Search results for: Spanning tree

Authors: Saahith M. S., Sivakami R.

Abstract:

In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

Procedia PDF Downloads 38

Authors: Ajin Frank J., Shridevi Jeevan Kamble

Abstract:

The primary objective of this research paper is to identify significant patterns and insights in road accident data in India spanning from 2016 to 2021. The study reveals that the frequency of accidents, injuries, and fatalities varies depending on numerous factors such as the type of vehicle, time of accidents, age of the vehicle, age and gender of the driver, among others. Notably, the COVID-19 pandemic and subsequent lockdown measures have significantly impacted these figures. One of the key findings of the analysis is the rise in the number of accidents and deaths involving two-wheeler vehicles, particularly among younger individuals, in major states across India. This trend is of concern, and there is a need for increased awareness and precautions to prevent these types of accidents. Additionally, with the imminent rise of electric vehicles in the coming years, ensuring their safety on the road is a critical matter. Another significant factor contributing to road accidents is the age of vehicles. As vehicles age, their handling becomes more challenging compared to new ones, increasing the risk of accidents. Thus, it is imperative for the government to impose stringent regulations and laws to reduce these accident-causing factors and raise awareness among individuals about taking necessary precautions to avoid accidents. This study highlights the importance of understanding the underlying patterns and factors contributing to road accidents in India. Through this knowledge, policymakers and stakeholders can develop effective strategies to address these challenges and promote road safety, ultimately reducing the number of accidents, injuries, and fatalities on Indian roads.

Keywords: road accidents, India, road safety, accident deaths

Procedia PDF Downloads 84

Authors: Timothy E. Frank, Josh R. Aldred, Sophie B. Boulware, Michelle K. Cabonce, Justin H. White

Abstract:

Materials degrade at different rates in different environments depending on factors such as temperature, aridity, salinity, and solar radiation. Therefore, predicting asset longevity depends, in part, on the environmental conditions to which the asset is exposed. Heating, ventilation, and air conditioning (HVAC) systems are critical to building operations yet are responsible for a significant proportion of their energy consumption. HVAC energy use increases substantially with slight operational inefficiencies. Understanding the environmental influences on HVAC degradation in detail will inform maintenance schedules and capital investment, reduce energy use, and increase lifecycle management efficiency. HVAC inspection records spanning 14 years from 21 locations across the United States were compiled and associated with the climate conditions to which they were exposed. Three environmental features were explored in this study: average high temperature, average low temperature, and annual precipitation, as well as four non-environmental features. Initial insights showed no correlations between individual features and the rate of HVAC component degradation. Using neighborhood component analysis, however, the most critical features related to degradation were identified. Two models were considered, and results varied between them. However, longitude and latitude emerged as potentially the best predictors of average HVAC component degradation. Further research is needed to evaluate additional environmental features, increase the resolution of the environmental data, and develop more robust models to achieve more conclusive results.

Keywords: climate, degradation, HVAC, neighborhood component analysis

Procedia PDF Downloads 431

Authors: Phan Minh Trang, Joacim Rocklöv, Kim Bao Giang, Gunnar Kullgren, Maria Nilsson

Abstract:

There are recent studies from high income countries reporting an association between heat waves and hospital admissions for mental health disorders. It is not previously studied if such relations exist in sub-tropical and tropical low- and middle-income countries. In this study from Vietnam, the assumption was that hospital admissions for mental disorders may be triggered, or exacerbated, by heat exposure and heat waves. A database from Hanoi Mental Hospital with mental disorders diagnosed by the International Classification of Diseases 10, spanning over five years, was used to estimate the heatwave-related impacts on admissions for mental disorders. The relationship was analysed by a Negative Binomial regression model accounting for year, month, and days of week. The focus of the study was heat-wave events with periods of three or seven consecutive days above the threshold of 35oC daily maximum temperature. The preliminary study results indicated that heat-waves increased the risks for hospital admission for mental disorders (F00-79) from heat-waves of three and seven days with relative risks (RRs) of 1.16 (1.01–1.33) and 1.42 (1.02–1.99) respectively, when compared with non-heat-wave periods. Heatwave-related admissions for mental disorders increased statistically significantly among men, among residents in rural communities and in elderly. Moreover, cases for organic mental disorders including symptomatic illnesses (F0-9) and mental retardation (F70-79) raised in high risks during heat waves. The findings are novel studying a sub-tropical middle-income city, facing rapid urbanisation and epidemiological and demographic transitions.

Keywords: mental disorders, admissions for F0-9 or F70-79, maximum temperature, heat waves

Procedia PDF Downloads 244

Authors: Jephte Sompud, Emily A. Gilbert, Andy Russel Mojiol, Cynthia B. Sompud, Alim Biun

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Naturally regenerated acacia forest and secondary indigenous forest forms some of the urban forests in Sabah. Naturally regenerated acacia trees are usually seen along the road that exists as forest islands. Acacia tree is not an indigenous tree species in Sabah that was introduced in the 1960’s as fire breakers that eventually became one of the preferred trees for forest plantation for paper and pulp production. Due to its adaptability to survive even in impoverished soils and poor-irrigated land, this species has rapidly spread throughout Sabah through natural regeneration. Currently, there is a lack of study to investigate the bird population in the naturally regenerated acacia forest. This study is important because it shed some light on the role of naturally regenerated acacia forest on bird’s population, as bird is known to be a good bioindicator forest health. The aim of this study was to document the bird’s population in naturally regenerated acacia forest with that adjacent secondary indigenous forest. The study site for this study was at Universiti Malaysia Sabah (UMS) Campus. Two forest types in the campus were chosen as a study site, of which were naturally regenerated Acacia Forest and adjacent secondary indigenous forest, located at the UMS Hill. A total of 21 sampling days were conducted in each of the forest types. The method used during this study was solely mist nets with three pockets. Whenever a bird is caught, it is extracted from the net to be identified and measurements were recorded in a standard data sheet. Mist netting was conducted from 6 morning until 5 evening. This study was conducted between February to August 2014. Birds that were caught were ring banded to initiate a long-term study on the understory bird’s population in the Campus The data was analyzed using descriptive analysis, diversity indices, and t-test. The bird population diversity at naturally regenerated Acacia forest with those at the secondary indigenous forest was calculated using two common indices, of which were Shannon-Wiener and Simpson diversity index. There were 18 families with 33 species that were recorded from both sites. The number of species recorded at the naturally regenerated acacia forest was 26 species while at the secondary indigenous forest were 19 species. The Shannon diversity index for Naturally Regenerated Acacia Forest and secondary indigenous forests were 2.87 and 2.46. The results show that there was very significantly higher species diversity at the Naturally Regenerated Acacia Forest as opposed to the secondary indigenous forest (p<0.001). This suggests that Naturally Regenerated Acacia forest plays an important role in urban bird conservation. It is recommended that Naturally Regenerated Acacia Forests should be considered as an established urban forest conservation area as they do play a role in biodiversity conservation. More future studies in Naturally Regenerated Acacia Forest should be encouraged to determine the status and value of biodiversity conservation of this ecosystem.

Keywords: naturally regenerated acacia forest, bird population diversity, Universiti Malaysia Sabah, biodiversity conservation

Procedia PDF Downloads 427

Authors: Hounyèmè Romuald, Mama Daouda, Argillier Christine

Abstract:

The present study focused on the use of benthic macrofauna to define the reference state of an anthropized lagoon (Nokoué-Benin) from the responses of relevant metrics to proxies. The approach used is a combination of a joint species distribution model and Bayesian networks. The joint species distribution model was used to select the relevant metrics and generate posterior probabilities that were then converted into posterior response probabilities for each of the quality classes (pressure levels), which will constitute the conditional probability tables allowing the establishment of the probabilistic graph representing the different causal relationships between metrics and pressure proxies. For the definition of the reference thresholds, the predicted responses for low-pressure levels were read via probability density diagrams. Observations collected during high and low water periods spanning 03 consecutive years (2004-2006), sampling 33 macroinvertebrate taxa present at all seasons and sampling points, and measurements of 14 environmental parameters were used as application data. The study demonstrated reliable inferences, selection of 07 relevant metrics and definition of quality thresholds for each environmental parameter. The relevance of the metrics as well as the reference thresholds for ecological assessment despite the small sample size, suggests the potential for wider applicability of the approach for aquatic ecosystem monitoring and assessment programs in developing countries generally characterized by a lack of monitoring data.

Keywords: pressure proxies, bayesian inference, bioindicators, acadjas, functional traits

Procedia PDF Downloads 83

Authors: Manoj Gupta, Nirmendra Singh Bhadauria

Abstract:

Most of the applications in image processing require high spatial and high spectral resolution in a single image. For example satellite image system, the traffic monitoring system, and long range sensor fusion system all use image processing. However, most of the available equipment is not capable of providing this type of data. The sensor in the surveillance system can only cover the view of a small area for a particular focus, yet the demanding application of this system requires a view with a high coverage of the field. Image fusion provides the possibility of combining different sources of information. In this paper, we have decomposed the image using DTCWT and then fused using average and hybrid of (maxima and average) pixel level techniques and then compared quality of both the images using PSNR.

Keywords: image fusion, DWT, DT-CWT, PSNR, average image fusion, hybrid image fusion

Procedia PDF Downloads 606

Authors: Asawari Ajay Avhad

Abstract:

The Soil and Water Assessment Tool (SWAT) is a hydrological model designed to predict the complex interactions within natural and human-altered watersheds. This research applies the SWAT model to the Ulhas River basin, a small watershed undergoing urbanization and characterized by bowl-like topography. Three simulation scenarios (LC17, LC22, and LC27) are investigated, each representing different land use and land cover (LULC) configurations, to assess the impact of urbanization on runoff. The LULC for the year 2027 is generated using the MOLUSCE Plugin of QGIS, incorporating various spatial factors such as DEM, Distance from Road, Distance from River, Slope, and distance from settlements. Future climate data is simulated within the SWAT model using historical data spanning 30 years. A susceptibility map for runoff across the basin is created, classifying runoff into five susceptibility levels ranging from very low to very high. Sub-basins corresponding to major urban settlements are identified as highly susceptible to runoff. With consideration of future climate projections, a slight increase in runoff is forecasted. The reliability of the methodology was validated through the identification of sub-basins known for experiencing severe flood events, which were determined to be highly susceptible to runoff. The susceptibility map successfully pinpointed these sub-basins with a track record of extreme flood occurrences, thus reinforcing the credibility of the assessment methodology. This study suggests that the methodology employed could serve as a valuable tool in flood management planning.

Keywords: future land use impact, flood management, run off prediction, ArcSWAT

Procedia PDF Downloads 46

Authors: Hyung Jun Ahn

Abstract:

With the growing importance of social media, it is imperative to analyze it to understand the users. Users share useful information and their experience through social media, where much of what is shared is in the form of texts. This study focused on blogs and aimed to test whether the psycho-linguistic characteristics of blog texts vary with the subject or the type of experience of the texts. For this goal, blog texts about four different types of experience, Go, skiing, reading, and musical were collected through the search API of the Tistory blog service. The analysis of the texts showed that various psycholinguistic characteristics of the texts are different across the four categories of the texts. Moreover, the machine learning experiment using the characteristics for automatic text classification showed significant performance. Specifically, the ensemble method, based on functional tree and bagging appeared to be most effective in classification.

Keywords: blog, social media, text analysis, psycholinguistics

Procedia PDF Downloads 279

Authors: Feras Hanandeh, Izzat Alsmadi, Muhammad M. Kwafha

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Prefix trees or tries are data structures that are used to store data or index of data. The goal is to be able to store and retrieve data by executing queries in quick and reliable manners. In principle, the structure of the trie depends on having letters in nodes at the different levels to point to the actual words in the leafs. However, the exact structure of the trie may vary based on several aspects. In this paper, we evaluated different structures for building tries. Using datasets of words of different sizes, we evaluated the different forms of trie structures. Results showed that some characteristics may impact significantly, positively or negatively, the size and the performance of the trie. We investigated different forms and structures for the trie. Results showed that using an array of pointers in each level to represent the different alphabet letters is the best choice.

Keywords: data structures, indexing, tree structure, trie, information retrieval

Procedia PDF Downloads 452

Authors: Changju Lee, Seungmo Ku, Sondo Kim, Woojin Chang

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How to detecting financial bubble? Addressing this simple question has been the focus of a vast amount of empirical research spanning almost half a century. However, financial bubble is hard to observe and varying over the time; there needs to be more research on this area. In this paper, we used abnormal difference between common stocks price and those preferred stocks price to explain financial bubble. First, we proposed the ‘W-index’ which indicates spread between common stocks and those preferred stocks in stock market. Second, to prove that this ‘W-index’ is valid for measuring financial bubble, we showed that there is an inverse relationship between this ‘W-index’ and S&P500 rate of return. Specifically, our hypothesis is that when ‘W-index’ is comparably higher than other periods, financial bubbles are added up in stock market and vice versa; according to our hypothesis, if investors made long term investments when ‘W-index’ is high, they would have negative rate of return; however, if investors made long term investments when ‘W-index’ is low, they would have positive rate of return. By comparing correlation values and adjusted R-squared values of between W-index and S&P500 return, VIX index and S&P500 return, and TED index and S&P500 return, we showed only W-index has significant relationship between S&P500 rate of return. In addition, we figured out how long investors should hold their investment position regard the effect of financial bubble. Using this W-index, investors could measure financial bubble in the market and invest with low risk.

Keywords: financial bubble detection, future return, forecasting, pairs trading, preferred stocks

Procedia PDF Downloads 368

Authors: Abdullah A. Al Qurashi, Hattan A. Hassani, Bader K. Alaslap

Abstract:

Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy (ARVD/C) is an uncommon, inheritable cardiac disorder characterized by the progressive substitution of cardiac myocytes by fibro-fatty tissues. This pathologic substitution predisposes patients to ventricular arrhythmias and right ventricular failure. The underlying genetic defect predominantly involves genes encoding for desmosome proteins, particularly plakophilin-2 (PKP2). These aberrations lead to impaired cell adhesion, heightening the susceptibility to fibrofatty scarring under conditions of mechanical stress. Primarily, ARVD/C affects the right ventricle, but it can also compromise the left ventricle, potentially leading to biventricular heart failure. Clinical presentations can vary, spanning from asymptomatic individuals to those experiencing palpitations, syncopal episodes, and, in severe instances, sudden cardiac death. The establishment of a diagnostic criterion specifically tailored for ARVD/C significantly aids in its accurate diagnosis. Nevertheless, the task of early diagnosis is complicated by the disease's frequently asymptomatic initial stages, and the overall rarity of ARVD/C cases reported globally. In some cases, as exemplified by the adult female patient in this report, the disease may advance to terminal stages, rendering therapies like Ventricular Tachycardia (VT) ablation ineffective. This case underlines the necessity for increased awareness and understanding of ARVD/C to aid in its early detection and management. Through such efforts, we aim to decrease morbidity and mortality associated with this challenging cardiac disorder.

Keywords: arrhythmogenic right ventricular dysplasia, cardiac disease, interventional cardiology, cardiac electrophysiology

Procedia PDF Downloads 58

Authors: Mohsen Dourandish

Abstract:

Iran has a huge pipeline network in every state of country which is the longest and vastest pipeline network after Russia and USA (360,000 Km high pressure pipelines and 250,000 Km distribution networks). Furthermore in recent years National Iranian Gas Company is planning to develop natural gas network to cover all cities and villages above 20 families, in a way that 97 percent of Iran population will be gas consumer by 2020. In this condition, network resiliency will be the first priority of NIGC and due to that several planning for increasing resiliency of gas network is under construction. The most important strategy of NIGC is converting tree form pattern network to loop gas networks and developing underground gas storage near main gas consuming centers. In this regard NIGC is planning for construction of over 3500 km high-pressure pipeline and also 10 TCM gas storage capacities in UGSs.

Keywords: Iranian gas network, peak shaving, resiliency, underground gas storage

Procedia PDF Downloads 325

Authors: D. Augutis, M. Balalaikins, D. Bastyte, R. Ferenca, A. Gintaras, R. Karpuska, G. Svitra, U. Valainis

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Osmoderma species complex (consisting of Osmoderma eremita, O. barnabita, O. lassallei and O. cristinae) is a scarab beetle serving as indicator species in nature conservation. Osmoderma inhabits cavities containing sufficient volume of wood mould usually caused by brown rot in veteran deciduous trees. As the species, having high demands for the habitat quality, they indicate the suitability of the habitat for a number of other specialized saproxylic species. Since typical habitat needed for Osmoderma and other species associated with hollow veteran trees is rapidly declining, the species complex is protected under various legislation, such as Bern Convention, EU Habitats Directive and the Red Lists of many European states. Natura 2000 sites are the main tool for conservation of O. barnabita in Lithuania, currently 17 Natura 2000 sites are designated for the species, where monitoring is implemented once in 3 years according to the approved methodologies. Despite these monitoring efforts in species reports, provided to EU according to the Article 17 of the Habitats Directive, it is defined on the national level, that overall assessment of O. barnabita is inadequate and future prospects are poor. Therefore, research on the distribution and habitat status of O. barnabita was launched on the national level in 2016, which was complemented by preparatory actions of LIFE OSMODERMA project. The research was implemented in the areas equally distributed in the whole area of Lithuania, where O. barnabita was previously not observed, or not observed in the last 10 years. 90 areas, such as Habitats of European importance (9070 Fennoscandian wooded pastures, 9180 Tilio-Acerion forests of slopes, screes, and ravines), Woodland key habitats (B1 broad-leaved forest, K1 single giant tree) and old manor parks, were chosen for the research after review of habitat data from the existing national databases. The first part of field inventory of the habitats was carried out in 2016 and 2017 autumn and winter seasons, when relative abundance of O. barnabita was estimated according to larval faecal pellets in the tree cavities or around the trees. The state of habitats was evaluated according to the density of suitable and potential trees, percentage of not overshadowed trees and amount of undergrowth. The second part of the field inventory was carried out in the summer with pheromone traps baited with (R)-(+)-γ –decalactone. Results of the research show not only occurrence and habitat status of O. barnabita, but also help to clarify O. barnabita habitat requirements in Lithuania, define habitat size, its structure and distribution. Also, it compares habitat needs between the regions in Lithuania and inside and outside Natura 2000 areas designated for the species.

Keywords: habitat status, insect conservation, Osmoderma barnabita, veteran trees

Procedia PDF Downloads 137

Authors: Layla El Gaini, Majdouline Belaqziz, Meriem Outaki, Mariam Minhaj

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In this study, it introduce and validate a high-performance liquid chromatography method with diode-array detection (HPLC-DAD) specifically designed for the accurate quantification of caffeic acid in phenolic extracts obtained from olive mill wastewater. The separation process of caffeic acid was effectively achieved through the use of an Acclaim Polar Advantage column (5µm, 250x4.6mm). A meticulous multi-step gradient mobile phase was employed, comprising water acidified with phosphoric acid (pH 2.3) and acetonitrile, to ensure optimal separation. The diode-array detection was adeptly conducted within the UV–VIS spectrum, spanning a range of 200–800 nm, which facilitated precise analytical results. The method underwent comprehensive validation, addressing several essential analytical parameters, including specificity, repeatability, linearity, as well as the limits of detection and quantification, alongside measurement uncertainty. The generated linear standard curves displayed high correlation coefficients, underscoring the method's efficacy and consistency. This validated approach is not only robust but also demonstrates exceptional reliability for the focused analysis of caffeic acid within the intricate matrices of wastewater, thus offering significant potential for applications in environmental and analytical chemistry.

Keywords: high-performance liquid chromatography (HPLC-DAD), caffeic acid analysis, olive mill wastewater phenolics, analytical method validation

Procedia PDF Downloads 70

Authors: Zanele Ntshidi, Sebinasi Dzikiti, Dominic Mazvimavi

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South Africa is a dry country and yet it is ranked as the 8th largest exporter of fresh apples (Malus Domestica) globally. Prime apple producing regions are in the Eastern and Western Cape Provinces of the country where all the fruit is grown under irrigation. Climate change models predict increasingly drier future conditions in these regions and the frequency and severity of droughts is expected to increase. For the sustainability and growth of the fruit industry it is important to minimize non-beneficial water losses from the orchard floor. The aims of this study were firstly to compare the water use of cover crop species used in South African orchards for which there is currently no information. The second aim was to investigate how orchard water use (evapotranspiration) was partitioned into beneficial (tree transpiration) and non-beneficial (orchard floor evaporation) water uses for micro-sprinkler irrigated orchards with different canopy covers. This information is important in order to explore opportunities to minimize non-beneficial water losses. Six cover crop species (four exotic and two indigenous) were grown in 2 L pots in a greenhouse. Cover crop transpiration was measured using the gravimetric method on clear days. To establish how water use was partitioned in orchards, evapotranspiration (ET) was measured using an open path eddy covariance system, while tree transpiration was measured hourly throughout the season (October to June) on six trees per orchard using the heat ratio sap flow method. On selected clear days, soil evaporation was measured hourly from sunrise to sunset using six micro-lysimeters situated at different wet/dry and sun/shade positions on the orchard floor. Transpiration of cover crops was measured using miniature (2 mm Ø) stem heat balance sap flow gauges. The greenhouse study showed that exotic cover crops had significantly higher (p < 0.01) average transpiration rates (~3.7 L/m2/d) than the indigenous species (~ 2.2 L/m²/d). In young non-bearing orchards, orchard floor evaporative fluxes accounted for more than 60% of orchard ET while this ranged from 10 to 30% in mature orchards with a high canopy cover. While exotic cover crops are preferred by most farmers, this study shows that they use larger quantities of water than indigenous species. This in turn contributes to a larger orchard floor evaporation flux. In young orchards non-beneficial losses can be minimized by adopting drip or short range micro-sprinkler methods that reduce the wetted soil fraction thereby conserving water.

Keywords: evapotranspiration, sap flow, soil evaporation, transpiration

Procedia PDF Downloads 388

Authors: Aisha M. Khairat

Abstract:

Community education in the Arab Republic of Egypt is a model that provides education to remote, underprivileged villages and hamlets where children have no access to public education. The community education model is based on the philosophy of transforming individuals to reach their full potential and on instilling the seeds of empowerment and citizenship to induce societal transformation. This research aims at investigating the degree to which the leadership style and organizational culture of the Egyptian community schools demonstrates an empowering approach. Nile Valley NGO, an Egyptian Non-Governmental Organization (NGO) leading hundreds of Egyptian community schools was studied to investigate the perceptions of empowerment amongst its leadership. This in turn will have serious implications on the level of empowerment the communities managed by Nile Valley NGO are experiencing, and will serve as an indicator to the degree to which community schools are achieving their goals in transforming individuals and empowering communities and reforming Egyptian education – and not just a tool to reach literacy. This mixed-methods research utilized surveys and semi-structured interviews to capture the perceptions of empowerment in the views of a sample of 380 community schools facilitators (teachers) spanning 8 Egyptian governorates and Nile Valley NGO’s community education project team and leadership. The findings demonstrate interesting leadership approaches with traits from transformational and servant leadership theoretical models. The organizational culture at Nile Valley NGO reflects the universal dichotomy between market-oriented and humanitarian orientations. The perceptions of empowerment were positive, and several success stories were uncovered in spite of the many challenges faced on the national level and despite the scarcity or resources.

Keywords: community education, community schools in Egypt, empowerment, organizational culture, leadership

Procedia PDF Downloads 179

Authors: Fahad Almehmadi, Abdullah Alrajhi, Bader K. Alaslab, Abdullah A. Al Qurashi, Hattan A. Hassani

Abstract:

Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy (ARVD/C) is an uncommon, inheritable cardiac disorder characterized by the progressive substitution of cardiac myocytes by fibro-fatty tissues. This pathologic substitution predisposes patients to ventricular arrhythmias and right ventricular failure. The underlying genetic defect predominantly involves genes encoding for desmosome proteins, particularly plakophilin-2 (PKP2). These aberrations lead to impaired cell adhesion, heightening the susceptibility to fibrofatty scarring under conditions of mechanical stress. Primarily, ARVD/C affects the right ventricle, but it can also compromise the left ventricle, potentially leading to biventricular heart failure. Clinical presentations can vary, spanning from asymptomatic individuals to those experiencing palpitations, syncopal episodes, and, in severe instances, sudden cardiac death. The establishment of a diagnostic criterion specifically tailored for ARVD/C significantly aids in its accurate diagnosis. Nevertheless, the task of early diagnosis is complicated by the disease's frequently asymptomatic initial stages, and the overall rarity of ARVD/C cases reported globally. In some cases, as exemplified by the adult female patient in this report, the disease may advance to terminal stages, rendering therapies like Ventricular Tachycardia (VT) ablation ineffective. This case underlines the necessity for increased awareness and understanding of ARVD/C to aid in its early detection and management. Through such efforts, we aim to decrease morbidity and mortality associated with this challenging cardiac disorder.

Keywords: ARVD/C, cardiology, interventional cardiology, cardiac electrophysiology

Procedia PDF Downloads 63

Authors: Abdul Muqsit Abbasi, Ibrahim Chhipa, Asad Anwer, Saad Farooq, Hassan Berry, Sonu Kumar, Sundar Ali, Muhammad Owais Mahmood, Areeb Ur Rehman, Bahram Baloch

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Accurate translation between structurally diverse languages, such as Arabic and English, presents a critical challenge in natural language processing due to significant linguistic and cultural differences. This paper investigates the effectiveness of Facebook’s mBART model, fine-tuned specifically for sequence-tosequence (seq2seq) translation tasks between Arabic and English, and enhanced through advanced refinement techniques. Our approach leverages the Alef Dataset, a meticulously curated parallel corpus spanning various domains to capture the linguistic richness, nuances, and contextual accuracy essential for high-quality translation. We further refine the model’s output using advanced language models such as GPT-3.5 and GPT-4, which improve fluency, coherence, and correct grammatical errors in translated texts. The fine-tuned model demonstrates substantial improvements, achieving a BLEU score of 38.97, METEOR score of 58.11, and TER score of 56.33, surpassing widely used systems such as Google Translate. These results underscore the potential of mBART, combined with refinement strategies, to bridge the translation gap between Arabic and English, providing a reliable, context-aware machine translation solution that is robust across diverse linguistic contexts.

Keywords: natural language processing, machine translation, fine-tuning, Arabic-English translation, transformer models, seq2seq translation, translation evaluation metrics, cross-linguistic communication

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Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

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Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

Procedia PDF Downloads 520

Authors: Abewardhana Arachchi Bandula Dimuthu Priyadarshana Abewardhana, Rasika Nirosh Gonapinuwala Vithanage, Karawe Thanthreege Amila Madusanka Perera, Asanka Sanjeewa Karunarathne, Navullage Mayuri Radhika Perera

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The phenomenon of irregular migration and human trafficking presents multifaceted challenges to Sri Lanka, with specific focus on the migration routes to the United Arab Emirates (UAE), the Sultanate of Oman, and Malaysia. This research critically assesses the efficacy of a pilot project instituted at Bandaranaike International Airport aimed at the identification and deterrence of potential irregular migrants. Additionally, the study conducts a nuanced analysis of the geographical tendencies pertaining to passengers who revise their migration intentions at the airport. Pertinently, the findings indicate that Colombo and Gampaha Districts emerge as the most susceptible to human trafficking, with Galle, Nuwaraeliya, Rathnapura, and Polonnaruwa Districts following as areas of elevated concern, particularly within the framework of the 'visit visa' scenario. These insights emanate from an extensive data collection period spanning 50 days of the pilot project, encompassing 1,479 passengers, of which 46 returnees reported to the Safe Migration Promotion Unit. The research is founded on the twin objectives of comprehending the motivations of passengers and evaluating the effectiveness of interventions, with a view to devising precision-targeted prevention strategies. Through this endeavor, the study actively contributes to the safeguarding of the rights and welfare of migrants, significantly advancing the ongoing battle against irregular migration.

Keywords: irregular migration, human trafficking, airport interventions, geographic trends

Procedia PDF Downloads 81

Authors: Paula García-Llamas, Polonia Díez González, Angela Taboada

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In an era marked by unprecedented anthropogenic alterations to landscapes and biodiversity, the consequential loss of fauna, flora, and habitats poses a grave concern. It is imperative to evaluate our capacity to manage and mitigate such changes effectively. This study aims to scrutinize the efficacy of the Natura 2000 Network (NN2000) in landscape conservation within the autonomous community of Castile and Leon (Spain), spanning from 1990 to 2018. Leveraging land use change maps from the European Corine Land Cover database across four subperiods (1990-2000, 2000-2006, 2006-2012, and 2012-2018), we quantified alterations occurring both within NN2000 protected sites and within a 5km buffer zone. Additionally, we spatially assess land use/land cover patterns of change considering fluxes of various habitat types defined within NN2000. Our findings reveal that the protected areas under NN2000 were particularly susceptible to change, with the most significant transformations observed during the 1990-2000 period. Predominant change processes include secondary succession and scrubland formation due to land use cessation, deforestation, and agricultural intensification. While NN2000 demonstrates efficacy in curtailing urbanization and industrialization within buffer zones, its management measures have proven insufficient in safeguarding landscapes against the dynamic changes witnessed between 1990 and 2018, especially in relation to rural abandonment.

Keywords: Corine land cover, land cover changes, site of community importance, special protection area

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Authors: Dali Gaganidze, Ekaterine Abashidze

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Potato is one of the main agricultural crops in Georgia. Georgia produces early and late potato varieties in almost all regions. In traditional potato growing regions (Svaneti, Samckhet javaheti and Tsalka), the yield is higher than 30-35 t/ha. Among the plant pests that limit potato production and quality, the potato cyst nematodes (PCN) are harmful around the world. Yield losses caused by PCN are estimated up to 30%. Rout surveys conducted in two geographically distinct regions of Georgia producing potatoes - Samtskhe - Javakheti and Svaneti revealed potato cyst nematode Globodera rostochiensi. The aim of the study was the Phylogenetic analyses of Globodera rostochiensi revealed in Georgia by the amplification and sequencing of 28S gen in the D3 region and intergenic ITS1-15.8S-ITS2 region. Identification of all the samples from the two Globodera populations (Samtskhe - Javakheti and Svaneti), i.e., G. rostochiensis (20 isolates) were confirmed by conventional multiplex PCR with ITS 5 universal and PITSp4, PITSr3 specific primers of the cyst nematodes’ (G. pallida, G. rostochiensis). The size of PCR fragment 434 bp confirms that PCN samples from two populations, Samtskhe- Javakheti and Svaneti, belong to G. rostochiensi . The ITS1–5.8S-ITS2 regions were amplified using prime pairs: rDNA1 ( 5’ -TTGATTACGTCCCTGCCCTTT-3’ and rDNA2( 5’ TTTCACTCGCCGTTACTAAGG-3’), D3 expansion regions were amplified using primer pairs: D3A (5’ GACCCCTCTTGAAACACGGA-3’) and D3B (5’-TCGGAAGGAACCAGCTACTA-3’. PCR products of each region were cleaned up and sequenced using an ABI 3500xL Genetic Analyzer. Obtained sequencing results were analyzed by computer program BLASTN (https://blast.ncbi.nlm.nih.gov/Blast.cg). Phylogenetic analyses to resolve the relationships between the isolates were conducted in MEGA7 using both distance- and character-based methods. Based on analysis of G.rostochiensis isolate`s D3 expansion regions are grouped in three major clades (A, B and C) on the phylogenetic tree. Clade A is divided into three subclades; clade C is divided into two subclades. Isolates from the Samtckhet-javakheti population are in subclade 1 of clade A and isolates in subclade 1 of clade C. Isolates) from Svaneti populations are in subclade 2 of clade A and in clad B. In Clade C, subclade two is presented by three isolates from Svaneti and by one isolate (GL17) from Samckhet-Javakheti. . Based on analysis of G.rostochiensis isolate`s ITS1–5.8S-ITS2 regions are grouped in two main clades, the first contained 20 Georgian isolates of Globodera rostochiensis from Svaneti . The second clade contained 15 isolates of Globodera rostochiensis from Samckhet javakheti. Our investigation showed of high genetic variation of D3 and ITS1–5.8S-ITS2 region of rDNA of the isolates of G. rostochiensis from different geographic origins (Svameti, Samckhet-Javakheti) of Georgia. Acknowledgement: The research has been supported by the Shota Rustaveli National Scientific Foundation of Georgia : Project # FR17_235

Keywords: globodera rostochiensi, PCR, phylogenetic tree, sequencing

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Authors: Chih-Fong Tsai, Ya-Han Hu

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The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets.

Keywords: data mining, feature selection, pattern classification, dimensionality reduction

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Authors: M. C. Schloesser

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The purpose of this paper is to explore the use and impact of business analytics in European football. Despite good evidence from other major sports leagues, research on this topic in Europe is currently very scarce. This research relies on expert interviews on the use and objective of business analytics. Along with revenue data over 16 seasons spanning from 2004/05 to 2019/20 from Manchester City FC, we conducted a time series analysis to detect a structural breakpoint on the different revenue streams, i.e., sponsorship and ticketing, after analytical tools have been implemented. We not only find that business analytics have indeed been applied at Manchester City FC and revenue increase is the main objective of their utilization but also that business analytics is indeed a good means to increase revenues if applied sufficiently. We can thereby support findings from other sports leagues. Consequently, professional sports organizations are advised to apply business analytics if they aim to increase revenues. This research has shown that analytical practices do, in fact, support revenue growth and help to work more efficiently. As the knowledge of analytical practices is very confidential and not publicly available, we had to select one club as a case study which can be considered a research limitation. Other practitioners should explore other clubs or leagues. Further, there are other factors that can lead to increased revenues that need to be considered. Additionally, sports organizations need resources to be able to apply and utilize business analytics. Consequently, findings might only apply to the top teams of the European football leagues. Nonetheless, this paper combines insights and results on usage, objectives, and impact of business analytics in European professional football and thereby fills a current research gap.

Keywords: business analytics, expert interviews, revenue management, time series analysis

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Authors: Nguyen J. M., Lucas G., Ruan S., Digonnet H., Antonioli D.

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Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata.

Keywords: tabular data, CNNs, NICs, DeepNICs, random forest of perfect trees, classification

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Authors: Vivian Khamis

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Exposure to war and forced migration have been widely linked to child subsequent adaptation. What remains sparse is research spanning multiple risk and protective factors and examining their unique and relative implications to difficulties in mental health among refugee girls. This study investigated the mechanisms through which posttraumatic stress disorder (PTSD), emotion dysregulation , neuroticism, and behavioral and emotional disorders in Syrian refugee girls is impacted by exposure to war traumas, age, and other risk and protective factors such as coping styles, family relationships, and school environment. The sample consisted of 539 Syrian refugee girls who ranged in age from 7 to 18 years attending public schools in various governorates in Lebanon and Jordan. Two school counselors carried out the interviews with children at school. Results indicated that war trauma, older age, and a combination of negative copying style associated with conflict in the family could lead to an overall state of emotion dysregulation, neuroticism, behavioral and emotional disorders, and PTSD in refugee girls. On the other hand, lapse of time since resettlement in host country, positive copying style, cohesion, and expressiveness in the family would lead to more positive mental health status, including lower levels of emotion dysregulation, neuroticism, behavioral and emotional disorders, and PTSD . Enhanced understanding of the mechanistic role of risk and protective factors in contributing to difficulties in mental health in refugee girls may contribute to the development of effective interventions to target the psychological effects of the refugee experience.

Keywords: refugee girls, PTSD, emotion dysregulation, neuroticism, behavioral and emotional disorders

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

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Authors: Harshit Mittal, Neeraj Garg

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Hand symbol recognition is a pivotal component in the domain of computer vision, with far-reaching applications spanning sign language interpretation, human-computer interaction, and accessibility. This research paper discusses the approach with the integration of the Canny Edge algorithm and convolutional neural network. The significance of this study lies in its potential to enhance communication and accessibility for individuals with hearing impairments or those engaged in gesture-based interactions with technology. In the experiment mentioned, the data is manually collected by the authors from the webcam using Python codes, to increase the dataset augmentation, is applied to original images, which makes the model more compatible and advanced. Further, the dataset of about 6000 coloured images distributed equally in 5 classes (i.e., 1, 2, 3, 4, 5) are pre-processed first to gray images and then by the Canny Edge algorithm with threshold 1 and 2 as 150 each. After successful data building, this data is trained on the Convolutional Neural Network model, giving accuracy: 0.97834, precision: 0.97841, recall: 0.9783, and F1 score: 0.97832. For user purposes, a block of codes is built in Python to enable a window for hand symbol recognition. This research, at its core, seeks to advance the field of computer vision by providing an advanced perspective on hand sign recognition. By leveraging the capabilities of the Canny Edge algorithm and convolutional neural network, this study contributes to the ongoing efforts to create more accurate, efficient, and accessible solutions for individuals with diverse communication needs.

Keywords: hand symbol recognition, computer vision, Canny edge algorithm, convolutional neural network

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Authors: Seyed Abolhasan Naeini, Hamidreza Rahmani

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Laboratory consolidated undrained triaxial (CU) tests were carried out to study the strength behavior of silty soil reinforced with randomly plastic waste bottle chips. Specimens mixed with plastic waste chips in triaxial compression tests with 0.25, 0.50, 0.75, 1.0, and 1.25% by dry weight of soil and tree different length including 4, 8, and 12 mm. In all of the samples, the width and thickness of plastic chips were kept constant. According to the results, the amount and size of plastic waste bottle chips played an important role in the increasing of the strength parameters of reinforced silt compared to the pure soil. Because of good results, the suggested method of soil improvement can be used in many engineering problems such as increasing the bearing capacity and settlement reduction in foundations.

Keywords: reinforcement, silt, soil improvement, triaxial test, waste bottle chips

Procedia PDF Downloads 285