Search results for: random forest models

Authors: Sungzoon Cho, Hye Jin Lee, Sungwhan Jeon, Dongyoung Min, Sungwon Lyu

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Major public corporations worldwide use conference calls to report their quarterly earnings. These 'earnings' conference calls allow for questions from stock analysts. We investigated if it is possible to identify sentiment from the call script and use it to predict stock price movement. We analyzed call scripts from six companies, two each from Korea, China and Indonesia during six years 2011Q1 – 2017Q2. Random forest with Frequency-based sentiment scores using Loughran MacDonald Dictionary did better than control model with only financial indicators. When the stock prices went up 20 days from earnings release, our model predicted correctly 77% of time. When the model predicted 'up,' actual stock prices went up 65% of time. This preliminary result encourages us to investigate advanced sentiment scoring methodologies such as topic modeling, auto-encoder, and word2vec variants.

Keywords: earnings call script, random forest, sentiment analysis, stock price prediction

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Authors: Cheng-Yu Tsai, Wen-Te Liu, Shin-Mei Hsu, Yin-Tzu Lin, Chi Wu

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Background: Obstructive Sleep apnea syndrome (OSAS) is a common respiratory disorder during sleep. In addition, Body parameters were identified high predictive importance for OSAS severity. However, the effects of body parameters on OSAS severity remain unclear. Objective: In this study, the objective is to establish a prediction model for OSAS by using body parameters and investigate the effects of body parameters in OSAS. Methodologies: Severity was quantified as the polysomnography and the mean hourly number of greater than 3% dips in oxygen saturation during examination in a hospital in New Taipei City (Taiwan). Four levels of OSAS severity were classified by the apnea and hypopnea index (AHI) with American Academy of Sleep Medicine (AASM) guideline. Body parameters, including neck circumference, waist size, and body mass index (BMI) were obtained from questionnaire. Next, dividing the collecting subjects into two groups: training and testing groups. The training group was used to establish the random forest (RF) to predicting, and test group was used to evaluated the accuracy of classification. Results: There were 3330 subjects recruited in this study, whom had been done polysomnography for evaluating severity for OSAS. A RF of 1000 trees achieved correctly classified 79.94 % of test cases. When further evaluated on the test cohort, RF showed the waist and BMI as the high import factors in OSAS. Conclusion It is possible to provide patient with prescreening by body parameters which can pre-evaluate the health risks.

Keywords: apnea and hypopnea index, Body parameters, obstructive sleep apnea syndrome, Random Forest

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Authors: Viacheslav Shkuratskyy, Aminu Bello Usman, Michael O’Dea, Saifur Rahman Sabuj

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This paper examines relationships between solar activity and earthquakes; it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth.

Keywords: k-nearest neighbour, support vector regression, random forest regression, long short-term memory network, earthquakes, solar activity, sunspot number, solar wind, solar flares

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Authors: Pius Akindele Adeniyi

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The World's tropical forests are disappearing at an alarming rate of more than 200,000 ha per year as a result of deforestation due mainly to population pressures, economic growth, poor management and inappropriate policy. A forest policy determines the role of the sector in a nation's economy and it is formulated in accordance with the objectives of the national economic development. Urban forestry as a concept is relatively new in Nigeria when compared to European and American countries. It consists of growing of trees, shrubs and grass along streets, in parks, and around public or private buildings whose management rests in the hands of the public and private owners. Major urban centers in Nigeria are devoid of efficiently planned tree-planting programs. Hence, various factors militating against environmental improvements, such as climate and other agents of degradation, are highlighted for the necessary attention. The paper discusses the need for forest policy formulation and the objectives of forest policy. Elements of forest policy are also discussed and in particular, those peculiar to urbanization and degraded lands are Forest policy and land-use and policy implementation together with some problem issues in forest policy are discussed while recommendations are given on formulation of a forest policy.

Keywords: urban, forest, policy, environment, interaction, degraded

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Authors: Amit Kumar Verma

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The present study deals with fire prediction model in Terai Arc Landscape, one of the most dramatic ecosystems in Asia where large, wide-ranging species such as tiger, rhinos, and elephant will thrive while bringing economic benefits to the local people. Forest fires cause huge economic and ecological losses and release considerable quantities of carbon into the air and is an important factor inflating the global burden of carbon emissions. Forest fire is an important factor of behavioral cum ecological habit of tiger in wild. Post fire changes i.e. micro and macro habitat directly affect the tiger habitat or land. Vulnerability of fire depicts the changes in microhabitat (humus, soil profile, litter, vegetation, grassland ecosystem). Microorganism like spider, annelids, arthropods and other favorable microorganism directly affect by the forest fire and indirectly these entire microorganisms are responsible for the development of tiger (Panthera tigris) habitat. On the other hand, fire brings depletion in prey species and negative movement of tiger from wild to human- dominated areas, which may leads the conflict i.e. dangerous for both tiger & human beings. Early forest fire prediction through mapping the risk zones can help minimize the fire frequency and manage forest fires thereby minimizing losses. Satellite data plays a vital role in identifying and mapping forest fire and recording the frequency with which different vegetation types are affected. Thematic hazard maps have been generated by using IDW technique. A prediction model for fire occurrence is developed for TAL. The fire occurrence records were collected from state forest department from 2000 to 2014. Disciminant function models was used for developing a prediction model for forest fires in TAL, random points for non-occurrence of fire have been generated. Based on the attributes of points of occurrence and non-occurrence, the model developed predicts the fire occurrence. The map of predicted probabilities classified the study area into five classes very high (12.94%), high (23.63%), moderate (25.87%), low(27.46%) and no fire (10.1%) based upon the intensity of hazard. model is able to classify 78.73 percent of points correctly and hence can be used for the purpose with confidence. Overall, also the model works correctly with almost 69% of points. This study exemplifies the usefulness of prediction model of forest fire and offers a more effective way for management of forest fire. Overall, this study depicts the model for conservation of tiger’s natural habitat and forest conservation which is beneficial for the wild and human beings for future prospective.

Keywords: fire prediction model, forest fire hazard, GIS, landsat, MODIS, TAL

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Authors: Mindaye Teshome, Nesibu Yahya, Carlos Moreira Miquelino Eleto Torres, Pedro Manuel Villaa, Mehari Alebachew

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Arbagugu forest is one of the remnant dry Afromontane forests under severe anthropogenic disturbances in central Ethiopia. Despite this fact, up-to-date information is lacking about the status of the forest and its role in climate change mitigation. In this study, we evaluated the woody species composition, structure, biomass, and carbon stock in this forest. We employed a systematic random sampling design and established fifty-three sample plots (20 × 100 m) to collect the vegetation data. A total of 37 woody species belonging to 25 families were recorded. The density of seedlings, saplings, and matured trees were 1174, 101, and 84 stems ha-1, respectively. The total basal area of trees with DBH (diameter at breast height) ≥ 2 cm was 21.3 m2 ha-1. The characteristic trees of dry Afromontane Forest such as Podocarpus falcatus, Juniperus procera, and Olea europaea subsp. cuspidata exhibited a fair regeneration status. On the contrary, the least abundant species Lepidotrichilia volkensii, Canthium oligocarpum, Dovyalis verrucosa, Calpurnia aurea, and Maesa lanceolata exhibited good regeneration status. Some tree species such as Polyscias fulva, Schefflera abyssinica, Erythrina brucei, and Apodytes dimidiata lack regeneration. The total carbon stored in the forest ranged between 6.3 Mg C ha-1 and 835.6 Mg C ha-1. This value is equivalent to 639.6 Mg C ha-1. The forest had a very low number of woody species composition and diversity. The regeneration study also revealed that a significant number of tree species had unsatisfactory regeneration status. Besides, the forest had a lower carbon stock density compared with other dry Afromontane forests. This implies the urgent need for forest conservation and restoration activities by the local government, conservation practitioners, and other concerned bodies to maintain the forest and sustain the various ecosystem goods and services provided by the Arbagugu forest.

Keywords: aboveground biomass, forest regeneration, climate change, biodiversity conservation, restoration

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Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

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Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

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Authors: Jiwan Kumar, Pooja, Sandeep Negi, Anjum Rouf, Amit Kumar, Naveen Lakra

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In modern times where, health issues are increasing day by day, breast cancer is also one of them, which is very crucial and really important to find in the early stages. Doctors can use this model in order to tell their patients whether a cancer is not harmful (benign) or harmful (malignant). We have used the knowledge of machine learning in order to produce the model. we have used algorithms like Logistic Regression, Random forest, support Vector Classifier, Bayesian Network and Radial Basis Function. We tried to use the data of crucial parts and show them the results in pictures in order to make it easier for doctors. By doing this, we're making ML better at finding breast cancer, which can lead to saving more lives and better health care.

Keywords: Bayesian network, radial basis function, ensemble learning, understandable, data making better, random forest, logistic regression, breast cancer

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Authors: Naduni Ranasinghe

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E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

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Authors: Prem Bahadur Giri

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The nationalization of forests during the early 1960s had become counterproductive for the conservation of forests in Nepal. Realizing this fact, the Government of Nepal initiated a paradigm shift from a government-controlled forestry system to people’s direct participation in managing forestry, conceptualizing a community forest approach in the early 1980s. The community forestry approach is expected to promote sustainable forest management, restoring degraded forests to enhance the forest condition on the one hand, and on the other, improvement of livelihoods, particularly of low-income people and forest-dependent communities, as well as promoting community ownership of a forest. As a result, the establishment of community forests started and had taken faster momentum in Nepal. Of the total land in Nepal, forest occupies 6.5 million hectares which are around 45 percent of the forest area. Of the total forest area, 1.8 million hectares have been handed over to community management. A total of 19,361 ‘community forest users groups’ are already created to manage the community forest. To streamline the governance of community forests, the enactment of ‘The Forest Act 1993’ provides a clear legal basis for managing community forests in Nepal. This article is based on an in-depth study taking the case of Tarpakha Community Forest (TCF) located in Siranchok Rural Municipality of Gorkha District in Nepal. It mainly discusses the extent to which the TCF is able to achieve the twin objectives of this community forest for catalyzing socio-economic improvement of the targeted community and conservation of the forest. The primary information was generated through in-depth interviews along with group discussions with members, the management committee, and other relevant stakeholders. The findings reveal that there is a significant improvement in the regeneration of the forest and also changes in the socio-economic status of the local community. However, coordination with local municipalities and forest governing entities is still weak.

Keywords: community forest, socio-economic benefit, sustainable forest management, Nepal

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Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

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Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula

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The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.

Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting

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Authors: K. Lalhmingsangi, U. K. Sahoo

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Non-Timber Forest Products represent one of the key sources of income and subsistence to the fringe communities living in rural areas. A study was conducted for the assessment of NTFP within the community forest of five villages under Thenzawl forest division. Participatory Rural Appraisal (PRA), questionnaire, field exercise, discussion and interview with the first hand NTFP exploiter and sellers was adopted for the field study. Fuel wood, medicinal plants, fodder, wild vegetables, fruits, broom grass, thatch grass, bamboo pole and cane species are the main NTFP harvested from the community forest. Among all the NTFPs, the highest percentage of household involvement was found in fuel wood, i.e. 53% of household and least in medicinal plants 5%. They harvest for their own consumption as well as for selling to the market to meet their needs. Edible food and fruits are sold to the market and it was estimated that 300 (Rs/hh/yr) was earned by each household through the selling of this NTFP from the community forest alone. No marketing channels are linked with fuelwood, medicinal plants and fodder since they harvest only for their own consumption.

Keywords: community forest, subsistence, non-timber forest products, Thenzawl Forest Division

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Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

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Authors: Abdul Mutolib, Yonariza Mahdi, Hanung Ismono

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The existence of forests is essential to human lives on earth, but its existence is threatened by forest deforestations and degradations. Forest deforestations and degradations in Indonesia is not only caused by the illegal activity by the company or the like, even today many cases in Indonesia forest damage caused by human activities, one of which cut down forests for agriculture and plantations. In West Sumatra, community forest management are the result supported the enactment of customary land tenure, including ownership of land within the forest. Indigenous forest management have a positive benefit, which gives the community an opportunity to get livelihood and income, but if forest management practices by indigenous peoples is not done wisely, then there is the destruction of forests and cause adverse effects on the environment. Based on intensive field works in Dhamasraya District employing some data collection techniques such as key informant interviews, household surveys, secondary data analysis, and satellite image interpretation. This paper answers the following questions; how the impact of forest management by local communities on forest conditions (foccus in Forest Production and Limited Production Forest) and knowledge of the local community on the benefits of forests. The site is a Nagari Bonjol, Dharmasraya District, because most of the forest in Dharmasraya located and owned by Nagari Bonjol community. The result shows that there is damage to forests in Dharmasraya because of forest management activities by local communities. Damage to the forest area of 33,500 ha in Dharmasraya because forests are converted into oil palm and rubber plantations with monocultures. As a result of the destruction of forests, water resources are also diminishing, and the community has experienced a drought in the dry season due to forest cut down and replaced by oil palm plantations. Knowledge of the local community on the benefits of low forest, the people considered that the forest does not have better benefits and cut down and converted into oil palm or rubber plantations. Local people do not understand the benefits of ecological and environmental services that forests. From the phenomena in Dharmasraya on land ownership, need to educate the local community about the importance of protecting the forest, and need a strategy to integrate forests management to keep the ecological functions that resemble the woods and counts the economic benefits for the welfare of local communities. One alternative that can be taken is to use forest management models agroforestry smallholders in accordance with the characteristics of the local community who still consider the economic, social and environmental.

Keywords: community, customary land, farmer plantations, and forests

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Authors: Mohammad Anwar, Shah Waliullah

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This study investigated panel data regression models. This paper used Bayesian and classical methods to study the impact of institutions on economic growth from data (1990-2014), especially in developing countries. Under the classical and Bayesian methodology, the two-panel data models were estimated, which are common effects and fixed effects. For the Bayesian approach, the prior information is used in this paper, and normal gamma prior is used for the panel data models. The analysis was done through WinBUGS14 software. The estimated results of the study showed that panel data models are valid models in Bayesian methodology. In the Bayesian approach, the effects of all independent variables were positively and significantly affected by the dependent variables. Based on the standard errors of all models, we must say that the fixed effect model is the best model in the Bayesian estimation of panel data models. Also, it was proved that the fixed effect model has the lowest value of standard error, as compared to other models.

Keywords: Bayesian approach, common effect, fixed effect, random effect, Dynamic Random Effect Model

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Authors: Saurabh Shukla, G. N. Pandey

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The fires have become the most serious disasters to forest resources and the human environment. In recent years, due to climate change, human activities and other factors the frequency of forest fires has increased considerably. The monitoring and prevention of forest fires have now become a global concern for forest fire prevention organizations. Currently, the methods for forest fire prevention largely consist of patrols, observation from watch towers. Thus, software like deployment of the wireless sensor network to prevent forest fire is being developed to get a better estimate of the temperature and humidity prospects. Now days, wireless sensor networks are beginning to be deployed at an accelerated pace. It is not unrealistic to expect that in coming years the world will be covered with wireless sensor networks. This new technology has lots of unlimited potentials and can be used for numerous application areas including environmental, medical, military, transportation, entertainment, crisis management, homeland defense, and smart spaces.

Keywords: deployment, sensors, wireless sensor networks, forest fires

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Authors: Semachew M. Kassa, Africa M Geremew, Tezera F. Azmatch, Nandyala Darga Kumar

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Frequency ratio (FR) and analytical hierarchy process (AHP) methods are developed based on past landslide failure points to identify the landslide susceptibility mapping because landslides can seriously harm both the environment and society. However, it is still difficult to select the most efficient method and correctly identify the main driving factors for particular regions. In this study, we used fourteen landslide conditioning factors (LCFs) and five soft computing algorithms, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Artificial Neural Network (ANN), and Naïve Bayes (NB), to predict the landslide susceptibility at 12.5 m spatial scale. The performance of the RF (F1-score: 0.88, AUC: 0.94), ANN (F1-score: 0.85, AUC: 0.92), and SVM (F1-score: 0.82, AUC: 0.86) methods was significantly better than the LR (F1-score: 0.75, AUC: 0.76) and NB (F1-score: 0.73, AUC: 0.75) method, according to the classification results based on inventory landslide points. The findings also showed that around 35% of the study region was made up of places with high and very high landslide risk (susceptibility greater than 0.5). The very high-risk locations were primarily found in the western and southeastern regions, and all five models showed good agreement and similar geographic distribution patterns in landslide susceptibility. The towns with the highest landslide risk include Amhara Saint Town's western part, the Northern part, and St. Gebreal Church villages, with mean susceptibility values greater than 0.5. However, rainfall, distance to road, and slope were typically among the top leading factors for most villages. The primary contributing factors to landslide vulnerability were slightly varied for the five models. Decision-makers and policy planners can use the information from our study to make informed decisions and establish policies. It also suggests that various places should take different safeguards to reduce or prevent serious damage from landslide events.

Keywords: artificial neural network, logistic regression, landslide susceptibility, naïve Bayes, random forest, support vector machine

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Authors: Osayande Pascal Omondiagbe, Sherlock a Licorish

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Stack Overflow is a popular community question and answer portal which is used by practitioners to solve technology-related challenges during software development. Previous studies have shown that this forum is becoming a substitute for official software programming languages documentation. While tools have looked to aid developers by presenting interfaces to explore Stack Overflow, developers often face challenges searching through many possible answers to their questions, and this extends the development time. To this end, researchers have provided ways of predicting acceptable Stack Overflow answers by using various modeling techniques. However, less interest is dedicated to examining the performance and quality of typically used modeling methods, and especially in relation to models’ and features’ complexity. Such insights could be of practical significance to the many practitioners that use Stack Overflow. This study examines the performance and quality of various modeling methods that are used for predicting acceptable answers on Stack Overflow, drawn from 2014, 2015 and 2016. Our findings reveal significant differences in models’ performance and quality given the type of features and complexity of models used. Researchers examining classifiers’ performance and quality and features’ complexity may leverage these findings in selecting suitable techniques when developing prediction models.

Keywords: feature selection, modeling and prediction, neural network, random forest, stack overflow

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Authors: Suzan Anwar, Utkarsh Goyal

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Heart rate (HR) is a predictor of cardiovascular, cerebrovascular, and all-cause mortality in the general population, as well as in patients with cardio and cerebrovascular diseases. Machine learning (ML) significantly improves the accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment while avoiding unnecessary treatment of others. This research examines relationship between the individual's various heart health inputs like age, sex, cp, trestbps, thalach, oldpeaketc, and the likelihood of developing heart disease. Machine learning techniques like logistic regression and decision tree, and Python are used. The results of testing and evaluating the model using the Heart Failure Prediction Dataset show the chance of a person having a heart disease with variable accuracy. Logistic regression has yielded an accuracy of 80.48% without data handling. With data handling (normalization, standardscaler), the logistic regression resulted in improved accuracy of 87.80%, decision tree 100%, random forest 100%, and SVM 100%.

Keywords: heart rate, machine learning, SVM, decision tree, logistic regression, random forest

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Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: color space, neural network, random forest, skin detection, statistical feature

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Authors: Huong Nguyen Thi Thanh, Van Mai Thi Khanh

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For around the world, payments for environmental services have been implemented since the late 1970s in Europe and North America; then, it was spread to Latin America, Asia, Africa, and finally Oceania in 2008. In Vietnam, payments for environmental services are an interesting issue recently with the forest as the main focus and therefore known as the program on payment for forest environmental services (PFES). PFES was piloted in Lam Dong and Son La in 2008 and has been widely applied in many provinces after 2010. PFES is in the orientation for the socialization of national forest protection in Vietnam and has made great strides in the last decade. By using the primary data and secondary data simultaneously, the paper clarifies two cases of implementing PFES in the Vietnam North Central area with the different mechanisms of payment. In the first case at Phu Loc district (Thua Thien Hue province), PFES is an indirect method by a water supply company via the Forest Protection and Development Fund. In the second one at Phong Nha – Ke Bang National Park (Quang Binh Province), tourism companies are the direct payers to forest owners. The paper describes the PFES implementation process at each site, clarifies the payment mechanism, and models the relationship between stakeholders in PFES implementation. Based on the current status of PFES sites, the paper compares and analyzes the advantages and disadvantages of the two payment methods. Finally, the paper proposes recommendations to improve the existing shortcomings in each payment mechanism.

Keywords: advantages and disadvantages, forest environmental services, forest protection, payment mechanism

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: Ahmed A. A. Fadol, Guangji Tong, Wlaa Mohamed

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This paper provides a comprehensive analysis of food insecurity in Sudan, focusing on consumption patterns and their implications, employing the Integrated Food Security Phase Classification (IPC) assessment framework. Years of conflict and economic instability have driven large segments of the population in Sudan into crisis levels of acute food insecurity according to the (IPC). A substantial number of people are estimated to currently face emergency conditions, with an additional sizeable portion categorized under less severe but still extreme hunger levels. In this study, we explore the multifaceted nature of food insecurity in Sudan, considering its historical, political, economic, and social dimensions. An analysis of consumption patterns and trends was conducted, taking into account cultural influences, dietary shifts, and demographic changes. Furthermore, we employ logistic regression and random forest analysis to identify significant independent variables influencing food security status in Sudan. Random forest clearly outperforms logistic regression in terms of area under curve (AUC), accuracy, precision and recall. Forward projections of the IPC for Sudan estimate that 15 million individuals are anticipated to face Crisis level (IPC Phase 3) or worse acute food insecurity conditions between October 2023 and February 2024. Of this, 60% are concentrated in Greater Darfur, Greater Kordofan, and Khartoum State, with Greater Darfur alone representing 29% of this total. These findings emphasize the urgent need for both short-term humanitarian aid and long-term strategies to address Sudan's deepening food insecurity crisis.

Keywords: food insecurity, consumption patterns, logistic regression, random forest analysis

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Authors: Mark Joseph Quinto, Roan Beronilla, Guiller Damian, Eliza Camaso, Ronaldo Alberto

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Forest inventories are essential to assess the composition, structure and distribution of forest vegetation that can be used as baseline information for management decisions. Classical forest inventory is labor intensive and time-consuming and sometimes even dangerous. The use of Light Detection and Ranging (LiDAR) in forest inventory would improve and overcome these restrictions. This study was conducted to determine the possibility of using LiDAR derived data in extracting high accuracy forest biophysical parameters and as a non-destructive method for forest status analysis of San Manual, Pangasinan. Forest resources extraction was carried out using LAS tools, GIS, Envi and .bat scripts with the available LiDAR data. The process includes the generation of derivatives such as Digital Terrain Model (DTM), Canopy Height Model (CHM) and Canopy Cover Model (CCM) in .bat scripts followed by the generation of 17 composite bands to be used in the extraction of forest classification covers using ENVI 4.8 and GIS software. The Diameter in Breast Height (DBH), Above Ground Biomass (AGB) and Carbon Stock (CS) were estimated for each classified forest cover and Tree Count Extraction was carried out using GIS. Subsequently, field validation was conducted for accuracy assessment. Results showed that the forest of San Manuel has 73% Forest Cover, which is relatively much higher as compared to the 10% canopy cover requirement. On the extracted canopy height, 80% of the tree’s height ranges from 12 m to 17 m. CS of the three forest covers based on the AGB were: 20819.59 kg/20x20 m for closed broadleaf, 8609.82 kg/20x20 m for broadleaf plantation and 15545.57 kg/20x20m for open broadleaf. Average tree counts for the tree forest plantation was 413 trees/ha. As such, the forest of San Manuel has high percent forest cover and high CS.

Keywords: carbon stock, forest inventory, LiDAR, tree count

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Authors: Siamack A. Shirazi, Farzin Darihaki

Abstract:

Solid particles could be found in many multiphase flows, including transport pipelines and pipe fittings. Such particles interact with the pipe material and cause erosion which threats the integrity of the system. Therefore, predicting the erosion rate is an important factor in the design and the monitor of such systems. Mechanistic models can provide reliable predictions for many conditions while demanding only relatively low computational cost. Mechanistic models utilize a representative particle trajectory to predict the impact characteristics of the majority of the particle impacts that cause maximum erosion rate in the domain. The erosion caused by particle impacts is not only due to the direct impacts but also random impingements. In the present study, an alternative model has been introduced to describe the erosion due to random impingement of particles. The present model provides a realistic trend for erosion with changes in the particle size and particle Stokes number. The present model is examined against the experimental data and CFD simulation results and indicates better agreement with the data incomparison to the available models in the literature.

Keywords: erosion, mechanistic modeling, particles, multiphase flow, gas-liquid-solid

Procedia PDF Downloads 147

Authors: Kan Yu, Khui Hung Lee, Eben Afrifa-Yamoah, Jing Guo, Katrina Harrison, Jack Goldblatt, Nicholas Pachter, Jitian Xiao, Guicheng Brad Zhang

Abstract:

Background and Significance of the Study: Congenital Heart Defects (CHDs) are the most common malformation at birth and one of the leading causes of infant death. Although the exact etiology remains a significant challenge, epigenetic modifications, such as DNA methylation, are thought to contribute to the pathogenesis of congenital heart defects. At present, no existing DNA methylation biomarkers are used for early detection of CHDs. The existing CHD diagnostic techniques are time-consuming and costly and can only be used to diagnose CHDs after an infant was born. The present study employed a machine learning technique to analyse genome-wide methylation data in children with and without CHDs with the aim to find methylation biomarkers for CHDs. Methods: The Illumina Human Methylation EPIC BeadChip was used to screen the genome‐wide DNA methylation profiles of 24 infants diagnosed with congenital heart defects and 24 healthy infants without congenital heart defects. Primary pre-processing was conducted by using RnBeads and limma packages. The methylation levels of top 600 genes with the lowest p-value were selected and further investigated by using a random forest approach. ROC curves were used to analyse the sensitivity and specificity of each biomarker in both training and test sample sets. The functionalities of selected genes with high sensitivity and specificity were then assessed in molecular processes. Major Findings of the Study: Three genes (MIR663, FGF3, and FAM64A) were identified from both training and validating data by random forests with an average sensitivity and specificity of 85% and 95%. GO analyses for the top 600 genes showed that these putative differentially methylated genes were primarily associated with regulation of lipid metabolic process, protein-containing complex localization, and Notch signalling pathway. The present findings highlight that aberrant DNA methylation may play a significant role in the pathogenesis of congenital heart defects.

Keywords: biomarker, congenital heart defects, DNA methylation, random forest

Procedia PDF Downloads 135

Authors: Dung Nguyen, Yu Wei, Eric Henderson

Abstract:

Analytical tools have been used for decades to assist in the development of forest plans. In 2016, a new decision support system, PRISM, was jointly developed by United States Forest Service (USFS) Northern Region and Colorado State University to support the forest planning process. Prism has a friendly user interface with functionality for database management, model development, data visualization, and sensitivity analysis. The software is tailored for USFS planning, but it is flexible enough to support planning efforts by other forestland owners and managers. Here, the core capability of PRISM and its applications in developing plans for several United States national forests are presented. The strengths of PRISM are also discussed to show its potential of being a preferable tool for managers and experts in the domain of forest management and planning.

Keywords: decision support, forest management, forest plan, graphical user interface, software

Procedia PDF Downloads 86

Authors: Melvin Ballera, Aldrich Olivar, Mary Soriano

Abstract:

Digital computers nowadays must be able to have a utility that is capable of generating random numbers. Usually, computer-generated random numbers are not random given predefined values such as starting point and end points, making the sequence almost predictable. There are many applications of random numbers such business simulation, manufacturing, services domain, entertainment sector and other equally areas making worthwhile to design a unique method and to allow unpredictable random numbers. Applying stochastic simulation using linear congruential algorithm, it shows that as it increases the numbers of the seed and range the number randomly produced or selected by the computer becomes unique. If this implemented in an environment where random numbers are very much needed, the reliability of the random number is guaranteed.

Keywords: stochastic simulation, random numbers, linear congruential algorithm, pseudorandomness

Procedia PDF Downloads 288

Authors: Mohammed Sghir Taleb

Abstract:

Forest ecosystems in Morocco are subject increasingly to natural and human pressures. Conscious of this problem, Morocco set a strategy that focuses on programs of in-situ and ex-situ biodiversity conservation. This study is the result of a synthesis of various existing studies on biodiversity and forest ecosystems. It gives an overview of Moroccan mountain forest ecosystems and flora diversity. It also focuses on the efforts made by Morocco to conserve and sustainably manage biodiversity.

Keywords: mountain, ecosystems, conservation, Morocco

Procedia PDF Downloads 554