Search results for: emotion mining

Authors: Javier Donoso-Bravo, Camila Cortés-Zambrano

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In this paper, we analyze socioterritorial inequalities in the region of Valparaiso (Chile) using secondary data to account for these inequalities drawing on economic, social, educational, and environmental dimensions regarding the thirty-six municipalities of the region. We looked over a wide-ranging set of secondary data from public sources regarding economic activities, poverty, employment, income, years of education, post-secondary education access, green areas, access to potable water, and others. We found sharp socioterritorial inequalities especially based on the economic performance in each territory. Analysis show, on the one hand, the existence of a dual and unorganized development model in some territories with a strong economic activity -especially in the areas of finance, real estate, mining, and vineyards- but, at the same time, with poor social indicators. On the other hand, most of the territories show a dispersed model with very little dynamic economic activities and very poor social development. Finally, we discuss how socioterritorial inequalities in the region of Valparaiso reflect the level of globalization of the economic activities carried on in every territory.

Keywords: socioterritorial inequalities, development model, Chile, secondary data, Region of Valparaiso

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Authors: Thomas P. Roper, Brad Forbes, Jurij Karlovšek

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Shear displacement along bedding defects is a well-recognised behaviour when tunnelling and mining in stratified rock. This deformation can affect the durability and integrity of installed rock bolts. In-situ monitoring of rock bolt deformation under bedding shear cannot be accurately derived from traditional strain gauge bolts as sensors are too large and spaced too far apart to accurately assess concentrated displacement along discrete defects. A possible solution to this is the use of fiber optic technologies developed for precision monitoring. Distributed Optic Sensor (DOS) embedded rock bolts were installed in a tunnel project with the aim of measuring the bolt deformation profile under significant shear displacements. This technology successfully measured the 3D strain distribution along the bolts when subjected to bedding shear and resolved the axial and lateral strain constituents in order to determine the deformational geometry of the bolts. The results are compared well with the current visual method for monitoring shear displacement using borescope holes, considering this method as suitable.

Keywords: distributed optical strain sensing, rock bolt, bedding shear, sandstone tunnel

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Authors: Ulas Cinar, Omer Faruk Ugurlu, Selcuk Cebi

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Occupational safety risk management is the most important element of a safe working environment. Effective risk management can only be possible with accurate analysis and evaluations. Scoring-based risk assessment methods offer considerable ease of application as they convert linguistic expressions into numerical results. It can also be easily adapted to any field. Contrary to all these advantages, important problems in scoring-based methods are frequently discussed. Effective measurability is one of the most critical problems. Existing methods allow experts to choose a score equivalent to each parameter. Therefore, experts prefer the score of the most likely outcome for risk. However, all other possible consequences are neglected. Assessments of the existing methods express the most probable level of risk, not the real risk of the enterprises. In this study, it is aimed to develop a method that will present a more comprehensive evaluation compared to the existing methods by evaluating the probability and severity scores, all sub-parameters, and potential results, and a new scoring-based method is proposed in the literature.

Keywords: occupational health and safety, risk assessment, scoring based risk assessment method, underground mining, weighted risk scores

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Authors: Diego De Castro Rodrigues, Marcelo B. Nery, Sergio Adorno

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The project aims to develop a framework for social data analysis, particularly by mobilizing criminal records and applying descriptive computational techniques, such as associative algorithms and extraction of tree decision rules, among others. The methods and instruments discussed in this work will enable the discovery of patterns, providing a guided means to identify similarities between recurring situations in the social sphere using descriptive techniques and data visualization. The study area has been defined as the city of São Paulo, with the structuring of social data as the central idea, with a particular focus on the quality of the information. Given this, a set of tools will be validated, including the use of a database and tools for visualizing the results. Among the main deliverables related to products and the development of articles are the discoveries made during the research phase. The effectiveness and utility of the results will depend on studies involving real data, validated both by domain experts and by identifying and comparing the patterns found in this study with other phenomena described in the literature. The intention is to contribute to evidence-based understanding and decision-making in the social field.

Keywords: social data analysis, criminal records, computational techniques, data mining, big data

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Authors: Ritu Vashistha, Jitender Kumar

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Clustering using the K-means algorithm is a very common way to understand and analyze the obtained output data. When a similar object is grouped, this is called the basis of Clustering. There is K number of objects and C number of cluster in to single cluster in which k is always supposed to be less than C having each cluster to be its own centroid but the major problem is how is identify the cluster is correct based on the data. Formulation of the cluster is not a regular task for every tuple of row record or entity but it is done by an iterative process. Each and every record, tuple, entity is checked and examined and similarity dissimilarity is examined. So this iterative process seems to be very lengthy and unable to give optimal output for the cluster and time taken to find the cluster. To overcome the drawback challenge, we are proposing a formula to find the clusters at the run time, so this approach can give us optimal results. The proposed approach uses the Euclidian distance formula as well melanosis to find the minimum distance between slots as technically we called clusters and the same approach we have also applied to Ant Colony Optimization(ACO) algorithm, which results in the production of two and multi-dimensional matrix.

Keywords: ant colony optimization, data clustering, centroids, data mining, k-means

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Authors: Daniel J. Patricio Jiménez

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Digital reality demands new ways of thinking, flexibility in learning, acquisition of new competencies, visualizing reality under new approaches, generating open spaces, understanding dimensions in continuous change, etc. We need inclusive growth, where colors are not lacking, where lights do not give a distorted reality, where science is not half-truth. In carrying out this study, the documentary or bibliographic collection has been taken into account, providing a reflective and analytical analysis of current reality. In this context, deductive and inductive methods have been used on different multidisciplinary information sources. Women today and tomorrow are a strategic element in science and arts, which, under the umbrella of sustainability, implies ‘meeting current needs without detriment to future generations’. We must build new scenarios, which qualify ‘the feminine and the masculine’ as an inseparable whole, encouraging cooperative behavior; nothing is exclusive or excluding, and that is where true respect for diversity must be based. We are all part of an ecosystem, which we will make better as long as there is a real balance in terms of gender. It is the time of ‘the lifting of the veil’, in other words, it is the time to discover the pseudonyms, the women who painted, wrote, investigated, recorded advances, etc. However, the current reality demands much more; we must remove doors where they are not needed. Mass processing of data, big data, needs to incorporate algorithms under the perspective of ‘the feminine’. However, most STEM students (science, technology, engineering, and math) are men. Our way of doing science is biased, focused on honors and short-term results to the detriment of sustainability. Historically, the canons of beauty, the way of looking, of perceiving, of feeling, depended on the circumstances and interests of each moment, and women had no voice in this. Parallel to science, there is an under-representation of women in the arts, but not so much in the universities, but when we look at galleries, museums, art dealers, etc., colours impoverish the gaze and once again highlight the gender gap and the silence of the feminine. Art registers sensations by divining the future, science will turn them into reality. The uniqueness of the so-called new normality requires women to be protagonists both in new forms of emotion and thought, and in the experimentation and development of new models. This will result in women playing a decisive role in the so-called "5.0 society" or, in other words, in a more sustainable, more humane world.

Keywords: art, digitalization, gender, science

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Authors: J. Burlakovs, Y. Jani, L. Grinberga, M. Kriipsalu, O. Anne, I. Grinfelde, W. Hogland

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The worldwide future sustainable economies are oriented towards the sea: the maritime economy is becoming one of the strongest driving forces in many regions as population growth is the highest in coastal areas. For hundreds of years sea resources were depleted unsustainably by fishing, mining, transportation, tourism, and waste. European Sustainable Development Strategy is identifying and developing actions to enable the EU to achieve a continuous, long-term improvement of the quality of life through the creation of sustainable communities. The aim of this paper is to provide insight in Baltic Sea Region case studies on implemented actions on tourism industry waste and beach wrack management in coastal areas, hazardous contaminants and plastic flow treatment from waste, wastewaters and stormwaters. These projects mentioned in study promote successful prevention of contaminant flows to the sea environments and provide perspectives for creation of valuable new products from residuals for future circular economy are the step forward to green innovation winning streak.

Keywords: resilience, hazardous waste, phytoremediation, water management, circular economy

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Authors: Seo-Hee Kim, Kee-Won Kim, Seung-Hoon Kim

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The most important elements of a novel are the characters, events and background. The background represents the time, place and situation that character appears, and conveys event and atmosphere more realistically. If readers have the proper knowledge about background of novels, it may be helpful for understanding the atmosphere of a novel and choosing a novel that readers want to read. In this paper, we are targeting Korean historical novels because spatio-temporal background especially performs an important role in historical novels among the genre of Korean novels. To the best of our knowledge, we could not find previous study that was aimed at Korean novels. In this paper, we build a Korean historical national dictionary. Our dictionary has historical places and temple names of kings over many generations as well as currently existing spatial words or temporal words in Korean history. We also present a method for recognizing spatio-temporal background based on patterns of phrasal words in Korean sentences. Our rules utilize postposition for spatial background recognition and temple names for temporal background recognition. The knowledge of the recognized background can help readers to understand the flow of events and atmosphere, and can use to visualize the elements of novels.

Keywords: data mining, Korean historical novels, Korean linguistic feature, spatio-temporal background

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Authors: Melba D. Horton

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Algae are identified as an alternative source of biofuel because of their ubiquitous distribution in aquatic environments. Diatoms are unique forms of algae characterized by silicified cell walls which have gained prominence in various technological applications. Polk County is home to a multitude of ponds and lakes but has not been explored for the presence of diatoms. Considering the condition of the waters brought about by predominant phosphate mining activities in the area, this research was conducted to determine if endemic diatoms are present and explore their potential for low-cost mass production. Using custom-built photobioreactors, water samples from various lakes provided by the Polk County Parks and Recreation and from nearby ponds were used as the source of diatoms together with other algae obtained during collection. Results of the initial culture cycles were successful, but later an overgrowth of other algae crashed the diatom population. Experiments were conducted in the laboratory to tease out some factors possibly contributing to the die-off. Generally, the total biomass declines after two culture cycles and the causative factors need further investigation. The lipid yield is minimum; however, the high frustule production after die-off adds value to the overall benefit of the harvest.

Keywords: diatoms, algae, biofuel, lipid, photobioreactor, frustule

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Authors: Xiao Zhou, Jianlin Cheng

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A single amino acid mutation can have a significant impact on the stability of protein structure. Thus, the prediction of protein stability change induced by single site mutations is critical and useful for studying protein function and structure. Here, we presented a deep learning network with the dropout technique for predicting protein stability changes upon single amino acid substitution. While using only protein sequence as input, the overall prediction accuracy of the method on a standard benchmark is >85%, which is higher than existing sequence-based methods and is comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results demonstrate that deep learning is a promising technique for protein stability prediction. The good performance of this sequence-based method makes it a valuable tool for predicting the impact of mutations on most proteins whose experimental structures are not available. Both the downloadable software package and the user-friendly web server (DNpro) that implement the method for predicting protein stability changes induced by amino acid mutations are freely available for the community to use.

Keywords: bioinformatics, deep learning, protein stability prediction, biological data mining

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Authors: Mostafa Gamal, Hoda K. Mohamed, Islam El-Maddah, Ali Hamdi

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In recent years, many digital documents on the web have been created due to the rapid growth of ’social applications’ communities or ’Data-intensive applications’. The evolution of online-based multimedia data poses new challenges in storing and querying large amounts of data for online recommender systems. Graph data models have been shown to be more efficient than relational data models for processing complex data. This paper will explain the key differences between graph and relational databases, their strengths and weaknesses, and why using graph databases is the best technology for building a realtime recommendation system. Also, The paper will discuss several similarity metrics algorithms that can be used to compute a similarity score of pairs of nodes based on their neighbourhoods or their properties. Finally, the paper will discover how NLP strategies offer the premise to improve the accuracy and coverage of realtime recommendations by extracting the information from the stored unstructured knowledge, which makes up the bulk of the world’s data to enrich the graph database with this information. As the size and number of data items are increasing rapidly, the proposed system should meet current and future needs.

Keywords: graph databases, NLP, recommendation systems, similarity metrics

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Authors: Toby Gibbs, Suwaree Yordchim

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This research analyzes factors affecting the success of Litecoin Value within Thailand and develops a guideline for self-reliance for effective business implementation. Samples in this study included 119 people through surveys. The results revealed four main factors affecting the success as follows: 1) Future Career training should be pursued in applied Litecoin development. 2) Didn't grasp the concept of a digital currency or see the benefit of a digital currency. 3) There is a great need to educate the next generation of learners on the benefits of Litecoin within the community. 4) A great majority didn't know what Litecoin was. The guideline for self-reliance planning consisted of 4 aspects: 1) Development planning: by arranging meet up groups to conduct further education on Litecoin and share solutions on adoption into every day usage. Local communities need to develop awareness of the usefulness of Litecoin and share the value of Litecoin among friends and family. 2) Computer Science and Business Management staff should develop skills to expand on the benefits of Litecoin within their departments. 3) Further research should be pursued on how Litecoin Value can improve business and tourism within Thailand. 4) Local communities should focus on developing Litecoin awareness by encouraging street vendors to accept Litecoin as another form of payment for services rendered.

Keywords: litecoin, mining, confirmations, payment method

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Authors: David Pham, Yongfeng Zhang

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Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.

Keywords: explainable AI, graph attention network, graph neural network, node classification

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Authors: Bishal Baniya

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This paper proposes the sustainability related policy areas for decarbonisation and value addition at an organizational level. General and public sector organizations around the world are usually significant in terms of consuming resources and producing waste – powered through their massive procurement capacity. However, these organizations also possess huge potential to cut resource use and emission as many of these organizations controls supply chain of goods/services. They can therefore be a trend setter and can easily lead other major economic sectors such as manufacturing, construction and mining, transportation, etc. in pursuit towards paradigm shift for sustainability. Whilst the environmental and social awareness has improved in recent years and they have identified policy areas to improve the organizational environmental performance, value addition to the core business of the organization hasn’t been understood and interpreted correctly. This paper therefore investigates ways to align sustainability policy measures in a way that it creates better value proposition relative to benchmark by accounting both eco and social efficiency. Preliminary analysis shows co-benefits other than resource and cost savings fosters the business cases for organizations and this can be achieved by better aligning the policy measures and engaging stakeholders.

Keywords: policy measures, environmental performance, value proposition, organisational level

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Authors: Evangelia-Chrysanthi Kouklari, Evdokia Tagkouli, Vassiliki Ntre, Artemios Pehlivanidis, Stella Tsermentseli, Gerasimos Kolaitis, Katerina Papanikolaou

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Executive Function (EF) is a set of goal-directed cognitive skills essentially needed in problem-solving and social behavior. Developmental EF research has indicated that EF emerges early in life and marks dramatic changes before the age of 5. Research evidence has suggested that it may continue to develop up to adolescence as well, following the development of the prefrontal cortex. Over the last decade, research evidence has suggested distinguished domains of cool and hot EF, but traditionally the development of EF in Autism Spectrum Disorder (ASD) has been examined mainly with tasks that address the “cool” cognitive aspects of EF. Thus, very little is known about the development of “hot” affective EF processes and whether the cross-sectional developmental pathways of cool and hot EF present similarities in ASD. Cool EF has also been proven to have a strong correlation with Theory of Mind (ToM) in young and middle childhood in typical development and in ASD, but information about the relationship of hot EF to ToM skills is minimal. The present study’s objective was to explore the age-related changes of cool and hot EF in ASD participants from middle childhood to adolescence, as well as their relationship to ToM. This study employed an approach of cross-sectional developmental trajectories to investigate patterns of cool and hot EF relative to chronological age within ASD. Eighty-two participants between 7 and 16 years of age were recruited to undertake measures that assessed cool EF (working memory, cognitive flexibility, planning & inhibition), hot EF (affective decision making & delay discounting) and ToM (false belief and mental state/emotion recognition). Results demonstrated that trajectories of all cool EF presented age-related changes in ASD (improvements with age). With regards to hot EF, affective decision-making presented age-related changes, but for delay discounting, there were no statistically significant changes found across younger and older ASD participants. ToM was correlated only to cool EF. Theoretical implications are discussed as the investigation of the cross-sectional developmental trajectories of the broader EF (cool and hot domains) may contribute to better defining cognitive phenotypes in ASD. These findings highlight the need to examine developmental trajectories of both hot and cool EF in research and clinical practice as they may aid in enhancing diagnosis or better-informed intervention programs.

Keywords: autism spectrum disorder, developmental trajectories, executive function, theory of mind

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Authors: Tamanna Goyal, Divya Bansal, Sanjeev Sofat

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In world-threatening terrorist attacks, where early detection, distinction, and prediction are effective diagnosis techniques and for functionally accurate and precise analysis of terrorism data, there are so many data mining & statistical approaches to assure accuracy. The computational extraction of derived patterns is a non-trivial task which comprises specific domain discovery by means of sophisticated algorithm design and analysis. This paper proposes an approach for similarity extraction by obtaining the useful attributes from the available datasets of terrorist attacks and then applying feature selection technique based on the statistical impurity measures followed by clustering techniques on the basis of similarity measures. On the basis of degree of participation of attributes in the rules, the associative dependencies between the attacks are analyzed. Consequently, to compute the similarity among the discovered rules, we applied a weighted similarity measure. Finally, the rules are grouped by applying using hierarchical clustering. We have applied it to an open source dataset to determine the usability and efficiency of our technique, and a literature search is also accomplished to support the efficiency and accuracy of our results.

Keywords: association rules, clustering, similarity measure, statistical approaches

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Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original data set. In this paper, a new design of distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 data set is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

Keywords: distributed intrusion detection system, mobile agent, feature selection, bees algorithm, decision tree

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Authors: Aziz Larbi, Widad Sadok

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In Morocco, the mountainous regions extend over about 26% of the national territory where 30% of the total population live. They contain opportunities for agriculture, forestry, pastureland and mining. The production systems in these zones are characterised by crop diversification. However, these areas have become vulnerable to the effects of climate change. To understand these effects in relation to the population living in these areas, a study was carried out in the zone of Taliouine, in the Anti-Atlas. The vulnerability of crop productions to climate change was analysed and the different ways of adaptation adopted by farmers were identified. The work was done on saffron, the most profitable crop in the target area even though it requires much water. Our results show that the majority of the farmers surveyed had noticed variations in the climate of the region: irregularity of precipitation leading to a decrease in quantity and an uneven distribution throughout the year; rise in temperature; reduction in the cold period and less snow. These variations had impacts on the cropping system of saffron and its productivity. To cope with these effects, the farmers adopted various strategies: better management and use of water; diversification of agricultural activities; increase in the contribution of non-agricultural activities to their gross income; and seasonal migration.

Keywords: climate change, Taliouine, saffron, perceptions, adaptation strategies

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Authors: Kim Jin-OK, Lee Jin-Eui, Han Seung-Hoon, Kim Nam-Jo

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This study aimed to analyze the effect of the motivation of Chinese tourists to visit North Korea on their decision making process. Chinese tourists account for a considerable portion of foreign tourists in the world, while North Korea is the favorite tourist attraction of Chinese tourists. The motivation to visit North Korea was divided into three factors: the redness, which is the modern cultural heritage of Communism based on the red tourism accounting for the significant portion of domestic tourism, the novelty of the special environment of North Korean society, and the convenience of tour to North Korea in terms of geographical distance and policy of China. Red tourism refers to visiting the places of revolutionary events, monuments, artifacts and the residences of previous communist leaders, and other places related to the past Chinese Communist Party. As a revolutionary tourism, red tourism has recently been taking place in the old communist countries to recall their memories on the revolutionary places in China, as well as in North Korea, Vietnam, Cambodia, Russia, Bulgaria, Cuba, etc. In order to examine the effect of the segmented motivations on the revisit intention of Chinese tourists who have experienced a tour to North Korea, this study employed the model of goal-directed behavior, a model developed by adding a variable of emotion to the theory of planned behavior, which has a strong explanatory power on the decision making process of people in social science. For achieving the aim of the study, the data was collected through the survey in Dandong, China against Chinese tourists who have visited North Korea. The results of this study found that not only the novelty of North Korea, but also the redness, which accounts for the largest proportion in the domestic tourism, are significantly affecting overseas tour of Chinese tourists at this time point where overseas tour of Chinese tourists continue to increase. The results, therefore, suggest that the old communist countries, including those in Asia, need an emotional promotion strategy that stimulates nostalgia by focusing on the redness of the modern cultural heritage of Communism to attract Chinese tourists.

Keywords: model of goal-directed behavior, modern cultural heritage, North Korea, red tourism

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Authors: I. Stacher, B. Schrank, K. Stiehl, K. A. Woodcock

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Background: The need and desire for connectedness and belonging to a peer group is a major concern in middle childhood. This is particularly true for the period of school transition when making and maintaining friendships is put to the test. Social relations are important for enhancing self-esteem, confidence, and mental health. Conflicts with peers and victimization mark challenges in the complex social environment of early adolescents. Thus, the promotion of supportive peer relationships is an important social goal. The current literature lacks an in-depth analysis of young people’s experiences connected to making and maintaining friendships. Aim: This qualitative study aims to understand the factors that facilitate and hinder friendship and peer relations within the complex context of school transition. Methods: Youth engagement workshops at primary and secondary schools were conducted with 53 classes (N = 906 pupils; M age = 10.44; SD = .912) in 29 different schools across lower Austria. A big poster was created with the entire class, collecting early adolescents’ ideas on ways they can support each other in the school environment. Then, students were divided into smaller groups and encouraged to share their personal experiences of friendship. Verbatim quotes from students were collected on observation sheets and sticky notes during the activities. A thematic analysis was conducted. Results: Early adolescents describe facilitating factors that allow them to connect with peers. These descriptions are mainly on a behavioral level and are relevant for face-to-face and digital contact, e.g., practical and emotional support, spending time together, pleasure and fun. Specific challenges such as offensive actions, betrayal, and lack of emotion regulation exist and need to be addressed if aiming to reduce barriers between peers. Conclusion: Knowing first-hand experiences, desires, and barriers for making and maintaining friends at the time of school transition will help researchers to develop preventive health programs that adequately address the needs and preferences of today’s youth.

Keywords: youth voice, experts by experience, friendship, peer relations, primary-secondary school, transition

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Authors: Sirilak Areerachakul

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Water quality has initiated serious management efforts in many countries. Artificial Neural Network (ANN) models are developed as forecasting tools in predicting water quality trend based on historical data. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (T-Coliform). The methodology involves applying data mining techniques using multilayer perceptron (MLP) neural network models. The data consisted of 11 sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2007-2011. The results of multilayer perceptron neural network exhibit a high accuracy multilayer perception rate at 94.23% in classifying the water quality of Saen Saep canal in Bangkok. Subsequently, this encouraging result could be combined with GIS data improves the classification accuracy significantly.

Keywords: artificial neural network, geographic information system, water quality, computer science

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Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

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Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

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Authors: Magdalena Igras, Joanna Grzybowska, Mariusz Ziółko

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The study of emotions expressed in emergency phone call is presented, covering both statistical analysis of emotions configurations and an attempt to automatically classify emotions. An emergency call is a situation usually accompanied by intense, authentic emotions. They influence (and may inhibit) the communication between caller and responder. In order to support responders in their responsible and psychically exhaustive work, we studied when and in which combinations emotions appeared in calls. A corpus of 45 hours of conversations (about 3300 calls) from emergency call center was collected. Each recording was manually tagged with labels of emotions valence (positive, negative or neutral), type (sadness, tiredness, anxiety, surprise, stress, anger, fury, calm, relief, compassion, satisfaction, amusement, joy) and arousal (weak, typical, varying, high) on the basis of perceptual judgment of two annotators. As we concluded, basic emotions tend to appear in specific configurations depending on the overall situational context and attitude of speaker. After performing statistical analysis we distinguished four main types of emotional behavior of callers: worry/helplessness (sadness, tiredness, compassion), alarm (anxiety, intense stress), mistake or neutral request for information (calm, surprise, sometimes with amusement) and pretension/insisting (anger, fury). The frequency of profiles was respectively: 51%, 21%, 18% and 8% of recordings. A model of presenting the complex emotional profiles on the two-dimensional (tension-insecurity) plane was introduced. In the stage of acoustic analysis, a set of prosodic parameters, as well as Mel-Frequency Cepstral Coefficients (MFCC) were used. Using these parameters, complex emotional states were modeled with machine learning techniques including Gaussian mixture models, decision trees and discriminant analysis. Results of classification with several methods will be presented and compared with the state of the art results obtained for classification of basic emotions. Future work will include optimization of the algorithm to perform in real time in order to track changes of emotions during a conversation.

Keywords: acoustic analysis, complex emotions, emotion recognition, machine learning

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Authors: Temidayo O. Omotehinwa, David O. Oyewola, Friday J. Agbo

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This study aims to gather a proper perspective of the development landscape of Computer Science research in Nigeria. Therefore, a bibliometric analysis of 4,333 bibliographic records of Computer Science research in Nigeria in the last 31 years (1991-2021) was carried out. The bibliographic data were extracted from the Scopus database and analyzed using VOSviewer and the bibliometrix R package through the biblioshiny web interface. The findings of this study revealed that Computer Science research in Nigeria has a growth rate of 24.19%. The most developed and well-studied research areas in the Computer Science field in Nigeria are machine learning, data mining, and deep learning. The social structure analysis result revealed that there is a need for improved international collaborations. Sparsely established collaborations are largely influenced by geographic proximity. The funding analysis result showed that Computer Science research in Nigeria is under-funded. The findings of this study will be useful for researchers conducting Computer Science related research. Experts can gain insights into how to develop a strategic framework that will advance the field in a more impactful manner. Government agencies and policymakers can also utilize the outcome of this research to develop strategies for improved funding for Computer Science research.

Keywords: bibliometric analysis, biblioshiny, computer science, Nigeria, science mapping

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Authors: Niringiye Fred

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Banyabunagana has always had access to land resources for grazing animals, sand mining, and farmland across the border in the Democratic Republic of Congo during the pre-colonial and colonial times, usually on an informal arrangement facilitated by kinship ties and rent transactions for these resources. However, in recent periods, the government of the Democratic Republic of the Congo (DRC) has been pursuing a policy of constructing barriers such as walls and fences so that Banyabunagana communities do not access the land on the DRC side of the border. This is happening in the background of increased and intensified demand for land use on the side of the Ugandan community. This paper will attempt to discuss the reasons behind the construction of walls, fences, and other barriers which deny access to land for Banyabunagana communities in Bunagana Parish, Muramba Sub-county- Kisoro district, Uganda. The research will attempt to answer the following main questions, among others, whether there are the factors that explain the construction of walls and fences which could limit or deny access to the informal use of land and other resources and whether policy options to ensure continued access to land and other resources for local communities.

Keywords: border, walls, fences, land resource access

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Authors: Opeyemi Peter Idowu

Abstract:

Breast cancer is prevalent in northern Nigerian women, most especially in Jos, Plateau State, owing to anthropogenic activities such as solid earth mineral mining as far back as 1904. In this study, atomic absorption spectrometry was used to determine the concentration of eight heavy metals (Cd, As, Cr, Cu, Fe, Pb, Ni, and Zn) in cancerous and non-cancerous breast tissues of Jos Nigerian Women. The levels of heavy metals ranged from 1.08 to 29.34 mg/kg, 0.29 to 10.76 mg/kg, 0.35 to 51.93 mg/kg, 5.15 to 62.93 mg/kg, 11.64 to 51.10 mg/kg, 0.42 to 83.16 mg/kg, 2.08 to 43.07 mg/kg and 1.67 to 71.53 mg/kg for Cd, As, Cr, Cu, Fe, Pb, Ni and Zn respectively. Using MATLAB R2016a, significant differences (tᵥ = 0.0041 - 0.0317) existed between the levels of all the heavy metals in cancerous and non-cancerous breast tissues except Fe. At 0.01 level of significance, a positive significant correlation existed between Pb and Fe, Pb and Cu, Pb and Fe, Ni and Fe, Cr and Pb, as well as Ni and Cr (r = 0.583 – 0.998) in cancerous breast tissues. Using ANOVA, significant differences also occurred in the levels of these heavy metals in cancerous breast tissues (p = 1.910510×10⁻²⁶). The relatively high levels of the cancer-induced heavy metals (Cd, As, Cr, and Pb) compared with control indicated contamination or exposure to heavy metals, which could be the major cause of cancer in these female subjects. This was evidence of contamination as a result of exposure by ingestion, inhalation, or other means to one anthropogenic activity of the other. Therapeutic measures such as gastric lavage, ascorbic acid consumption, and divalent cation treatment are all effective ways to manage heavy metal toxicity in the subjects to lower the risk of breast cancer.

Keywords: breast cancer, heavy metals, spectroscopy, bio-accumulation

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Authors: William Xiu Shun Wong, Myungsu Lim, Yoonjin Hyun, Chen Liu, Seongi Choi, Dasom Kim, Kee-Young Kwahk, Namgyu Kim

Abstract:

Recently, many users have begun to frequently share their opinions on diverse issues using various social media. Therefore, numerous governments have attempted to establish or improve national policies according to the public opinions captured from various social media. In this paper, we indicate several limitations of the traditional approaches to analyze public opinion on science and technology and provide an alternative methodology to overcome these limitations. First, we distinguish between the science and technology analysis phase and the social issue analysis phase to reflect the fact that public opinion can be formed only when a certain science and technology is applied to a specific social issue. Next, we successively apply a start list and a stop list to acquire clarified and interesting results. Finally, to identify the most appropriate documents that fit with a given subject, we develop a new logical filter concept that consists of not only mere keywords but also a logical relationship among the keywords. This study then analyzes the possibilities for the practical use of the proposed methodology thorough its application to discover core issues and public opinions from 1,700,886 documents comprising SNS, blogs, news, and discussions.

Keywords: big data, social network analysis, text mining, topic modeling

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Authors: Diana López-Soto, Soumaya Yacout, Francisco Ángel-Bello

Abstract:

Although inventories are considered as stocks of money sitting on shelve, they are needed in order to secure a constant and continuous production. Therefore, companies need to have control over the amount of inventory in order to find the balance between excessive and shortage of inventory. The classification of items according to certain criteria such as the price, the usage rate and the lead time before arrival allows any company to concentrate its investment in inventory according to certain ranking or priority of items. This makes the decision making process for inventory management easier and more justifiable. The purpose of this paper is to present a new approach for the classification of new items based on the already existing criteria. This approach is called the Logical Analysis of Data (LAD). It is used in this paper to assist the process of ABC items classification based on multiple criteria. LAD is a data mining technique based on Boolean theory that is used for pattern recognition. This technique has been tested in medicine, industry, credit risk analysis, and engineering with remarkable results. An application on ABC inventory classification is presented for the first time, and the results are compared with those obtained when using the well-known AHP technique and the ANN technique. The results show that LAD presented very good classification accuracy.

Keywords: ABC multi-criteria inventory classification, inventory management, multi-class LAD model, multi-criteria classification

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Authors: Ju-Young Kim, Tae-Heon Moon, Jung-Hun Cho

Abstract:

Urban regeneration projects have been actively promoted in Korea. In particular, Jeonju Hanok Village is evaluated as one of representative cases in terms of utilizing local cultural heritage sits in the urban regeneration project. However, recently, there has been a growing concern in this area, due to the ‘gentrification’, caused by the excessive commercialization and surging tourists. This trend was changing land and building use and resulted in the loss of identity of the region. In this regard, this study analyzed the land use transformation between 2010 and 2016 to identify the commercialization trend in Jeonju Hanok Village. In addition, it conducted SNS big data analysis on Jeonju Hanok Village from February 14th, 2016 to March 31st, 2016 to identify visitors’ awareness of the village. The study results demonstrate that rapid commercialization was underway, unlikely the initial intention, so that planners and officials in city government should reconsider the project direction and rebuild deliberate management strategies. This study is meaningful in that it analyzed the land use transformation and SNS big data to identify the current situation in urban regeneration area. Furthermore, it is expected that the study results will contribute to the vitalization of regeneration area.

Keywords: land use, SNS, text mining, urban regeneration

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

Abstract:

Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: cancer classification, feature selection, deep learning, genetic algorithm

Procedia PDF Downloads 108