Search results for: gender classification

Authors: Joseph Kimutai Langat

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Tea typology is the science of classifying tea. This study was carried out between November 2023 and July 2024, whose main objective was to investigate the typological classification nomenclature of processed tea in the world, narrowing down to Kenya. Centres of origin, historical background, tea growing region, scientific naming system, market, fermentation levels, processing/ oxidation levels and cultural reasons are used to classify tea at present. Of these, the most common typology is by oxidation, and more specifically, by the production methods within the oxidation categories. While the Asian tea producing countries categorises tea products based on the decreasing oxidation levels during the manufacturing process: black tea, green tea, oolong tea and instant tea, Kenya’s tea typology system is based on the degree of fermentation process, i.e. black tea, purple tea, green tea and white tea. Tea is also classified into five categories: black tea, green tea, white tea, oolong tea, and dark tea. Black tea is the main tea processed and exported in Kenya, manufactured mainly by withering, rolling, or by use of cutting-tearing-curling (CTC) method that ensures efficient conversion of leaf herbage to made tea, oxidizing, and drying before being sorted into different grades. It is from these varied typological methods that this review paper concludes that different regions of the world use different classification nomenclature. Therefore, since tea typology is not standardized, it is recommended that a global tea regulator dealing in tea classification be created to standardize tea typology, with domestic in-country regulatory bodies in tea growing countries accredited to implement the global-wide typological agreements and resolutions.

Keywords: classification, fermentation, oxidation, tea, typology

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: Suchitra Pal, Arjun Mitra

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Objective: The purpose of this empirical study is to ascertain if gender, personality traits and social support predict the self-esteem amongst business school students. Method: The study was conducted through an online survey administered on 160 business school students of which equal-number of males and females were taken, with controls for education and family income status. The participants were contacted through emails. Data was gathered and statistically analyzed to determine the relationship between the variables. Results: The results showed that gender was not associated with self-esteem. Whilst all the personality and social support factors were found to be significantly inter-correlated with self-esteem, only extraversion, openness to new experiences, conscientiousness, emotional stability and total perceived social support were found to predict self-esteem. Conclusion: The findings were explained in the light of existing conceptualizations in the field of self-concept. Recommendations for early identification and interventions for a population with lower self-esteem levels have been made based on findings of the study. Major implications for researchers and practitioners are discussed.

Keywords: self-esteem, personality, social support, gender, self-concept

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Authors: Yu Yitian, Chen Kaizhe, Liu Jin

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'Glass ceiling' phenomenon refers to the invisible barriers that specific groups encounter in career advancement within organizations. This phenomenon is widespread all over the world and is prevalent among university faculty. However, there has been limited attention in the previous studies on Chinese university faculty. This research mainly focuses on whether the existence of 'glass ceiling' phenomenon exists among female faculty in the Chinese academic community and the characteristics among different disciplines in China. By utilizing the big data from education faculty members in 149 research-oriented universities in China, the research employs a Curriculum Vitae analysis to draw the academic career trajectories of faculty, along with potential variations across different academic disciplines within the Chinese academic landscape. This research addresses the existing gap in the scholarly investigation of gender equality in China and is helpful to promote gender equality in the academic community.

Keywords: big data, China academic community, curriculum vitae analysis, glass ceiling

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Authors: Kaewkarn Phuangsombat, Arthit Phuangsombat, Anupun Terdwongworakul

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Hard seeds will not grow and can cause mold in sprouting process. Thus, the hard seeds need to be separated from the normal seeds. Near infrared hyperspectral imaging in a range of 900 to 1700 nm was implemented to develop a model by partial least squares discriminant analysis to discriminate the hard seeds from the normal seeds. The orientation of the seeds was also studied to compare the performance of the models. The model based on hilum-up orientation achieved the best result giving the coefficient of determination of 0.98, and root mean square error of prediction of 0.07 with classification accuracy was equal to 100%.

Keywords: mung bean, near infrared, germinatability, hard seed

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Authors: Rana Alaa El-Deen Ahmed, M. Elemam Shehab, Shereen Morsy, Nermeen Mekawie

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With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. Since selling a wide variety of products has become easier due to the popularity of online stores, online retailers are able to sell more products than a physical store. The disadvantage is that the customers might not find products they need. In this research the customer will be able to find the products he is searching for, because recommender systems are used in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the needed product. In this paper eleven classification algorithms are comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior in the experimented dataset. The WEKA knowledge analysis tool, which is an open source data mining workbench software used in comparing conventional classifiers to get the best classifier was used in this research. In this research by using the data mining tool (WEKA) with the experimented classifiers the results show that decision table and filtered classifier gives the highest accuracy and the lowest accuracy classification via clustering and simple cart.

Keywords: classification, data mining, machine learning, online shopping, WEKA

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Authors: Marvelyn F. Carolino, Charlene R. Cunanan, Gellien Faith O. Masongsong, Berlinda A. Ofrecio

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This study delves into the language usage differences among men, women, and individuals with other gender preferences. It specifically centers on the sociolinguistic aspects within the English majors at the College of Education of Rizal Technological University-Pasig, spanning from the first-year to fourth-year levels. The researchers employed a triangulation approach for data collection, utilizing a validated self-made questionnaire, interviews, and observations. The results revealed that language usage among different genders is influenced by a combination of cultural norms, social dynamics, and technological factors. Cultural norms significantly shape how respondents use language, as they conform to expected speech patterns based on their gender. Social factors, such as peer pressure, were found to impact language usage for individuals of all genders. This influence was viewed as constructive for personal development rather than inhibiting performance or communication. In terms of technological factors, respondents strongly agreed that the time spent on social media and educational applications influenced their language use. These platforms provided opportunities to expand and enhance their vocabulary. Additionally, the study employed hypothesis testing through the z-test formula to assess the impact of demographic profiles on language usage differences among genders. The results indicated that gender, economic status, locality, and ethnicity did not show statistically significant differences in language use. This lack of significant variation in findings was attributed to the relatively homogeneous demographic profile of respondents, primarily composed of females with low-income backgrounds and Tagalog ethnicity. This demographic similarity likely minimized the diversity of responses.

Keywords: gender, language, sociolinguistics, differences

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Authors: Usman Tunde Saadu

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The study examined gender differences in emotional adjustment of fresh students in Kwara State University, Malete. The descriptive survey design was adopted for the study, and 300 fresh students were randomly selected across the six colleges in the University. An adapted Questionnaire from Nadia (2012) was used to collect data from respondents on emotional adjustment. One research question was answered with a descriptive statistic of frequency count and percentage, and one hypothesis was tested with t-test statistical analysis at 0.05 level of significance. Findings of the study revealed that fresh students have a low level of emotional adjustment, and male students were found to have more emotional adjustment than female. Based on these findings, the researcher, therefore, concluded that fresh students have a low level of emotional adjustment. Based on these findings, the researcher recommended among others that emotional adjustment skills should be introduced into the secondary school curriculum to give students the opportunity to learn about these skills before they are being admitted into University.

Keywords: emotional adjustment, fresh students, gender differences, students

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Authors: K. Michael, G. Yanay-Ventura

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Objectives: It is well established that employees are the essence of the organization. Employees' personal characteristics and emotional state may decrease or increase organizational performance. Therefore, organizations should enhance employees' well-being. The present study examined gender and seniority differences in three factors of employees' well-being: motivation, commitment, and burnout. Methods: Participants in this quantitative cross-sectional study were 400 service organization employees aged 19-71 (Mean=29.94; SD=10.25). Regarding gender, 59.7% were women, and regarding seniority, 66.9% were less than two years in the organization. All participants completed questionnaires evaluating motivation, sense of organizational commitment (affective, continuance), and level of burnout (emotional exhaustion, depersonalization, personal accomplishment). Data were analyzed using IBM-SPSS (version 25) through independent-sample t-tests. Results: Women were less motivated and felt less affective commitment toward the organization than men. They also felt more burnout than men in terms of emotional exhaustion and depersonalization. Additionally, employees in lower seniority levels felt less affective commitment toward the organization than employees in higher seniority levels. They also felt more burnout than employees in higher seniority levels in terms of emotional exhaustion, depersonalization, and personal accomplishment. Conclusions: The findings suggest that women and employees in lower seniority levels experience more vulnerable emotions in service organizations. Therefore, strategies for enhancing their well-being are recommended.

Keywords: burnout, gender and seniority differences, motivation, organizational commitment

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

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Authors: Hamidreza Fallah Lajimi , Elham Karami, Fatemeh Ali nasab, Mostafa Mahdavikia

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Being responsive is an increasingly important skill for firms in today’s global economy; thus firms must be agile. Naturally, it follows that an organization’s agility depends on its supply chain being agile. However, achieving supply chain agility is a function of other abilities within the organization. This paper analyses results from a survey of 71 Iran manufacturing companies in order to identify some of the factors for agile organizations in managing their supply chains. Then we classification this company in four cluster with fuzzy c-mean technique and with four validations functional determine automatically the optimal number of clusters.

Keywords: agile supply chain, clustering, fuzzy clustering

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Authors: Simone Tamires Vieira

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This paper aims to investigate the history of women's schooling in Brazil and to reflect on the condition and social space of women today. Therefore, the following question arises as a research problem: how does the history of the school in/exclusion of women in Brazil relate to the occupations occupied today? As for the objectives, we seek to collect data on the education of women and girls in Brazil, analyze some institutionalized educational legislation and policies, reflect on issues of opportunity and deprivation in order to problematize the female condition through the review of qualitative literature. The results showed that gender and symbolic violence are powerful categories to analyze this theme since the trajectories, choices, and opportunities given to women are permeated by veiled mechanisms perpetuated by a structurally patriarchal society, focused on the interests of the elite, which denies diversity to maintain its status. The aim of this research is to contribute to reflections on the potential of dialogical action, as it highlights the forces that act and permeate the trajectories of women to empower current and future generations.

Keywords: gender, school in/exclusion symbolic violence, women, symbolic violence, women

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Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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Authors: Riffi Mohammed Amine

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A cloud is made up of small particles of liquid water or ice suspended in the atmosphere, which generally do not reach the ground. Various methods are used to classify clouds. This article focuses specifically on a technique known as particle swarm optimization (PSO), an AI approach inspired by the collective behaviors of animals living in groups, such as schools of fish and flocks of birds, and a method used to solve complex classification and optimization problems with approximate solutions. The proposed technique was evaluated using a series of second-generation METOSAT images taken by the MSG satellite. The acquired results indicate that the proposed method gave acceptable results.

Keywords: remote sensing, particle swarm optimization, clouds, meteorological image

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Authors: Narjis Kahtoon

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The issue of the wage discrimination on the basis of gender and social problem has been a significant research problem for several decades. Whereas lots of have explored reasons for the persistence of an inequality in the wages of male and female, none has successfully explained away the entire differentiation. The wage discrimination on the basis of gender and social problem of working women is a global issue. Although inequality in political and economic and social make-up of countries all over the world, the gender wage discrimination, and social constraint is present. The aim of the research is to examine the gender wage discrimination and social constraint from an international perspective and to determine whether any pattern exists among cultural dimensions of a country and the man and women remuneration gap in Readymade Garment Sector of Pakistan. Population growth rate is significant indicator used to explain the change in population and play a crucial point in the economic development of a country. In Pakistan, readymade garment sector consists of small, medium and large sized firms. With an estimated 30 percent of the workforce in textile- Garment is females’. Readymade garment industry is a labor intensive industry and relies on the skills of individual workers and provides highest value addition in the textile sector. In the Garment sector, female workers are concentrated in poorly paid, labor-intensive down-stream production (readymade garments, linen, towels, etc.), while male workers dominate capital- intensive (ginning, spinning and weaving) processes. Gender wage discrimination and social constraint are reality in Pakistan Labor Market. This research allows us not only to properly detect the size of gender wage discrimination and social constraint but to also fully understand its consequences in readymade garment sector of Pakistan. Furthermore, research will evaluated this measure for the three main clusters like Lahore, Karachi, and Faisalabad. These data contain complete details of male and female workers and supervisors in the readymade garment sector of Pakistan. These sources of information provide a unique opportunity to reanalyze the previous finding in the literature. The regression analysis focused on the standard 'Mincerian' earning equation and estimates it separately by gender, the research will also imply the cultural dimensions developed by Hofstede (2001) to profile a country’s cultural status and compare those cultural dimensions to the wage inequalities. Readymade garment of Pakistan is one of the important sectors since its products have huge demand at home and abroad. These researches will a major influence on the measures undertaken to design a public policy regarding wage discrimination and social constraint in readymade garment sector of Pakistan.

Keywords: gender wage differentials, decomposition, garment, cultural

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Authors: Samaneh Sadat Fattahi Massoom, Hossein Salimi Bajestani

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The study examines loneliness and depression in students who regularly care for themselves after school (latchkey students) in Mashhad and compares them with parent supervised students using a causal-comparative research method. The 270 participants, aged 7 -13, were selected using convenience and cluster random-assignment sampling. Independent t-test results showed significant differences between loneliness (-4.32, p ≤ 0.05) and depression (-3.02, p ≤0.05) among latchkey and non-latchkey students. Using the Pearson correlation test, significant correlation between depression and loneliness among latchkey students was also discovered (r=0.59, p ≤ 0.05). However, regarding non latchkey students, no significant difference between loneliness and depression was observed (r= 0.02. p ≥ 0.05). Multiple regression results also showed that depression variance can be determined by gender (22%) and loneliness (34%). The findings of this study, specifically the significant difference between latchkey and non-latchkey children regarding feelings of loneliness and depression, carries clear implications for parents. It can be concluded that mothers who spend most of their time working out of the house and devoid their children of their presence in the home may cause some form of mental distress like loneliness and depression. Moreover, gender differences affect the degree of these psychological disorders.

Keywords: loneliness, depression, self-care students, latchkey and non-latchkey students, gender

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

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Authors: Maria de Nazaré Moraes Soares, Silvia Maria Dias Pedro Rebouças, José Carlos Lázaro

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The Solidarity Economy (SE) acts in the re-articulation of the economic field to the other spheres of social action. The significant participation of women in SE resulted in the formation of a national network of self-managed enterprises in Brazil: The Solidarity and Feminist Economy Network (SFEN). The objective of the research is to identify factors of gender justice and feminist self-management practices that adhere to the reality of women in SE enterprises. The conceptual apparatus related to feminist studies in this research covers Nancy Fraser approaches on gender justice, and Patricia Yancey Martin approaches on feminist management practices, and authors of postcolonial feminism such as Mohanty and Maria Lugones, who lead the discussion to peripheral contexts, a necessary perspective when observing the women’s movement in SE. The research has a quantitative nature in the phases of data collection and analysis. The data collection was performed through two data sources: the database mapped in Brazil in 2010-2013 by the National Information System in Solidary Economy and 150 questionnaires with women from 16 enterprises in SFEN, in a state of Brazilian northeast. The data were analyzed using the multivariate statistical technique of Factor Analysis. The results show that the factors that define gender justice and feminist self-management practices in SE are interrelated in several levels, proving statistically the intersectional condition of the issue of women. The evidence from the quantitative analysis allowed us to understand the dimensions of gender justice and feminist management practices intersectionality; in this sense, the non-distribution of domestic work interferes in non-representation of women in public spaces, especially in peripheral contexts. The study contributes with important reflections to the studies of this area and can be complemented in the future with a qualitative research that approaches the perspective of women in the context of the SE self-management paradigm.

Keywords: feminist management practices, gender justice, self-management, solidarity economy

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Authors: Yashvi Italiya

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Dysthymia (DD) is a much-overlooked soft mood disorder and mostly confused with other forms of chronic depression. This research paper gives a spotlight to the DD prevailing in men and women. It also focuses on one of the comorbidities of Dysthymia, i.e., Anxiety. The comorbidities, hurdles in diagnosis, the ubiquity of the disorder, and the relation of Anxiety and DD are briefly described. Gender was the main focus here because the researcher of this paper found it as a research gap while doing the literature review. The study was done through secondary data obtained primarily from a questionnaire having Alpha 0.891 reliability. T-test method of data analysis was used to test the hypotheses. The result shows that the researcher failed to accept alternative hypothesis 1 (M1 > M2), while the alternative hypothesis 2 (M1 > M2) was accepted. The ratio of DD in women (M1) is not higher than that of men (M2) (hypothesis 1). But, women are more anxious than men (hypothesis 2). It was found that comorbid Anxiety is more widespread in one gender. It further plays a significant role in mixing up the symptoms. It was concluded that the dividing line between Dysthymia and MDD is still unclear for an accurate diagnosis. There is an essential need for spreading knowledge concerning the differences between the symptoms of DD and MDD so that the actual disorder can be identified, and proper help can be received from/provided by professionals.

Keywords: anxiety, comorbidity, dysthymia, gender, MDD

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Authors: Jesús Horacio, Alfredo Ollero, Víctor Bouzas-Blanco, Augusto Pérez-Alberti

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Rivers have traditionally been classified, assessed and managed in terms of hydrological, chemical and / or biological criteria. Geomorphological classifications had in the past a secondary role, although proposals like River Styles Framework, Catchment Baseline Survey or Stroud Rural Sustainable Drainage Project did incorporate geomorphology for management decision-making. In recent years many studies have been attracted to the geomorphological component. The geomorphological processes and their associated forms determine the structure of a river system. Understanding these processes and forms is a critical component of the sustainable rehabilitation of aquatic ecosystems. The fluvial auto-classification approach suggests that a river is a self-built natural system, with processes and forms designed to effectively preserve their ecological function (hydrologic, sedimentological and biological regime). Fluvial systems are formed by a wide range of elements with multiple non-linear interactions on different spatial and temporal scales. Besides, the fluvial auto-classification concept is built using data from the river itself, so that each classification developed is peculiar to the river studied. The variables used in the classification are specific stream power and mean grain size. A discriminant analysis showed that these variables are the best characterized processes and forms. The statistical technique applied allows to get an individual discriminant equation for each geomorphological type. The geomorphological classification was developed using sites with high naturalness. Each site is a control point of high ecological and geomorphological quality. The changes in the conditions of the control points will be quickly recognizable, and easy to apply a right management measures to recover the geomorphological type. The study focused on Galicia (NW Spain) and the mapping was made analyzing 122 control points (sites) distributed over eight river basins. In sum, this study provides a method for fluvial geomorphological classification that works as an open and flexible tool underlying the fluvial auto-classification concept. The hydrographic mapping is the visual expression of the results, such that each river has a particular map according to its geomorphological characteristics. Each geomorphological type is represented by a particular type of hydraulic geometry (channel width, width-depth ratio, hydraulic radius, etc.). An alteration of this geometry is indicative of a geomorphological disturbance (whether natural or anthropogenic). Hydrographic mapping is also dynamic because its meaning changes if there is a modification in the specific stream power and/or the mean grain size, that is, in the value of their equations. The researcher has to check annually some of the control points. This procedure allows to monitor the geomorphology quality of the rivers and to see if there are any alterations. The maps are useful to researchers and managers, especially for conservation work and river restoration.

Keywords: fluvial auto-classification concept, mapping, geomorphology, river

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Authors: Onaza Ajmal

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The purpose of the study is to explore the power relations linguistically and visually with reference to the representation of gender, race, violence, and empowerment through male characters and female superheroes from the two selected Marvel comics, Ms. Marvel (2014) and Captain Marvel (2019-). The study also aims to elaborate on the different cultural backgrounds of female superheroes and their choices and behaviors concerning the male characters. Moreover, it also seeks to explore whether the female superheroes reassert or resists the established gender roles. Using the tenets of critical discourse analysis (CDA) and feminist critical discourse analysis (FCDA) by Lazar (2005), the study analyzed the power relations from a feminist viewpoint. The linguistic analysis of textual features such as ‘adjectives’, ‘lexical items’, ‘metaphors’, and ‘use of pronouns’, etc., found in the selected comics is carried out under the framework of CDA given by Fairclough (1989). Kress and van Leeuwen's model of reading images (2006) are used to analyze the visual images in this study. The findings of the study show that despite the empowering nature of female superheroes, the unequal power relations between male and female characters are established linguistically and visually, which further sustains and reinforces the racial and patriarchal gender ideologies in the selected comics. Moreover, it is recommended that the female representations in the feminist themes of empowerment with respect to the Pakistani female superheroes should also be explored for further research.

Keywords: feminist critical discourse analysis, patriarchal gender ideology, power relations, superhero comics

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Authors: Jaeyoung Lee, Minji Je

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Objectives: The adolescents not attending school are named out-of-school adolescents. They are more vulnerable to health management and are likely to be exposed to a number of risk factors. This study was conducted to investigate the problem behavior of out-of-school adolescents and analyze the difference caused by gender. Methods: In this study, the problem behaviors of out-of-school adolescents, the vulnerable class, were defined in 8 types and based on this definition, the survey on run away from home, drop out, prostitution, violence, internet game addiction, theft, drug addiction, and smoking was conducted. The study was conducted in a total of 507 out-of-school adolescents, including 342 males, and 165 females. The type, frequency and start time of the 8 problem behaviors were identified. The collected data were analyzed with chi-square test and t-test using SPSS statistics 22. Results: Among the problem behaviors of the subjects, violence ( =17.41, p < .001), internet game addiction ( =16.14, p < .001), theft ( =22.48, p < .001), drug addiction ( =4.17, p=.041), and smoking ( =3.90, p=.048) were more significantly high in male out-of-school adolescents than female out-of-school adolescents. In addition, the frequency of the problem behavior was higher in male out-of-school adolescents with statistical significance than in female out-of-school adolescents (t=5.08, p= < .001). In terms of the start time of the problem behavior, only internet game addiction was higher in male out-of-school adolescents with the statistical significance than in female out-of-school adolescents ( =6.22, p=.032). No statistically significant difference was found in other problem behaviors (p > .05). Conclusions: In this study, it was found that gender difference in problem behaviors of out-of-school adolescents exists, and its frequency and difference of types were identified. When the social countermeasures were provided for those adolescents, a distinguished approach is required depending on the patterns of problem behavior and gender. When preparing policy alternatives and interventions for out-of-school adolescents, it is required to reflect the results of this study.

Keywords: addictive behavior, adolescent, gender, problem behavior

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Authors: Rachael Olatoun Okunuga

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Developing young people’s employability is a key policy issue for ensuring their successful transition to the labour market and their access to career oriented employment. The youths of today irrespective of their gender need to acquire the knowledge, skills and attitudes that will enable them to create or find jobs as well as cope with unpredictable labour market changes throughout their working lives. In a study carried out to determine the influence of gender on job-competencies requirements of chemical-based industries and undergraduate-competencies acquisition by chemists working in the industries, all chemistry graduates working in twenty (20) chemical-based industries that were randomly selected from six sectors of chemical-based industries in Lagos and Ogun States of Nigeria were administered with Job-competencies required and undergraduate-competencies acquired assessment questionnaire. The data were analysed using means and independent sample t-test. The findings revealed that the population of female chemists working in chemical-based industries is low compared with the number of male chemists; furthermore, job-competencies requirements are found not to be gender sensitive while there is no significant difference in undergraduate-competencies acquisition of male and female chemists. This suggests that females should be given the same opportunity of employment in chemical-based industries as their male counterparts. The study also revealed the level of acquisition of undergraduate competencies as related to the needs of chemical-based industries.

Keywords: knowledge, skill, attitude, acquired, required, employability

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Authors: Chia-Chun Wu, Pei-Shan Lee

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Gender equality is an important issue in the workplace today. This study aimed to explore whether female naval with agreeableness personality traits can increase bystander attitudes towards sexual harassment by reducing sexism. A total of 281 female navalin Taiwan participated in this study and completed the BFI-10 scale and questionnaires on sexism and bystander attitudes towards sexual harassment. Path analysis was performed using AMOS 23 version. The results demonstrated that female naval with an agreeableness personality predicted bystander attitudes towards sexual harassment, and when sexism was reduced, it was more helpful to increase bystander attitudes toward sexual harassment. These results informed the perspectives of female naval. It is suggested that when promoting gender equality in the military in the future, people with agreeableness personality can be selected to attend gender equality courses to improve bystander attitudes towards sexual harassment. This provided the Navy with strategies to reduce the probability of sexual harassment.

Keywords: semism, agreeableness, female, bystander attitude

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Authors: Liz van Acker, Linda Colley

Abstract:

Political processes are shaped by the gendered culture of parliaments. This paper examines how the institution of parliament has been affected by the changing number of women in politics. In order to understand how and why gender change occurs, the paper employs a feminist historical institutionalism approach. It argues that while it is difficult to change the gendered nature of political institutions, it is possible, from a gender perspective, to understand the processes of change both formally and informally. Increasing women’s representation has been a slow process which has not occurred without political struggles. A broadly defined ‘feminist historical institutionalism’ has critiqued existing approaches to institutions and combined historical institutional analysis with tools of gender to enhance our understanding of institutional processes and change. The paper examines the gendered rules, norms, and practices that influence institutional design choices and processes. Institutions such as Parliament often are able to adjust to women’s entry and absorb them without too much interruption. Exploring the hidden aspects to informal institutions involves identifying unspoken and accepted norms that may guide decision-making – exposing and questioning the gender status quo. This paper examines the representation of women in the Queensland Parliament, Australia. It places the Queensland experience in historical context, as well as in the national and international context. The study is interesting, given that its gender representation has rocketed from one of the worst performing states in 2012 to one of the best performing in 2015 with further improvements in 2017. The state currently has a re-elected female Premier, a female Deputy Premier and a female-dominated cabinet – in fact, Queensland was the first ministry in Australia to have a majority of women in its Cabinet. However, it is unnecessary to dig far below these headlines to see that this is uncharacteristic of its history: progress towards this current position has been slow and patchy. The paper finds that matters such as the glass ceiling and the use of quotas explain women’s recent success in Queensland politics.

Keywords: feminist historical institutional approach, glass ceiling, quotas, women’s participation in politics

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

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Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet

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Authors: A. Deswal, N. S. Deora, H. N. Mishra

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The aim of the present study was to develop a rapid method for electronic nose for online quality control of oat milk. Analysis by electronic nose and bacteriological measurements were performed to analyse spoilage kinetics of oat milk samples stored at room temperature and refrigerated conditions for up to 15 days. Principal component analysis (PCA), discriminant factorial analysis (DFA) and soft independent modelling by class analogy (SIMCA) classification techniques were used to differentiate the samples of oat milk at different days. The total plate count (bacteriological method) was selected as the reference method to consistently train the electronic nose system. The e-nose was able to differentiate between the oat milk samples of varying microbial load. The results obtained by the bacteria total viable counts showed that the shelf-life of oat milk stored at room temperature and refrigerated conditions were 20 hours and 13 days, respectively. The models built classified oat milk samples based on the total microbial population into “unspoiled” and “spoiled”.

Keywords: electronic-nose, bacteriological, shelf-life, classification

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Authors: Lu Anne A. Godinez, May Claudine I. Gador, Preacious G. Gumolon, Louiechi Von R. Mendoza, Neil Bryan N. Moninio

Abstract:

Gender equality and women empowerment is the third of eight Millennium Development Goals. Understanding corruption’s linkages to gender equality issues and how it impacts women’s empowerment is part of the broader process of advancing women’s rights and understanding the gender dimensions of democratic governance. Taking a long view of political (corruption index) and the social (women empowerment) dimension — a view from 2015 to 2030, a context based forecast was conducted to forecast the ASEAN corruption index in the next 15 years, answering the question: “Does women political involvement decrease corruption rate index of ASEAN countries in the next 15 years?” The study have established that there will be an increase women political involvement in the ASEAN countries in the next 15 years that will cause a drop on corruption rate index. There will be a significant decline on corruption rate index in 2030. This change entails reform not only in the political aspect of progress, but to the social aspect as well. Finally, the political aspect is increasing at a constant rate however a double or triple increase of the social aspect is seen to be the key solution for corruption.

Keywords: women, women political involvement, corruption, gender equity index, economic participation, educational attainment, political empowerment, control of corruption, regulatory quality, rule of law, voice and accountability government effectiveness, political stability and corruption perception index

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Authors: Babar Khan, Wang Zhijie

Abstract:

Machine Vision has been playing a significant role in Industrial Automation, to imitate the wide variety of human functions, providing improved safety, reduced labour cost, the elimination of human error and/or subjective judgments, and the creation of timely statistical product data. Despite the intensive research, there have not been any attempts to classify fabric prints based on printed texture and colour, most of the researches so far encompasses only black and white or grey scale images. We proposed a biologically inspired processing architecture to classify fabrics w.r.t. the fabric print texture and colour. We created a texture descriptor based on the HMAX model for machine vision, and incorporated colour descriptor based on opponent colour channels simulating the single opponent and double opponent neuronal function of the brain. We found that our algorithm not only outperformed the original HMAX algorithm on classification of fabric print texture and colour, but we also achieved a recognition accuracy of 85-100% on different colour and different texture fabric.

Keywords: automatic classification, texture descriptor, colour descriptor, opponent colour channel

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Authors: Reza Shakoori

Abstract:

This paper presents a structural correction of Arabic and Persian strokes using manipulation of their chain codes in order to improve the rate and performance of Persian and Arabic handwritten word recognition systems. It collects pure and effective features to represent a character with one consolidated feature vector and reduces variations in order to decrease the number of training samples and increase the chance of successful classification. Our results also show that how the proposed approaches can simplify classification and consequently recognition by reducing variations and possible noises on the chain code by keeping orientation of characters and their backbone structures.

Keywords: Arabic, chain code normalization, OCR systems, image processing

Procedia PDF Downloads 404