Search results for: gender prediction

Authors: Sophie N. Selby-Pham, Yudie Wang, Louise Bennett

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Consumption of grapes promotes health and reduces the risk of chronic diseases due to the action of grape phytochemicals in regulation of Oxidative Stress and Inflammation (OSI). The bioefficacy of phytochemicals depends on their absorption in the human body. The time required for phytochemicals to achieve maximal plasma concentration (Tₘₐₓ) after oral intake reflects the time window of maximal bioefficacy of phytochemicals, with Tₘₐₓ dependent on physicochemical properties of phytochemicals. This research collated physicochemical properties of grape phytochemicals from white and red grapes to predict their Tₘₐₓ using pharmacokinetic modelling. The predicted values of Tₘₐₓ were then compared to the measured Tₘₐₓ collected from clinical studies to determine the accuracy of prediction. In both liquid and solid intake forms, white grapes exhibit a shorter Tₘₐₓ range (0.5-2.5 h) versus red grapes (1.5-5h). The prediction accuracy of Tₘₐₓ for grape phytochemicals was 33.3% total error of prediction compared to the mean, indicating high prediction accuracy. Pharmacokinetic modelling allows prediction of Tₘₐₓ without costly clinical trials, informing dosing frequency for sustained presence of phytochemicals in the body to optimize the health benefits of phytochemicals.

Keywords: absorption kinetics, phytochemical, phytochemical absorption prediction model, Vitis vinifera

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Authors: Mohammed Murtuza Soofi

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Purpose: Marketers and Researchers alike have simultaneously, yet in mutually exclusive instances, promote the use of humour by brands in their communication and gendering of brands, as both enhance brand equity and can generate positive attitudinal responses from customers. However, the gendering of brands comes with associated gendered stereotypical expectations. The current paper consolidates the long standing literature on gender role/stereotype theory and brand gender theories establishing a theoretical framework for understanding how gender-based stereotypes about humour can influence consumers’ attitudinal responses towards brands. Design/methodology/approach: Using parallel constrain satisfaction theory as domain theory to explain the highhandedness of stereotypes and gender stereotype theories (particularly around feminine use of humour), we explain why gender based stereotypes could constrain brand behaviors, and in turn, feminine brands get penalised for using witty, aggressive and self-enhancing humor. Findings: Extension of gender stereotypes to anthropomorphised brands will lead consumers to judge the use of negative humour by a feminine brand as less appropriate, which will trigger the causal chain of reduced sense of communal appropriateness and brand warmth which will result in a negative attitude towards the brand. Originality/value: Brand gendering being susceptible to gender based stereotypes, has very little attention in the literature and hence use of negative humour (stereotypical male behaviour), has never been studied in the context of gendered brands. It also helps understand to what extent stereotypes will impact attitudinal responses to the brand. Our work can help understand when heavily gendered brands can optimise the use of humour and when they can avoid it.

Keywords: brand femininity, brand gender, gender stereotypes, humour

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Authors: Cedric Leong, Parth Desai, Parth Patel

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The purpose of this project was to develop a neural network based on qualitative team data to predict alliance scores to determine winners of matches in the FIRST Robotics Competition (FRC). The game for the competition changes every year with different objectives and game objects, however the idea was to create a prediction system which can be reused year by year using some of the statistics that are constant through different games, making our system adaptable to future games as well. Aerial Assist is the FRC game for 2014, and is played in alliances of 3 teams going against one another, namely the Red and Blue alliances. This application takes any 6 teams paired into 2 alliances of 3 teams and generates the prediction for the final score between them.

Keywords: artifical neural network, prediction system, qualitative team data, FIRST Robotics Competition (FRC)

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Authors: Khalaf Khatatneh, Nabeel Al-Milli, Amjad Hudaib, Monther Ali Tarawneh

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Software fault prediction identify potential faults in software modules during the development process. In this paper, we present a novel approach for software fault prediction by combining a feedforward neural network with particle swarm optimization (PSO). The PSO algorithm is employed as a feature selection technique to identify the most relevant metrics as inputs to the neural network. Which enhances the quality of feature selection and subsequently improves the performance of the neural network model. Through comprehensive experiments on software fault prediction datasets, the proposed hybrid approach achieves better results, outperforming traditional classification methods. The integration of PSO-based feature selection with the neural network enables the identification of critical metrics that provide more accurate fault prediction. Results shows the effectiveness of the proposed approach and its potential for reducing development costs and effort by detecting faults early in the software development lifecycle. Further research and validation on diverse datasets will help solidify the practical applicability of the new approach in real-world software engineering scenarios.

Keywords: feature selection, neural network, particle swarm optimization, software fault prediction

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Authors: Mamita Panda

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Popular shows are considered to be powerful medium for bringing social change and development. It has the responsibility for not only entertaining, but spreading awareness among common mass which it results social intervention in the major social institutions. Gender construction in one of these social institutions where one can build their capacity to construct a better human society. Mass media in general, TV in particular has an important intervening factor in responding to these processes. The obligatory role of media not only through news but popular shows (serials) becomes compulsion for social formation including construction through gender. This paper attempts to map and examine the gendered contents from serials including viewer’s response to understand the level of influence. The regression analysis shows that socio-economic factors have wider influence on understanding of gender equality including TV popular contents. The social construction of gender through serials remains a serious debatable issue and concern thereafter.

Keywords: construction, empowerment, gender, media and women

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Authors: Ajayi Olusola Olajide, Alonge Olaide Moses

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Predicting the outcome of soccer matches poses an interesting challenge for which it is realistically impossible to successfully do so for every match. Despite this, there are lots of resources that are being expended on the correct prediction of soccer matches weekly, and all over the world. Soccer Match Result Prediction System Model (SMRPSM) is a system that is proposed whereby the results of matches between two soccer teams are auto-generated, with the added excitement of giving users a chance to test their predictive abilities. Soccer teams from different league football are loaded by the application, with each team’s corresponding manager and other information like team location, team logo and nickname. The user is also allowed to interact with the system by selecting the match to be predicted and viewing of the results of completed matches after registering/logging in.

Keywords: predicting, soccer match, outcome, soccer, matches, result prediction, system, model

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Authors: J. W. Kim, Y. R. Kwon, Y. J. Ho, H. M. Jeon, G. M. Eom

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The aim of this study was to investigate the association of anthropometry with static balance in the elderly and their possible gender difference. Forty six subjects (23 men and 23 women) participated in this study. COP (Center of Pressure) was measured on a force-platform during quiet feet-together standing. As outcome measures, mean distance were derived from the COP. Weight was significantly correlated with postural variable only in the elderly men. This result suggests that the gender should be considered when normalizing postural variables.

Keywords: body characteristics, postural balance, elderly, gender difference

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Authors: Martha Pastorek Gripson, Anna Lindqvist

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The research project points at, and discusses, strategies, problems and possibilities when preschool teachers describe their work with dance in two Swedish preschools. The use of dance itself is a strategy for a more inclusive preschool practice and the use of so-called “dance skirts” is regarded as central for facilitating both dance qualities and to promote gender equality. The research is carried out in an action research project, involving two preschools with specific focus on gender equality work. The result problematizes the use of so-called “dance skirts”, as those can be both a tool for appreciation of aesthetics associated with femininity but at the same time create dance mainly as ballet related activity.

Keywords: dance, body, education, preschool, gender

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Authors: Shital Suresh Borse, Vijayalaxmi Kadroli

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E-commerce industries nowadays implement the latest AI, ML Techniques to improve their own performance and prediction accuracy. This helps to gain a huge profit from the online market. Ant Colony Optimization, Genetic algorithm, Particle Swarm Optimization, Neural Network & GWO help many e-commerce industries for up-gradation of their predictive performance. These algorithms are providing optimum results in various applications, such as stock price prediction, prediction of drug-target interaction & user ratings of similar products in e-commerce sites, etc. In this study, customer reviews will play an important role in prediction analysis. People showing much interest in buying a lot of services& products suggested by other customers. This ultimately increases net profit. In this work, a convolution neural network (CNN) is proposed which further is useful to optimize the prediction accuracy of an e-commerce website. This method shows that CNN is used to optimize hyperparameters of GWO algorithm using an appropriate coding scheme. Accurate model results are verified by comparing them to PSO results whose hyperparameters have been optimized by CNN in Amazon's customer review dataset. Here, experimental outcome proves that this proposed system using the GWO algorithm achieves superior execution in terms of accuracy, precision, recovery, etc. in prediction analysis compared to the existing systems.

Keywords: prediction analysis, e-commerce, machine learning, grey wolf optimization, particle swarm optimization, CNN

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Authors: Khodijah Ismail, Elfindri

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Tourism development have many environmental, economically and sociocultural benefits. Small islands have a lot of potential for marine tourism development. But, stereotype gender issues still dominate the social and cultural life of rural communities that have an impact on the gap in benefits of local development. The purpose of this study is to found development strategy concept of marine tourism in small islands gender-based. This study found in the marine tourism development of small islands not involved women, from planning to monitor marine tourism development in small islands. It's affects to the low of socio-economic of women in the coastal village and small islands. This condition is not advantage for sustainable development of marine tourism in small islands. Therefore, strengthening of livelihood assets by gender based through the marine tourism development in small islands is very important to attention, that women can contributed to household welfare, bargaining positioned in social culture was better and increase broad access to local government development policies. To realize it requires the full support of the government and relevant stakeholders through gender empowerment and strengthening of accessibility, connectivity, regulation, and design institution.

Keywords: gender inequality, marine tourism, development, tourism management

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Authors: Qian Wang

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In recent years, a growing number of Chinese women choose to pursue Ph.D. education. Except for the male and female, women with PhD degrees are stigmatized as the ‘third gender’ in Chinese society. People, especially most men, believe that female PhD students challenge the traditional image and gender role of Chinese women. This gender stereotype causes a range of difficulties in finding partners in marriage market for Chinese female PhD students. In this study, the author conducted in-depth interviews with 15 participants who are currently doing their PhD studies in Chinese universities to explore their perceptions of being leftover women on the basis of their experience. All the participants are single. Based on the analysis of qualitative data, this study found that the ‘leftover women’ phenomenon among Chinese female PhD students is the result of the contradictions generated between Chinese patriarchal society and them. Although Chinese female PhD students is an attention-attracting group, the studies about them are very limited in China. This study could not only contribute to the understanding of the ‘third gender’ phenomenon and the ‘leftover women’ studies in China, but also, in practical level, could give some guidance for governments to resolve the social problems of female PhD students.

Keywords: Chinese female Ph.D. students, the ‘leftover women’, the Chinese patriarchal society, gender role, Chinese culture

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Authors: Priscilla Adoley Moffat

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One common element of African cultures is gender role socialization. This is a significant component of African cultures because gender roles are considered in these cultures, to define males and females and distinguish males from females. Various studies have established the impact of gender role socialization on individuals, on activities of individuals, including business activities, and on society, in general. This study further examined the effect of gender role socialization on the marketing of gendered products. The study sought to establish whether gender role socialization affects marketing, particularly word-of-mouth marketing, of gender-specific products. For a comprehensive examination of the influence of gender role socialization on word-of-mouth marketing of gendered products, 2150 respondents (1075 males and 1075 females), comprising 550 students of Marketing from various Ghanaian universities/colleges and 1600 other individuals (100 from each of the 16 regions of Ghana, representing the various cultures) were randomly sampled and interviewed. The study found that females are more willing to market male products than males when tasked to market female products. Also, females are more efficient in marketing male products than males in marketing female products. Again, most female audiences feel uncomfortable or embarrassed and are less receptive when approached by a male marketer of female products. Then, the study found that the fear of stigmatization is a major influencer of males’ negative attitude towards marketing of female products and that female marketers of male products, however, suffer less or no stigma. Aside from its addition to the literature on the impact of gender role socialization on marketing and, for that matter, the influence of socialization on marketing, the findings of the study are useful to multinational companies, which become better informed in their strategy when assigning marketing roles, especially in Africa.

Keywords: gender, socialization, marketing, gendered, role, Ghana

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Authors: C. Manjula, Lilly Florence

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Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: decision tree, genetic algorithm, machine learning, software defect prediction

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Authors: Alka Obadić

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The research concentrates on the role of tourism in generating female employment and on impact of gender discrimination in tourism sector. Unfortunately, in many countries there are still some barriers to the inclusion of women at all hierarchical levels of tourism labor market. Research analysis focuses on EU countries where tourism is a main employer of women. The analysis shows that women represent over third persons employed in the non-financial business economy and almost two thirds in core tourism activities. Women's gross hourly earnings in accommodation and food services were below those of men in the European Union and only countries who recorded increase of gender pay gap from the beginning of crisis are Bulgaria and Croatia. Women in tourism industry are still overrepresented in lower status jobs with fewer opportunities for career progression and are often treated unequally.

Keywords: employment, gender discrimination, tourism, women’s participation

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Authors: Rodrigo Aguiar, Adelino Ferreira

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Road traffic accidents are the leading cause of unnatural death and injuries worldwide, representing a significant problem of road safety. In this context, the use of artificial intelligence with advanced machine learning techniques has gained prominence as a promising approach to predict traffic accidents. This article investigates the application of machine learning algorithms to develop traffic accident frequency prediction models. Models are evaluated based on performance metrics, making it possible to do a comparative analysis with traditional prediction approaches. The results suggest that machine learning can provide a powerful tool for accident prediction, which will contribute to making more informed decisions regarding road safety.

Keywords: machine learning, artificial intelligence, frequency of accidents, road safety

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Authors: Tatia Kekelia

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This paper is part of an ongoing research addressing gender discrimination in the Court of Georgia. The research suggests that gender stereotypes influence the processes at the Court in contemporary Georgia, which causes uneven fights for women and men, not to mention other gender identities. The sub-hypothesis proposes that the gender stereotypes derive from feudal representations, which persisted during the Soviet rule. It is precisely those stereotypes that feed gender-based discrimination today. However, this paper’s main focus is on the main hypothesis, describing the revealed stereotypes, and identifying the Court as a place where their presence is most hindering societal development. First of all, this happens by demotivating people, causing loss of trust in the Court, and therefore potentially encouraging crime. Secondly, it becomes harder to adequately mobilize human resources, since more than a half of the population is female, and under the influence of rigid or more subtle forms of discrimination, they lose not only equal rights, but also the motivation to work or fight for them. Consequently, this paper falls under democracy studies as well – considering that an unbiased Court is one of the most important criteria for assessing the democratic character of a state. As the research crosses the disciplines of sociology, law, and history, a complex of qualitative research methods is applied, among which this paper relies mainly on expert interviews, interviews with attorneys, and desk research. By showcasing and undermining the gender stereotypes that work at the Court of Georgia, this research might assist in rising trust towards it in the long-term. As for the broader relevance, the study of the Georgian case opens the possibility to conduct comparative analyses in the region and the continent, and, presumably, carve the lines of cultural influences.

Keywords: gender, stereotypes, bias, democratization, judiciary

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Authors: Vephkhvia Grigalashvili

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The defense of Georgia is every citizen’s duty. The present article reviews the principles and standards of gender policy in the Georgian national defense sector. In addition, it looks at mechanisms for ensuring gender equality, going through the relevant Georgian legislation. Furthermore, this work aims to conduct a comparative analysis of defense models of Georgia, Finland, and the Baltic States in order to identify core institutional challenges. The study produced the following findings:(a) The national defense planning is based on the Total Defense approach, which implies a wide involvement of the country`s population in state defense. (b) This political act does not specify gender equality aspects of the Total Defense strategy; (c) According to the Constitution of Georgia, irrespective of gender factors, every citizen of Georgia is legally obliged to participate in state security activities. However, the state has an authority (power of choice) to decide which gender group (male or/and female citizen) must fulfill above mentioned their constitutional commitment. For instance, completion of compulsory military and reserve military services is a male citizen’s duty, whereas professional military service is equally accessible to both genders. The study concludes that effective implementation of the Total Defense concept largely depends on how Georgia uses its capabilities and human resources. Based on the statistical fact that more than 50% of the country’s population are women, Georgia has to elaborate on relevant institutional mechanisms for implementation of gender equality in the national defense organization. In this regard, it would be advisable: (i) to give the legal opportunity to women to serve in compulsory military service, and (ii) to develop labor reserve service as a part of the anti-crisis management system of Georgia.

Keywords: gender in defense organisation, gender mechanisms, gender in defense policy, gender policy

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Authors: Aaminah Hassan

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Advertisements carry a great potential to influence our lives because they are crafted to meet particular ends. Stereotypical representation in advertisements is capable of forming unconscious attitudes among people towards any gender and their abilities. This study focuses on gender representation in Pakistani prime time advertisements. For this purpose, 13 advertisements were selected from three different categories of foods and beverages, cosmetics, cell phones and cellular networks from the prime time slots of one of the leading Pakistani entertainment channel, ‘Urdu 1’. Both quantitative and qualitative analyses are carried out for range of variables like gender, age, roles, activities, setting, appearance and voice overs. The results revealed that gender representation in advertisements is stereotypical. Moreover, in few instances, the portrayal of women is not only culturally inappropriate but is demeaning to the image of women as well. Their bodily charm is used to promote products. Comparing different entertainment channels for their prime time advertisements and broadening the scope of this research will yield greater implications for the researchers who want to carry out the similar research. It is hoped that the current study would help in the promotion of media literacy among the viewers and media authorities in Pakistan.

Keywords: Advertisements, Content Analysis, Gender, Prime time

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Authors: Kehinde Emmanuel Atanlusi

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It is impossible to stifle the gust of social change as it makes its way through institutionalised hierarchies on its way to expressing itself. This advancement might also have repercussions for institutions, families, and politics, so modifying the norms and establishing new societal ideals. In the following paragraphs, it will explore how gender roles in the family have changed over time, how this has affected consumption, and how marketing has been influenced by these changes. It was decided to use the empirical research method, which led to several discoveries, one of which was that marketing in the pre-modern era was predicated on metanarratives and gender stereotypes. However, these aspects of marketing have undergone significant transformations in the post-modern era, which led to the formation of an assumption regarding what future marketing trends will be like. In spite of the fact that post-modern marketing methods have a number of drawbacks, it was suggested that these strategies be embraced and updated in the future in order to expand consumer bases and target audiences.

Keywords: Marketing, Gender Roles, Advertising, Decentralisation, Fragmentation

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Authors: Asmir Gogic, Aljo Mujcic, Sandra Ibric, Nermin Suljanovic

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Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature.

Keywords: bluetooth low energy, disaster prediction, mesh routing protocols, wireless sensor networks

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Authors: Desiree Llaguno Cerezo, Elizabeth Valdes-Miranda Fernandez

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Women have a critical role in sustaining the economy and in the development of trade. However, such a role has long been invisible due to orthodox conceptions that have ignored the gender variable in commercial analyses. Today, it is generally accepted that neither the economy nor business are gender-neutral and that the performance of these activities often impact negatively the lives of women. Women’s participation in trade, on equal terms as men, in any of the various possible roles -producer, wage earner, consumer, merchant, taxpayer- will not only favour the lives of women but also the performance of the economies in which they participate. Transparency, as a principle of the multilateral trading system, can play a significant role as a strategy for the empowerment of women.

Keywords: trade, human rights, gender equality, transparency, WTO, women workers, women's economic empowerment

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Authors: Rishmidevi Kirtie Algoe

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Advocating for gender equality in sports is a global struggle but a greater challenge for small nations with weak economies like Suriname, a Dutch-speaking country in the Caribbean. This paper presents the experience of the Suriname Olympic Committee (SOC) in addressing gender inequality in sports in the global context of the policies implemented by the International Olympic Committee (IOC). The case of Suriname is interesting because it shows the results of a small nation in creating protective measures. The SOC has succeeded in developing a code of conduct for sports and is now taking steps to establish a sports justice institute. All of this is happening in a situation where there are few women leaders in sport: only three of the seventeen national member federations are led by women, and there are two women on SOC's 9-member board. Three arguments are made. First, gender inequality in sports in Suriname is a reflection of national power structures and cultural barriers to women in sports. Second, IOC policies and resources to reduce gender inequality in sports, while important, do not guarantee national change. Third, and more importantly, the SOC has addressed gender inequality with an approach based on the principles of "walk the talk" and "trial and error." All three arguments are elaborated on using the framework of intersectionality. The study draws empirically on data collected during and on SOC Gender and Sport Commission seminars and workshops, including two surveys, transcripts, and newspaper articles.

Keywords: Caribbean, gender inequality, safeguarding, Suriname Olympic Committee

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Authors: Aqleem Fatimah

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The purpose of study was to find out the impact of teachers’ gender on students’ score achievement. Focusing on primary level’s teachers & students, a survey research was conducted by using convenient sampling technique. All the students of grade four (1500) and fifty-six teachers (equally divided by gender) from the 50 randomly selected coeducational schools from Lahore were taken as sample. The academic performance was operationalized using a t-test on standardized achievement tests of the students in language, science mathematics and social studies. In addition, all those gender based characteristics of teachers that count a lot in classroom interactions (taking Multi-grade classes, classroom strategies, feedback strategies and evaluation method) that influence students’ achievement were also analyzed by using a questionnaire and an observation schedule. The results of the study showed better academic achievement of students (girl &boy) of female teachers comparatively to the students of male teachers. Therefore, as the female teachers’ number lacks in Pakistan, the study suggests policy makers to seek guidelines to induct more specialized and professionally competent female teachers because their induction will prove highly beneficial for the betterment of students’ score achievement.

Keywords: gender, teacher, competency, score achievement

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Authors: Emad Amar, Tawfik Khattab, Fatma Zada

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Predicting earthquakes is an important issue in the study of geography. Accurate prediction of earthquakes can help people to take effective measures to minimize the loss of personal and economic damage, such as large casualties, destruction of buildings and broken of traffic, occurred within a few seconds. United States Geological Survey (USGS) science organization provides reliable scientific information of Earthquake Existed throughout history & Preliminary database from the National Center Earthquake Information (NEIC) show some useful factors to predict an earthquake in a seismic area like Aleutian Arc in the U.S. state of Alaska. The main advantage of this prediction method that it does not require any assumption, it makes prediction according to the future evolution of object's time series. The article compares between simulation data result from trained BP and RBF neural network versus actual output result from the system calculations. Therefore, this article focuses on analysis of data relating to real earthquakes. Evaluation results show better accuracy and higher speed by using radial basis functions (RBF) neural network.

Keywords: BP neural network, prediction, RBF neural network, earthquake

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Authors: Adadu Yahaya, Abdullahi Erunke Canice

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The study sets out to examine the interface that tends to exist in the relationship between gender policy and Nigeria’s socio-economic development. Despite Nigeria’s ratification of virtually all international instruments on the protection and promotion of gender rights and equality, it appears that the practice is honored in the breach than in observance; hence, these policies have not been adequately domesticated and implemented. The implication of this is that the women folks have generally been isolated from mainstream politics and their political rights and privileges truncated in the scheme of things. The paper observes that gender inequality and marginalization in Nigeria has practically occasioned the unwholesome subjugation of Nigerian women to the background, hence poses more critical questions and challenges to the national question. The consequence of this, to this paper, is that Nigeria’s development process will be adversely affected if this trend is not checked. The paper sums up with appropriate policy options which are believed to have the potentials of giving women the right pride of place in the socio-economic and political dynamics in the 21st century Nigeria and beyond.

Keywords: development, equality, gender, policy

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Authors: Azzedine Hamza, Chouaib Chakour, Messaoud Ramdani

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Energy demand prediction plays a crucial role in achieving next-generation power systems for agricultural greenhouses. As a result, high prediction quality is required for efficient smart grid management and therefore low-cost energy consumption. The aim of this paper is to investigate the effectiveness of a hybrid data-driven model in day-ahead energy demand prediction. The proposed model consists of Discrete Wavelet Transform (DWT), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The DWT is employed to decompose the original signal in a set of subseries and then an ANFIS is used to generate the forecast for each subseries. The proposed hybrid method (DWT-ANFIS) was evaluated using a greenhouse energy demand data for a week and compared with ANFIS. The performances of the different models were evaluated by comparing the corresponding values of Mean Absolute Percentage Error (MAPE). It was demonstrated that discret wavelet transform can improve agricultural greenhouse energy demand modeling.

Keywords: wavelet transform, ANFIS, energy consumption prediction, greenhouse

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Authors: Alexander Scott English, Yilin Ma, Xiaoying Liu

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The widespread use of artificial intelligence in everyday life has triggered a fervent discussion covering a wide range of areas. However, to date, research on the influence of gender in various segments and factors from a social science perspective is still limited. This study aims to explore whether there are gender differences in human trust in AI for its application in basic everyday life and correlates with human perceived similarity, perceived emotions (including competence and warmth), and attractiveness. We conducted a study involving 321 participants using a two-subject experimental design with a two-factor (masculinized vs. feminized voice of the AI) multiplied by a two-factor (pitch level of the AI's voice) between-subject experimental design. Four contexts were created for the study and randomly assigned. The results of the study showed significant gender differences in perceived similarity, trust, and perceived emotion of the AIs, with females rating them significantly higher than males. Trust was higher in relation to AIs presenting the same gender (e.g., human female to female AI, human male to male AI). Mediation modeling tests indicated that emotion perception and similarity played a sufficiently mediating role in trust. Notably, although trust in AIs was strongly correlated with human gender, there was no significant effect on the gender of the AI. In addition, the study discusses the effects of subjects' age, job search experience, and job type on the findings.

Keywords: artificial intelligence, gender differences, human-robot trust, mediation modeling

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Authors: Yiannis G. Smirlis

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The classification and the prediction of efficiencies in Data Envelopment Analysis (DEA) is an important issue, especially in large scale problems or when new units frequently enter the under-assessment set. In this paper, we contribute to the subject by proposing a grid structure based on interval segmentations of the range of values for the inputs and outputs. Such intervals combined, define hyper-rectangles that partition the space of the problem. This structure, exploited by Interval DEA models and a dominance relation, acts as a DEA pre-processor, enabling the classification and prediction of efficiency scores, without applying any DEA models.

Keywords: data envelopment analysis, interval DEA, efficiency classification, efficiency prediction

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Authors: Jamerson Felipe Pereira Lima, Jeane Cecília Bezerra de Melo

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The difficulty and cost related to obtaining of protein tertiary structure information through experimental methods, such as X-ray crystallography or NMR spectroscopy, helped raising the development of computational methods to do so. An approach used in these last is prediction of tridimensional structure based in the residue chain, however, this has been proved an NP-hard problem, due to the complexity of this process, explained by the Levinthal paradox. An alternative solution is the prediction of intermediary structures, such as the secondary structure of the protein. Artificial Intelligence methods, such as Bayesian statistics, artificial neural networks (ANN), support vector machines (SVM), among others, were used to predict protein secondary structure. Due to its good results, artificial neural networks have been used as a standard method to predict protein secondary structure. Recent published methods that use this technique, in general, achieved a Q3 accuracy between 75% and 83%, whereas the theoretical accuracy limit for protein prediction is 88%. Alternatively, to achieve better results, support vector machines prediction methods have been developed. The statistical evaluation of methods that use different AI techniques, such as ANNs and SVMs, for example, is not a trivial problem, since different training sets, validation techniques, as well as other variables can influence the behavior of a prediction method. In this study, we propose a prediction method based on artificial neural networks, which is then compared with a selected SVM method. The chosen SVM protein secondary structure prediction method is the one proposed by Huang in his work Extracting Physico chemical Features to Predict Protein Secondary Structure (2013). The developed ANN method has the same training and testing process that was used by Huang to validate his method, which comprises the use of the CB513 protein data set and three-fold cross-validation, so that the comparative analysis of the results can be made comparing directly the statistical results of each method.

Keywords: artificial neural networks, protein secondary structure, protein structure prediction, support vector machines

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Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami

Abstract:

Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.

Keywords: ANFIS, MGT, prediction modeling, rail track degradation

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