions, PRA tools and in person interviews. Statistical tools such as principal component analysis for index development, cumulative square root methods for categorization of respondents and regression analysis were performed for exploring relationship. Result: Result depicted that majority of the farmers has medium level of climate resilience index score i.e. 0.456. Further, it was found that farmer’s involvement in social institute such as, gram panchayat, agriculture and dairy co-operative society, self-help group, farmers’ association and others affected their resilience capacity in changing climatic scenario. The need for better technology inclusion and access to more formal systems of finance is necessary to increase the overall resilience capacity of households.

Detecting homophobic and transphobic texts from youtube comments using machine learning models

Today's world has witnessed an exponential rise in disseminating degrading and offensive content via social media. A global increase in violence against minorities, such as gun violence, murders, and forced displacement, has been connected to using harsh and derogatory language online. The policies enacted to prevent abusive or derogatory language risk stifling free speech and are applied differently. These languages can affect the mental state of social media users. Homophobic and transphobic expressions are a insulting people's sexuality or character among harsh and disrespectful remarks. To study social media information and discriminate between homophobic and transphobic comments, it is necessary to construct language-based automatic categorization methods. This dataset consists of Tamil YouTube comments collected as part of the Shared Task on Sentiment Analysis and Homophobia detection of YouTube comments in Code-Mixed Dravidian Languages. This study detects and categorises abusive comments using machine learning models such as SVM, Naive Bayes, Logistic regression, and KNN. In comparison to other classifiers, the accuracy of logistic regression is the highest, at 55%.

Prediction Evaluation of Gene Ontology Using Support Vector Machine

The present state of sequenced programs requires the assignment of gene product functions in a timely, accurate and trustworthy manner. Many approaches to large-scale label designs have been developed. On the other hand, these approaches can only be used on a limited number of sub-sets. Their conclusions are not formalized. On the other hand, such approaches can only be used on a limited number of subsets, as their conclusions are not standardized. Annotation was supplied using Gene Ontology (GO) or categorization of valid or incorrect prediction using Support Vector Machines (SVM). A large database was used to assess the system's overall effectiveness. Reliability prediction was cross-validated organization by organization, yielding an average accuracy of 74% of all test cycles and 80%. The verification results revealed that the predictive efficacy was not dependent on the micro-organism because it could duplicate the high-quality automatic manual annotation. We used our trained categorization method to annotate Xenopuslaevis sequences, and greater than half of the known expressed genome was functionally annotated. We gave more than double the number of contigs with excellent annotations of high brightness compared to the already accessible annotations, and we also allocated a confidence score to each anticipated Gene Ontology (GO).

ESPEG model and corporate governance system for ensuring sustainable development of enterprises

In 2023, there is a pressing requirement to promote enterprise growth and guarantee enduring sustainability, therefore, it is timely to craft an effective model for sustainable progress and establish a corporate governance framework. The aim of the research was to develop innovative approaches to ensure the sustainable development of enterprises through the development of a sustainable development model and corporate governance system. The essence of the concepts of “corporate governance” and “corporate management” is also defined in the article using the categorical method of two-level triadic decoding. The research is based on the principles of sustainable development, systemic and holistic approaches. The article proposes a four-component model of the enterprise sustainable development concept (ESPEG model), which reflects the hierarchical arrangement of the environmental (E), socio-political (SP), economic (E), and governance (G) spheres of the enterprise. This model identifies the directions for the application of management tools to influence specific interacting spheres. The result of the work is an improved model of enterprise sustainable development that enables the structuring of the key components of the concept to ensure effective management of the enterprise based on the balance of its key elements. The model of corporate governance system has been improved, incorporating the paradigm of sustainable development and considering the necessity of integrating various management subsystems within the company. The proposed corporate governance system identifies the control loop of corporate management and defines the governing and controlled subsystems along with their components. It has been established that a corporate governance system built on the principles of sustainable development will contribute to achieving positive results in the company’s development, ensuring the sustainability of its social, environmental, and economic aspects. The implementation of the proposed corporate governance system will facilitate further research into its essence and application specifics in modern companies

Особенности этикета виртуальной коммуникации

The article describes the concept of network etiquette, or virtual communication etiquette, as a special type of communication carried out with the help of technical means. The research objective was to characterize netiquette and its basic rules. The review includes domestic and foreign publications on the theory of communication and speech etiquette. The list of methods includes general scientific methods of description, analysis, categorization, classification, and comparison, as well as some elements of interpretive stylistic analysis. Politeness is the main principle of netiquette: it serves as a regulator of communicative behavior. Polite online behavior includes greetings and addresses by name; a ban on excessive emoticons, insults, and profanity; abuse of abbreviations; compliance with the basic punctuation rules; personal signature, etc. Netiquette also follows the rules of communication established by a particular network resource or platform.

SAGE III/ISS aerosol/cloud categorization and its impact on GloSSAC

Abstract. The Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) began its mission in June 2017. SAGE III/ISS is an updated version of the SAGE III on Meteor (SAGE III/M3M) instrument and makes observations of the stratospheric aerosol extinction coefficient at wavelengths that range from 385 to 1550 nm with a near-global coverage between 60∘ S and 60∘ N. While SAGE III/ISS makes reliable and robust solar occultation measurements in the stratosphere, similar to its predecessors, interpreting aerosol extinction measurements in the vicinity of the tropopause and in the troposphere has been a challenge for all SAGE instruments because of the potential for cloud interference. Herein, we discuss some of the challenges associated with discriminating between aerosols and clouds within the extinction measurements and describe the methods implemented to categorize clouds and aerosols using available SAGE III/ISS aerosol measurements. This cloud/aerosol categorization method is based on the results of Thomason and Vernier (2013), with some modifications that now incorporate the influence of recent volcanic/pyrocumulonimbus (PyroCb) events. Herein we describe this new cloud/aerosol categorization algorithm, demonstrate how it identifies enhanced aerosols and aerosol–cloud mixture in the lower stratospheric region, and discuss the impact of this cloud-filtering algorithm on the latest release of the Global Space-based Stratospheric Aerosol Climatology (GloSSAC) data set.

Automated cardiovascular risk categorization through AI-driven coronary calcium quantification in cardiac PET acquired attenuation correction CT

BackgroundWe present an automatic method for coronary artery calcium (CAC) quantification and cardiovascular risk categorization in CT attenuation correction (CTAC) scans acquired at rest and stress during cardiac PET/CT. The method segments CAC according to visual assessment rather than the commonly used CT-number threshold. MethodsThe method decomposes an image containing CAC into a synthetic image without CAC and an image showing only CAC. Extensive evaluation was performed in a set of 98 patients, each having rest and stress CTAC scans and a dedicated calcium scoring CT (CSCT). Standard manual calcium scoring in CSCT provided the reference standard. ResultsThe interscan reproducibility of CAC quantification computed as average absolute relative differences between CTAC and CSCT scan pairs was 75% and 85% at rest and stress using the automatic method compared to 121% and 114% using clinical calcium scoring. Agreement between automatic risk assessment in CTAC and clinical risk categorization in CSCT resulted in linearly weighted kappa of 0.65 compared to 0.40 between CTAC and CSCT using clinically used calcium scoring. ConclusionThe increased interscan reproducibility achieved by our method may allow routine cardiovascular risk assessment in CTAC, potentially relieving the need for dedicated CSCT.

The World as Existential Security: The Concept of Peace of Third Generation

The relevance of the topic is due to the fact that traditional social disciplines have not been able to solve the most important problems of humanity – the elimination of destructive forms of social interactions. The article deals with the problem of conceptualization of the concept of “peace”, the stages of formation of the disciplinary field of peace research. The hypothesis of the study is that understanding the initial intention that prompts the formation of new value orientations in human populations makes it possible to base the modern theory of the world on the concept of existential security. The purpose of the article is to analyze approaches to one of the basic concepts of social and humanitarian disciplines ‒ the category of peace. The use of methods of categorization, retrospective analysis and systematization allowed us to discover the paradigmatic dependence of the content of the concept peace on objective technological and socio-cultural circumstances. The results of the discussion were the following provisions. The first concept of peace, called the negative peace, could not be used as the basis for social construction programs, since it carried all the limitations of the classical paradigm of science, which was formed to serve natural science. The modern, third generation of the concept of peace, which has received the complex name of a multi-inter-transcultural world, is generated by the dangers associated with globalization and entry into digital civilization. Understanding the initial intention that encourages the formation of new value orientations in human populations allows us to base the modern theory of the world on the concept of existential security. The concept of existential security is formulated, which should be understood not as the physical security of an individual, but as the security of the existence of a population that allows preserving the population gene pool. A society that has provided existential security corresponds to people’s ideas of peaceful existence and does not create conditions for war, as one of the forms of violence, or for other forms of structural violence that reduce the adaptive potential of the population. Due to the fact that the adaptability and security of the digital society is ensured by the integrative and communitarian properties of the social system, post-material values are put forward in the first place in the value system in the modern world. They should be the basis of the concept of the existential world.

Fine-Grained Visual Categorization: A Spatial–Frequency Feature Fusion Perspective

Fine-grained visual categorization is a challenging issue owing to high intra-class and low inter-class variances. Classical approaches rely on pre-trained models or many fine annotations. In this paper, we observe that spatial and frequency information provides distinct image views, and propose a new spatial–frequency feature fusion (SFFF) perspective to handle this challenging issue. Specifically, we design a heterogeneous feature extraction loss function, construct a global and local fusion SFFF network, and propose an importance-sparsity selection strategy. For feature extraction, we focus on the frequency domain feature learning network, extract fine-grained features, and achieve feature complementarity. For feature selection, we propose importance ranking and sparse regularity to constrain spatial–frequency features. For feature fusion, we design a spatial–frequency loss and an inter-layer switching strategy to achieve local-global collaboration. Comparative experiments were performed on popular fine-grained datasets and classic datasets such as CUB200-2011, Stanford Cars, Stanford Dogs, FGVC-Aircraft, and CIFAR100. The effectiveness and outstanding performance of SFFF are confirmed by comparisons with more than 40 state-of-the-art fine-grained categorization methods. Ablation studies and visualizations are provided to facilitate an understanding of our approach.

무덤 연구의 다각화를 위한 노력

Research on burials in archaeology has mainly involved the identification of tomb type and the classification of the material. Although such an arrangement of the data is necessary prior to analysis, the method of ‘grouping based on similarities or differences’, which has been commonly used to organize archaeological data, is not without its problems. This is because the existing method of classification cannot handle ‘outlier’ tombs that deviate from the general trend; it is also difficult to highlight the differences that exist between tombs grouped together based on similarity, or, conversely, the similarities between the tombs divided into different groups. In this paper, ‘network visualization’ was presented a san alternative method of organizing data to compensate for such problems. First of all, a theoretical review of ‘categorization’ and ‘classification’ was conducted in order to suggest how ‘network visualization’ could provide an alternative method of categorization. This possibility was demonstrated using the case study of the burials of the Sangwoon-ri site, in Wanju. In addition, Deleuze and Guattari’s concept of Assemblage was introduced, as a means of supporting a break away from essentialism in categorization. As a result, a new perspective on archaeological style, form, and type was proposed and it was argued, using the example of the Nangnang (Lelang) tombs, that the ‘sub-types’ of the tombs could be viewed as ‘events’ that appear in the ‘creation process of types’, rather than as fixed entities. Finally, visualization methods of multivariate statistical analysis results were also examined to confirm whether the methodological and epistemological possibilities of network visualization presented in this paper are unique to it. By presenting the archaeological usefulness of the concept of network visualization and Assemblage, the paper aims to stimulate further critical thought on the possibility of alternative methods of categorization that can consider similarities and differences together, or indeed the fluidity of styles, forms, and types.

Survey on Different Morphology Detection Techniques

Abstract: There is a lot of data traffic that translates and transports data in the digital environment of the World Wide Web. The data is presented as files and photos. Data can morph, so it's important to detect these instances. The suggested method will identify modified photographs and alert the user to the validity of the images. In recent years, the research community has paid a great deal of attention to the problem of morph attack detection. To accurately detect morph attacks, various studies have been done in this area and various methods have been used. Although enough morph images are not readily available for research purposes, a variety of face databases are used to create morph image databases. Attack detection via face morphing is difficult. Automated border control gates utilize both manual inspection and automatic categorization methods to identify morphing attacks. It is vital to comprehend how a machine learning system can recognise altered faces and the most pertinent facial regions.

Substance use disorders, physical health and recovery capital: Examining the experiences of clients and the alcohol and other drug workforce.

Challenges associated with the integration of physical health within alcohol and other drug (AOD) treatment services persist. The construct of recovery capital has gained currency within the sector, however, its potential in understanding the integration of physical health within AOD treatment has not been examined. This study explores the role of physical health in the process of recovery, examining the potential of 'recovery capital' frameworks to enhance integrated care. Interviews were conducted with residential and outpatient AOD services across New South Wales, Australia. Qualitative data was collected from clients (n = 20) and staff (n = 13) and transcribed. Data were systematically coded and analysed using the iterative categorisation method. Unmanaged physical health issues compromised recovery, particularly when associated with hopelessness and pain. Improved physical health ameliorated sources of negative recovery capital such as boredom, social isolation and mental health symptoms. When addressed during treatment, physical health facilitated recovery capital resources through knowledge and skill acquisition, increased autonomy, enjoyment and immersive experience. Benefits extended to domains of 'social capital' (social connection) and 'cultural capital' (physical appearance). Physical health offers variegated pathways for building recovery capital. Findings support calls for a more assertive approach to addressing physical health when AOD treatment occurs outside of primary care settings. Future research should examine the potential of recovery capital as a heuristic for optimising care of physical health issues and health-related behaviour within AOD treatment services.

Cognitive advantages of sustainability concept mapping

Concept maps, which are graphical tools for organizing and connecting knowledge, hold promise for supporting students across disciplines in exercising systems thinking while tackling sustainability issues. We conducted a study to quantify cognitive load and performance (as an indicator of cognitive flexibility) among undergraduate students (N = 25) when engaging in sustainability concept mapping, as compared to simple listing tasks. Participants created two concept maps and two lists, engaging with four sustainability topics: water availability, climate change, food security, and renewable energy. During tasks, perceived and neurological cognitive load were assessed using the NASA Task Load Index (TLX) and a B-Alert X10 electroencephalogram (EEG), respectively. Concept mapping performance was quantified using established Traditional and Categorical Methods. Participants experienced higher perceived load (p < 0.001) and similar neurological load (p = 0.291) during concept mapping, as compared to listing. Higher neurological load during concept mapping was associated only with lower effort (ρ = −0.416, p < 0.01). Concept maps demonstrating greater breadth and connectedness of knowledge were associated with lower perceived total workload (ρ = −0.354, p < 0.05). Also, no concept map performance metric was associated with increased neurological load (p > 0.05). As such, we propose that concept maps may indeed be a uniquely-suited tool for helping students mediate cognitive load during systems thinking, thereby supporting educators in furthering education for sustainable development.

Classification of Gujarati Articles using Bernoulli Naïve Bayes Classifier and Extra-trees Classifier

On the internet, information technology generated massive amounts of data. Because this data was initially primarily in English, the majority of data mining research was conducted on English text documents. As internet usage grew, so did data in other languages such as Gujarati, Marathi, Tamil, Telugu, and Punjabi, among others. We present a text categorization method based on artificial text summarization of Gujarati Articles in this paper. For the classification of text documents, various learning techniques such as Naïve Bayes, Support Vector Machines, and Decision Trees are available. We gathered articles from various e-newspaper editorials. This paper focuses on a brief review of the various techniques and methods for Gujarati Articles Classification, so that research in Text Classification can be further explored using various classifier algorithms. The dataset, which contains 1604 documents from 8 different categories, is used by the system. The result shows that Stacking Classifier with Bernoulli Naïve Bayes Classifier and Extra-trees Classifier is efficient for Gujarati Articles.

Digital Literacy Requires School Resilience

Digital literacy requires school resilience. The purpose of this study is to describe the profile of school resilience and strategies for strengthening school resilience in order to strengthen digital literacy. The method used in this study is a descriptive method that combines quantitative and qualitative (mix method). The research was conducted at SMN N 1 Mungkid involving 40 teachers and 100 students with a multistage random sampling model. The sampling technique in this study was determined using the Lameshow formula, with a total sample of 140 people. Data collection techniques were also carried out using a questionnaire technique through online media (google form) and documentation. The analytical technique used is descriptive with the Excel program using a categorization method based on percentage calculations. The results showed that the overall resilience of Mungkid Junior High School in facing the digital literacy era according to teachers and students was at the lower score, which is in the category of positive resilience building scale (teachers: 31%; Students: 40%). The strategy for strengthening school resilience in the digital literacy era are (1) adapting to situations and conditions, (2) school intervention in overcoming environmental risks and transitions, (3) identifying factors that can help schools overcome crises, (4) skills mapping to identify training needs, (5) identifying the strengths and weaknesses of the education system, and (6) documenting the learning gained by taking into account the availability of school human resources (HR), continuous professional development, communication, network support, adaptation and knowledge-based development.

RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma.

Glioblastomas (GBM) are rapidly growing, aggressive, nearly uniformly fatal, and the most common primary type of brain cancer. They exhibit significant heterogeneity and resistance to treatment, limiting the ability to analyze dynamic biological behavior that drives response and resistance, which are central to advancing outcomes in glioblastoma. Analysis of the proteome aimed at signal change over time provides a potential opportunity for non-invasive classification and examination of the response to treatment by identifying protein biomarkers associated with interventions. However, data acquired using large proteomic panels must be more intuitively interpretable, requiring computational analysis to identify trends. Machine learning is increasingly employed, however, it requires feature selection which has a critical and considerable effect on machine learning problems when applied to large-scale data to reduce the number of parameters, improve generalization, and find essential predictors. In this study, using 7k proteomic data generated from the analysis of serum obtained from 82 patients with GBM pre- and post-completion of concurrent chemoirradiation (CRT), we aimed to select the most discriminative proteomic features that define proteomic alteration that is the result of administering CRT. Thus, we present a novel rank-based feature weighting method (RadWise) to identify relevant proteomic parameters using two popular feature selection methods, least absolute shrinkage and selection operator (LASSO) and the minimum redundancy maximum relevance (mRMR). The computational results show that the proposed method yields outstanding results with very few selected proteomic features, with higher accuracy rate performance than methods that do not employ a feature selection process. While the computational method identified several proteomic signals identical to the clinical intuitive (heuristic approach), several heuristically identified proteomic signals were not selected while other novel proteomic biomarkers not selected with the heuristic approach that carry biological prognostic relevance in GBM only emerged with the novel method. The computational results show that the proposed method yields promising results, reducing 7k proteomic data to 8 selected proteomic features with a performance value of 96.364%, comparing favorably with techniques that do not employ feature selection.

Contextual Perception in Internet of Mobile Things: A categorical structure

A new perception model in the Internet of Mobile Things (IoMT) is now underway: Contextual Perception (CP), which is encouraged by the mankind autonomic neural system functioning. CP’s general goal is to understand clearly IoMT, which can be self-managed without direct mankind intervention. To address this huge challenge of the CP expects a fundamental solution of the CP notion. For this purpose, in this article, a categorical method is used to construct a robust formal foundation for the CP model in order to obtain the CP formal semantics. Furthermore, the categorical structure of CP in IoMT is formally specified in which IoMT products and co-products, finite limits and colimits in IoMT are formally modeled and argued.

Breaking Gender Stereotypes in the Gaming Community: Importance in Providing All-Round Support for Female Gamer Against Misogyny

The experience of female gamers in online gaming and gaming communities arecharacterized by a lack of social-emotional and organizational support, as well as prevalent general harassment and sexual harassment. Offline, negative stereotypes associated with female gamers and the lack of understanding from others towards women with video games as a hobbylead to the unwillingness of women to self-identify as gamers. Gender stereotypes, specifically, traits of male gamers being traditionally associated with gamer stereotypes cause the exclusion of female gamers and feminine traits from fair and normal treatment in the gaming community. In this contribution, the effects of existing gender stereotypes and misogynistic ideals present insociety on the gamer stereotype and gaming community, as well as the subsequent treatment of female gamers are examined. I conclude that only a multifaceted approach capable of addressing the aforementioned relationship between the larger society and the gaming world would beeffective in improving the overall condition and support for women in the gaming community. Additionally, I argue that the categorization and grouping of coping strategies used by female gamers in response to harassment online should be standardized into specific categories – taking into account previous methods of categorization grounded in both quantitative and qualitativeapproaches – to benefit future research.

DenseNet_ HybWWoA: A DenseNet-Based Brain Metastasis Classification with a Hybrid Metaheuristic Feature Selection Strategy.

Brain metastases (BM) are the most severe consequence of malignancy in the brain, resulting in substantial illness and death. The most common primary tumors that progress to BM are lung, breast, and melanoma. Historically, BM patients had poor clinical outcomes, with limited treatment options including surgery, stereotactic radiation therapy (SRS), whole brain radiation therapy (WBRT), systemic therapy, and symptom control alone. Magnetic Resonance Imaging (MRI) is a valuable tool for detecting cerebral tumors, though it is not infallible, as cerebral matter is interchangeable. This study offers a novel method for categorizing differing brain tumors in this context. This research additionally presents a combination of optimization algorithms called the Hybrid Whale and Water Waves Optimization Algorithm (HybWWoA), which is used to identify features by reducing the size of recovered features. This algorithm combines whale optimization and water waves optimization. The categorization procedure is consequently carried out using a DenseNet algorithm. The suggested cancer categorization method is evaluated on a number of factors, including precision, specificity, and sensitivity. The final assessment findings showed that the suggested approach exceeded the authors' expectations, with an F1-score of 97% and accuracy, precision, memory, and recollection of 92.1%, 98.5%, and 92.1%, respectively.

White, or not quite? Predicting Arab American responses to racial categorization forms

We examined the implications of the institutional racial/ethnic designation of Arab Americans as White. Do Arab Americans prefer this categorization or another, and what factors predict categorization in one way or another? In Study 1, a representative sample of Arab Americans in Southeast Michigan (N = 1,001 57% female, ages 18 to 88, Age M = 43.64) completed measures of perceived discrimination, various forms of social identification, and self-categorized from Census-designated racial categories. Self-categorization as “Other” was significantly predicted by experiences of discrimination, Muslim religious affiliation, and having darker skin. In Study 2, with a convenience sample of Arab American college students (52% female, Age M = 20.25), participants were randomly assigned to self-categorize as either “White” or as “Middle Eastern/North African” and then completed measures of perceived discrimination and various forms of social identification. Assigned self-categorization as “Middle Eastern/North African” significantly predicted subgroup respect towards Arabs, but only among those who strongly identified as American. Far from being a neutral, merely reflective method of categorization, the Census and similar categorization forms are sites of racial/ethnic socialization. Respondents bring to such forms their social psychological experience. For many Arab Americans, a host of social experiences indicate the (in)appropriateness and meaning of being forced to self-categorize as White or being allowed to self-categorize differently.