Search results for: logistic model tree

Authors: Harminder Singh, Rupinder Kaur

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Peach is primarily a temperate fruit, but its low chilling cultivars are grown quite successfully in the sub-tropical climate as well. The area under peach cultivation is picking up rapidly in the sub tropics of northern India due to higher return on a unit area basis, availability of suitable peach cultivar and their production technology. Information on the use of different training systems on peach in the sub tropics is inadequate. In this investigation, conducted at Punjab Agricultural University, Ludhiana (Punjab), India, the trees of the Shan-i-Punjab peach were planted at four different spacings i.e. 6.0x3.0m, 6.0x2.5m, 4.5x3.0m and 4.5x2.5m and were trained to central leader system. The total radiation interception and penetration in the upper and lower canopy parts were higher in 6x3.0m and 6x2.5m planted trees as compared to other spacings. Average radiation interception was maximum in the upper part of the tree canopy, and it decreased significantly with the depth of the canopy in all the spacings. Tree planted at wider spacings produced more vegetative (tree height, tree girth, tree spread and canopy volume) and reproductive growth (flower bud density, number of fruits and fruit yield) per tree but productivity was maximum in the closely planted trees. Fruits harvested from the wider spaced trees were superior in fruit quality (size, weight, colour, TSS and acidity) and matured earlier than those harvested from closed spaced trees.

Keywords: quality, radiation, spacings, yield

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Authors: Jitin Rahul, Manish Kumar Jain

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Dust pollution refers to the various locations, activities, or factors which are responsible for the releasing of pollutants into the atmosphere. The sources of dust can be classified into two major categories anthropogenic sources (man-made sources) and natural sources. Dust kicked up by heavy vehicles (Bus, Truck, Loaders, Tankers, car etc.) travelling on highways may make up approximately 33-40% of air pollution. Plants naturally cleanse the atmosphere by absorbing gases and particulate matter plants (Leaves). Plants are very good pollution indicator and also very good for dust capturing (Dust controlling). Many types tree species like Azadirachta indica A. juss, Butea monosperma (Lam.) Kuntz., Ficus bengalensis (Linn)., Pterocarpus marspium (Roxb.), Terminalia arjuna (Roxb, exDC.), Dalbergia sissoo roxb., and Ficus religiosa (Linn.) generally occur in roadside. These selected tree spiciness can control the dust pollution or dust capturing. It is well known that plants absorb particulate pollutants and help in dust controlling. Some tree species like (Ficus bengalensis, Ficus religiosa and Azadirachta indica) are very effective and natural means for controlling air pollution.

Keywords: dust, pollution, road, tree species

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Authors: Alaa Allakany, Koji Okamura

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Multicast technology is an efficient and scalable technology for data distribution in order to optimize network resources. However, in the IP network, the responsibility for management of multicast groups is distributed among network routers, which causes some limitations such as delays in processing group events, high bandwidth consumption and redundant tree calculation. Software Defined Networking (SDN) represented by OpenFlow presented as a solution for many problems, in SDN the control plane and data plane are separated by shifting the control and management to a remote centralized controller, and the routers are used as a forwarder only. In this paper we will proposed fast switching mechanism for solving the problem of link failure in multicast tree based on Tabu Search heuristic algorithm and modifying the functions of OpenFlow switch to fasts switch to the pack up sub tree rather than sending to the controller. In this work we will implement multicasting OpenFlow controller, this centralized controller is a core part in our multicasting approach, which is responsible for 1- constructing the multicast tree, 2- handling the multicast group events and multicast state maintenance. And finally modifying OpenFlow switch functions for fasts switch to pack up paths. Forwarders, forward the multicast packet based on multicast routing entries which were generated by the centralized controller. Tabu search will be used as heuristic algorithm for construction near optimum multicast tree and maintain multicast tree to still near optimum in case of join or leave any members from multicast group (group events).

Keywords: multicast tree, software define networks, tabu search, OpenFlow

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Authors: Sengul Uysal, Gokhan Zengin, Abdurrahman Aktumsek, Sukru Karatas

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In this research, we determined the total sugar content, fatty acid compositions and α-amylase and α-glucosidase inhibitory activity of methanolic and water extracts of nine different fruit tree leaves. α-amylase and α-glycosidase inhibitory activity were determined by using Caraway-Somogyi–iodine/potassium iodide (IKI) and 4-nitrophenyl-α-D-glucopyranoside (PNPG) as substrate, respectively. Total sugar content of the nine different fruit tree leaves varies from 281.02 mg GE/g (glucose equivalents) to 643.96 mg GE/g. Methanolic extract from avocado leaves had the strongest in α-amylase and α-glucosidase inhibitory activity, 69.21% and 96.26 %, respectively. Fatty acid composition of nine fruit tree leaves was characterized by GC (gas chromatography) and twenty-four components were identified. Among the tested fruit tree leaves, the main component was linolenic acid (49.09%). The level of essential fatty acids are over 50% in mulberry, grape and loquat leaves. PUFAs (polyunsaturated fatty acids) were major group of fatty acids present in oils of mulberry, fig, pomegranate, grape, and loquat leaves. Therefore, these oils can be considered as a good source of polyunsaturated fatty acids. Furthermore, avocado can be regarded as a new source for diabetic therapies.

Keywords: fatty acid compositions, total sugar contents, α-amylase, α-glucosidase, fruit tree leaves, Turkey

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Authors: Mohamed H. El-Sheikh, Hoda F. Zahran

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Olive tree (Olea europaea L.) is considered as a Mediterranean tree which belongs to genus Olea that may comprise about 35 species. In fact, the crop requires mild to cool winters with a chilling accumulation from November to February with average temperatures varying between two groups of accumulated chilling hours (h1) of less than 7.2 °C (C1) and other group (h2) of less than 10 °C (C2) for flower bud differentiation. This work aims at studying the impact of chilling accumulation hours on the fruit yield of olive trees in Borg El Arab City, Alexandria Governorate, Egypt as a case study. Trees were aged around 7 years in 2010 and were exposed to chilling accumulation hours of h1, which was average of 280 hours under C1, and average h2 was around 150 hours under C2 the resulted fruit yield was around 0.5 kg/tree. On the hand, trees were aged around 7 years at 2016 showed that when average of h1 was around 390 hours under C1 and average h2 was around 220 hours under C2 then fruit yield was around 10 kg/tree. Increasing of fruit yield proved chilling accumulation effect on olive trees.

Keywords: chilling accumulation, fruit yield, Olea europaea, olive

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Authors: Srinivasa Rao Gadde

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In this article, we considered a group acceptance sampling plans for exponentiated half logistic distribution when the life-test is truncated at a pre-specified time. It is assumed that the index parameter of the exponentiated half logistic distribution is known. The design parameters such as the number of groups and the acceptance number are obtained by satisfying the producer’s and consumer’s risks at the specified quality levels in terms of medians and 10th percentiles under the assumption that the termination time and the number of items in each group are pre-fixed. Finally, an example is given to illustration the methodology.

Keywords: group acceptance sampling plan, operating characteristic, consumer and producer’s risks, truncated life-test

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Authors: Yousef Abu Nahleh, Arun Kumar, Fugen Daver, Reham Al-Hindawi

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Despite the availability of natural disaster related time series data for last 110 years, there is no forecasting tool available to humanitarian relief organizations to determine forecasts for emergency logistics planning. This study develops a forecasting tool based on identifying probability of disaster for each country in the world by using decision tree modeling. Further, the determination of aggregate forecasts leads to efficient pre-disaster planning. Based on the research findings, the relief agencies can optimize the various resources allocation in emergency logistics planning.

Keywords: decision tree modeling, forecasting, humanitarian relief, emergency supply chain

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Authors: Andrea Gion Saluz, Tal Hertig, Axel Heinrich, Stefan Stevanovic

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Higher temperatures, seasonal changes in precipitation, and extreme weather events are increasingly affecting trees. To counteract the increasing challenges of urban trees, strategies are increasingly being sought to preserve existing tree populations on the one hand and to prepare for the coming years on the other. One such strategy lies in strategic climate tree species selection. The search is on for species or varieties that can cope with the new climatic conditions. Many efforts in German-speaking countries deal with this in detail, such as the tree lists of the German Conference of Garden Authorities (GALK), the project Stadtgrün 2021, or the instruments of the Climate Species Matrix by Prof. Dr. Roloff. In this context, different methods for a correct species selection are offered. One possibility is to select certain physiological attributes that indicate the climate resilience of a species. To calculate the dissimilarity of the present climate of different geographic regions in relation to the future climate of any city, a weighted (standardized) Euclidean distance (SED) for seasonal climate values is calculated for each region of the Earth. The calculation was performed in the QGIS geographic information system, using global raster datasets on monthly climate values in the 1981-2010 standard period. Data from a European forest inventory were used to identify tree species growing in the calculated analogue climate regions. The inventory used is the compilation of georeferenced point data at a 1 km grid resolution on the occurrence of tree species in 21 European countries. In this project, the results of the methodological application are shown for the city of Zurich for the year 2060. In the first step, analog climate regions based on projected climate values for the measuring station Kirche Fluntern (ZH) were searched for. In a further step, the methods mentioned above were applied to generate tree species lists for the city of Zurich. These lists were then qualitatively evaluated with respect to the suitability of the different tree species for the Zurich area to generate a cleaned and thus usable list of possible future tree species.

Keywords: climate change, climate region, climate tree, urban tree

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Authors: Raymond Awinbilla

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The call for tree planting has long been set in motion by the government of Ghana. The Forestry Commission encourages plantation development through numerous interventions including formulating policies and enacting legislations. However, forest policies have failed and that has generated a major concern over the vast gap between the intentions of national policies and the realities established. This study addresses three objectives;1) Assessing the farmers' response and contribution to the tree planting initiative, 2) Identifying socio-economic factors hindering the development of smallholder plantations as a livelihood strategy, and 3) Determining the level of support available for smallholder tree growers and the factors influencing it. The field work was done in 12 farming communities in Ghana. The article illuminates that farmers have responded to the call for tree planting and have planted both exotic and indigenous tree species. Farmers have converted 17.2% (369.48ha) of their total land size into plantations and have no problem with land tenure. Operations and marketing constraints include lack of funds for operations, delay in payment, low price of wood, manipulation of price by buyers, documentation by buyers, and no ready market for harvesting wood products. Environmental institutions encourage tree planting; the only exception is with the Lands Commission. Support availed to farmers includes capacity building in silvicultural practices, organisation of farmers, linkage to markets and finance. Efforts by the Government of Ghana to enhance forest resources in the country could rely on the input of local populations.

Keywords: livelihood strategy, marketing constraints, environmental institutions, silvicultural practices

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Authors: Larry V. Aceres, Jesryl B. Paulite, Emelie M. Pelicano, J. A. Esteban, Mamangun

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Citrus rind borer still the most important pest infesting pummelo in the Philippines particularly in the Davao region. Hence, management of the pest is very important for successful pummelo production. This study was conducted to assess the effectiveness of the different control strategies against citrus rind borer; to determine the best treatment in controlling citrus rind borer; and to calculate the profitability of the various treatments in pummelo production. The experiment was laid-out in Completely Randomized Design (CRD) with five treatments replicated three times. The treatments were: T1- curry tree leaf leachate, T2- neem tree leaf leachate, T3- bagging with an ordinary net, T4- treated check (chlorpyrifos & betacyflutrin) and T5- untreated check. Data were analyzed using the Analysis of Variance and the differences among treatment means were computed using the Tukey’s Honest Significant Difference. The results of the study revealed that the curry tree leaf leachate and bagging treatments provide significant protection to the pummelo fruits which is comparable with the treated check (chlorpyrifos & betacyflutrin). Neem tree leaf leachate is not effective in controlling citrus rind borer which is comparable with the untreated check. In cost and return analysis, the most economical and effective is the bagging treatment using ordinary net.

Keywords: curry tree, neem tree, bagging, citrus rind borer

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Authors: Margareth E. Bolla, Ludfi Djakfar, Achmad Wicaksono

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The increase in the number of motorcycle users in Indonesia is in line with the increase in accidents involving motorcycles. Several previous studies have shown that humans are the biggest factor causing accidents, and the driver's personality factor will affect his behavior on the road. This study was conducted to see how a person's personality traits will affect the probability of having an accident while driving. The Big Five Inventory (BFI) questionnaire and the Honda Riding Trainer (HRT) simulator were used as measuring tools, while the analysis carried out was logistic regression analysis. The results of the descriptive analysis of the respondent's personality based on the BFI show that the majority of drivers have the dominant character of neuroticism (34%), while the smallest group is the driver with the dominant type of openness character (6%). The percentage of motorists who were not involved in an accident was 54%. The results of the logistic regression analysis form a mathematical model as follows Y = -3.852 - 0.288 X1 + 0.596 X2 + 0.429 X3 - 0.386 X4 - 0.094 X5 + 0.436 X6 + 0.162 X7, where the results of hypothesis testing indicate that the variables openness, conscientiousness, extraversion, agreeableness, neuroticism, history of traffic accidents and age at starting driving did not have a significant effect on the probability of a motorcyclist being involved in an accident.

Keywords: accidents, BFI, probability, simulator

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Authors: Misrak Assefa

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The Amharic language is the second most widely spoken Semitic language in the world. There are several new overloaded on the web. Searching some useful documents from the web on a specific topic, which is written in the Amharic language, is a challenging task. Hence, document categorization is required for managing and filtering important information. In the classification of Amharic text news, there is still a gap in the domain of information that needs to be launch. This study attempts to design an automatic Amharic news classification using a supervised learning mechanism on four un-touch classes. To achieve this research, 4,182 news articles were used. Naive Bayes (NB) and Decision tree (j48) algorithms were used to classify the given Amharic dataset. In this paper, k-fold cross-validation is used to estimate the accuracy of the classifier. As a result, it shows those algorithms can be applicable in Amharic news categorization. The best average accuracy result is achieved by j48 decision tree and naïve Bayes is 95.2345 %, and 94.6245 % respectively using three categories. This research indicated that a typical decision tree algorithm is more applicable to Amharic news categorization.

Keywords: text categorization, supervised machine learning, naive Bayes, decision tree

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Authors: Ashish Dhamaniya, Vineet Jain, Rajesh Chouhan

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Traffic management is one of the gigantic issue in most of the urban roads in al-most all metropolitan cities in India. Speed is one of the critical traffic parameters for effective Intelligent Transportation System (ITS) implementation as it decides the arrival rate of vehicles on an intersection which are majorly the point of con-gestions. The study aimed to leverage Machine Learning (ML) models to produce precise predictions of speed on urban roadway links. The research objective was to assess how categorized traffic volume and road width, serving as variables, in-fluence speed prediction. Four tree-based regression models namely: Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Extreme Gradient Boost (XGB)are employed for this purpose. The models' performances were validated using test data, and the results demonstrate that Random Forest surpasses other machine learning techniques and a conventional utility theory-based model in speed prediction. The study is useful for managing the urban roadway network performance under mixed traffic conditions and effective implementation of ITS.

Keywords: stream speed, urban roads, machine learning, traffic flow

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Authors: Kavyadevi N., Kaviya G., Gowsalya P., Janani M., Mohanraj S.

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Hevea brasiliensis, also known as the rubber tree, is one of the foremost assets of crops in the world. One of the most significant advantages of the Rubber Plant in terms of air oxygenation is its capacity to reduce the likelihood of an individual developing respiratory allergies like asthma. To construct such a system that can properly identify crop diseases and pests and then create a database of insecticides for each pest and disease, we must first give treatment for the illness that has been detected. We shall primarily examine three major leaf diseases since they are economically deficient in this article, which is Bird's eye spot, algal spot and powdery mildew. And the recommended work focuses on disease identification on rubber tree leaves. It will be accomplished by employing one of the superior algorithms. Input, Preprocessing, Image Segmentation, Extraction Feature, and Classification will be followed by the processing technique. We will use time-consuming procedures that they use to detect the sickness. As a consequence, the main ailments, underlying causes, and signs and symptoms of diseases that harm the rubber tree are covered in this study.

Keywords: image processing, python, convolution neural network (CNN), machine learning

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

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

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

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Authors: Ing-Jen Hung, Tzu-Chien Wang

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Nowadays, customers can interact with firms through miscellaneous online ads on different channels easily. In other words, customer now has innumerable options and limitless time to accomplish their commercial activities with firms, individualizing their own online customer journey. This kind of convenience emphasizes the importance of online advertisement allocation on different channels. Therefore, profound understanding of customer behavior can make considerable benefit from optimizing fund allocation on diverse ad channels. To achieve this objective, multiple firms utilize numerical methodology to create data-driven advertisement policy. In our research, we aim to exploit online customer click data to discover the correlations between each channel and their sequential relations. We use LSTM to deal with sequential property of our data and compare its accuracy with other non-sequential methods, such as CART decision tree, logistic regression, etc. Besides, we also classify our customers into several groups by their behavioral characteristics to perceive the differences between all groups as customer portrait. As a result, we discover distinct customer journey under each customer portrait. Our article provides some insights into marketing research and can help firm to formulate online advertising criteria.

Keywords: LSTM, customer journey, marketing, channel ads

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Authors: O. J. Akinsola, Yinan Zheng, Rose Anorlu, F. T. Ogunsola, Lifang Hou, Robert Leo-Murphy

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Cervical Cancer (CC) is the 2nd most common cancer among women living in low and middle-income countries, with no associated symptoms during formative periods. With the advancement and innovative medical research, there are numerous preventive measures being utilized, but the incidence of cervical cancer cannot be truncated with the application of only screening tests. The mortality associated with this invasive cervical cancer can be nipped in the bud through the important role of early-stage detection. This study research selected an array of different top features selection techniques which was aimed at developing a model that could validly diagnose the risk factors of cervical cancer. A retrospective clinic-based cohort study was conducted on 178 HIV-associated cervical cancer patients in Lagos University teaching Hospital, Nigeria (U54 data repository) in April 2022. The outcome measure was the automated prediction of the HIV-associated cervical cancer cases, while the predictor variables include: demographic information, reproductive history, birth control, sexual history, cervical cancer screening history for invasive cervical cancer. The proposed technique was assessed with R and Python programming software to produce the model by utilizing the classification algorithms for the detection and diagnosis of cervical cancer disease. Four machine learning classification algorithms used are: the machine learning model was split into training and testing dataset into ratio 80:20. The numerical features were also standardized while hyperparameter tuning was carried out on the machine learning to train and test the data. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Some fitting features were selected for the detection and diagnosis of cervical cancer diseases from selected characteristics in the dataset using the contribution of various selection methods for the classification cervical cancer into healthy or diseased status. The mean age of patients was 49.7±12.1 years, mean age at pregnancy was 23.3±5.5 years, mean age at first sexual experience was 19.4±3.2 years, while the mean BMI was 27.1±5.6 kg/m2. A larger percentage of the patients are Married (62.9%), while most of them have at least two sexual partners (72.5%). Age of patients (OR=1.065, p<0.001**), marital status (OR=0.375, p=0.011**), number of pregnancy live-births (OR=1.317, p=0.007**), and use of birth control pills (OR=0.291, p=0.015**) were found to be significantly associated with HIV-associated cervical cancer. On top ten 10 features (variables) considered in the analysis, RF claims the overall model performance, which include: accuracy of (72.0%), the precision of (84.6%), a recall of (84.6%) and F1-score of (74.0%) while LR has: an accuracy of (74.0%), precision of (70.0%), recall of (70.0%) and F1-score of (70.0%). The RF model identified 10 features predictive of developing cervical cancer. The age of patients was considered as the most important risk factor, followed by the number of pregnancy livebirths, marital status, and use of birth control pills, The study shows that data mining techniques could be used to identify women living with HIV at high risk of developing cervical cancer in Nigeria and other sub-Saharan African countries.

Keywords: associated cervical cancer, data mining, random forest, logistic regression

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Authors: Firouz Amani, Adalat Hoseinian, Sajjad Hakimian

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Background: The mortality due to Myocardial Infarction (MI) is often occur during the first hours after onset of symptom. So, for taking the necessary treatment and decreasing the mortality rate, timely visited of the hospital could be effective in this regard. The aim of this study was to investigate the impact of effective factors in mortality of MI patients by using Linear Logistic Regression. Materials and Methods: In this case-control study, all patients with Acute MI who referred to the Ardabil city hospital were studied. All of died patients were considered as the case group (n=27) and we select 27 matched patients without Acute MI as a control group. Data collected for all patients in two groups by a same checklist and then analyzed by SPSS version 24 software using statistical methods. We used the linear logistic regression model to determine the effective factors on mortality of MI patients. Results: The mean age of patients in case group was significantly higher than control group (75.1±11.7 vs. 63.1±11.6, p=0.001).The history of non-cardinal diseases in case group with 44.4% significantly higher than control group with 7.4% (p=0.002).The number of performed PCIs in case group with 40.7% significantly lower than control group with 74.1% (P=0.013). The time distance between hospital admission and performed PCI in case group with 110.9 min was significantly upper than control group with 56 min (P=0.001). The mean of delay time from Onset of symptom to hospital admission (patient delay) and the mean of delay time from hospital admissions to receive treatment (system delay) was similar between two groups. By using logistic regression model we revealed that history of non-cardinal diseases (OR=283) and the number of performed PCIs (OR=24.5) had significant impact on mortality of MI patients in compare to other factors. Conclusion: Results of this study showed that of all studied factors, the number of performed PCIs, history of non-cardinal illness and the interval between onset of symptoms and performed PCI have significant relation with morality of MI patients and other factors were not meaningful. So, doing more studies with a large sample and investigated other involved factors such as smoking, weather and etc. is recommended in future.

Keywords: acute MI, mortality, heart failure, arrhythmia

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Authors: K. S. Parlak, E. Turan

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The separation of marbles according to the pattern quality is a process made according to expert decision. The classification phase is the most critical part in terms of economic value. In this study, a self-learning system is proposed which performs the classification of marbles quickly and with high success. This system performs ten feature extraction by taking ten marble images from the camera. The marbles are classified by decision tree method using the obtained properties. The user forms the training set by training the system at the marble classification stage. The system evolves itself in every marble image that is classified. The aim of the proposed system is to minimize the error caused by the person performing the classification and achieve it quickly.

Keywords: decision tree, feature extraction, k-means clustering, marble classification

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Authors: Samuel Oluwafemi Oyamakin, Angela Unna Chukwu

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Richrads growth equation being a generalized logistic growth equation was improved upon by introducing an allometric parameter using the hyperbolic sine function. The integral solution to this was called hyperbolic Richards growth model having transformed the solution from deterministic to a stochastic growth model. Its ability in model prediction was compared with the classical Richards growth model an approach which mimicked the natural variability of heights/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using the coefficient of determination (R2), Mean Absolute Error (MAE) and Mean Square Error (MSE) results. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the behavior of the error term for possible violations. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic Richards nonlinear growth models better than the classical Richards growth model.

Keywords: height, diameter at breast height, DBH, hyperbolic sine function, Pinus caribaea, Richards' growth model

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Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Tesfay Gidey, Emiru Birhane, Ashenafi Manaye, Hailemariam Kassa, Tesfay Atsbha, Negasi Solomon, Hadgu Hishe, Aklilu Negussie, Petr Madera, Jose G. Borges

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The globally endangered Dracaena ombet is one of the ten dragon multipurpose tree species in arid ecosystems. Anthropogenic and natural factors are now impacting the sustainability of the species. This study was conducted to prioritize criteria and alternative strategies for the conservation of the species using the analytical hierarchy process (AHP) model by involving all relevant stakeholders in the Desa'a dry Afromontane forest in northern Ethiopia. Information about the potential alternative strategies and the criteria for their evaluation was first collected from experts, personal experiences, and literature reviews. Afterward, they were validated using stakeholders' focus group discussions. Five candidate strategies with three evaluation criteria were considered for prioritization using the AHP techniques. The overall priority ranking value of the stakeholders showed that the ecological criterion was deemed as the most essential factor for the choice of alternative strategies, followed by the economic and social criteria. The minimum cut-off strategy, combining exclosures with the collection of only 5% of plant parts from the species, soil and water conservation, and silviculture interventions, was selected as the best alternative strategy for sustainable D. ombet conservation. The livelihood losses due to the selected strategy should be compensated by the collection of non-timber forest products, poultry farming, home gardens, rearing small ruminants, beekeeping, and agroforestry. This approach may be extended to study other dragon tree species and explore strategies for the conservation of other arid ecosystems.

Keywords: conservation strategies, analytical hierarchy process model, Desa'a forest, endangered species, Ethiopia, overexploitation

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Authors: Nur Amira nor Arifin, Tashia Marie Anthony, Mohd Ruzlin Mokhtar, Huzainie Shafi Abd Halim

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Cross-linked polyethylene (XLPE) material is being used as the cable insulation for the past decades due to its higher working temperature of 90 ˚C and some other advantages. However, the use of XLPE as an insulating material for underground distribution cables may have subjected to the unforeseeable weather and uncontrollable environmental condition. These unfavorable condition when combine with high electric field may lead to the initiation and growth of water tree in XLPE insulation. There are several studies on numerous nanofillers incorporate into polymer matrix to hinder the growth of tree propagation. Hence, in this study aims to investigate the effect of MgO and rubber nanofillers at different concentration on the electrical tree of XLPE. The nanofillers and XLPE were mixed and later extruded. After extrusion, the material were then fabricated into the desired shape for experimental purposes. The result shows that the electrical tree propagation of XLPE filled with optimize concentration of nanofillers were much slower compared to pure XLPE. In this paper, the effect of nanofillers towards electrical treeing characteristic will be discussed.

Keywords: electrical trees, nanofillers, polymer nanocomposites, XLPE

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Authors: Yinglisong, Jingge Chen, Jingxuan Chen, Yan Wang, Hui Huang, Jing Zhnag, Qianqian Zhang, Zhenzhen Zhang, Ji Zhang

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Purpose: The study aims to establish a nomogram prediction model for postpartum hemorrhage (PPH) in vaginal delivery. Patients and Methods: Clinical data were retrospectively collected from vaginal delivery patients admitted to a hospital in Zhengzhou, China, from June 1, 2022 - October 31, 2022. Univariate and multivariate logistic regression were used to filter out independent risk factors. A nomogram model was established for PPH in vaginal delivery based on the risk factors coefficient. Bootstrapping was used for internal validation. To assess discrimination and calibration, receiver operator characteristics (ROC) and calibration curves were generated in the derivation and validation groups. Results: A total of 1340 cases of vaginal delivery were enrolled, with 81 (6.04%) having PPH. Logistic regression indicated that history of uterine surgery, induction of labor, duration of first labor, neonatal weight, WBC value (during the first stage of labor), and cervical lacerations were all independent risk factors of hemorrhage (P <0.05). The area-under-curve (AUC) of ROC curves of the derivation group and the validation group were 0.817 and 0.821, respectively, indicating good discrimination. Two calibration curves showed that nomogram prediction and practical results were highly consistent (P = 0.105, P = 0.113). Conclusion: The developed individualized risk prediction nomogram model can assist midwives in recognizing and diagnosing high-risk groups of PPH and initiating early warning to reduce PPH incidence.

Keywords: vaginal delivery, postpartum hemorrhage, risk factor, nomogram

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Authors: Mayuri Roy Choudhury

Abstract:

In recent times the concept of agrivoltaics has gained popularity due to the dual use of land and the added value provided by photovoltaics in terms of renewable energy and crop production on farms. However, the transition towards agrivoltaics has been slow, and our research tries to investigate the obstacles leading towards the slow progress of agrivoltaics. We applied data science decision tree algorithms to quantify qualitative perceptions of farmers in the United States for agrivoltaics. To date, there has not been much research that mentions farmers' perceptions, as most of the research focuses on the benefits of agrivoltaics. Our study adds value by putting forward the voices of farmers, which play a crucial towards the transition to agrivoltaics in the future. Our results show a mixture of responses in favor of agrivoltaics. Furthermore, it also portrays significant concerns of farmers, which is useful for decision-makers when it comes to formulating policies for agrivoltaics.

Keywords: agrivoltaics, decision-tree algorithms, farmers perception, transition

Procedia PDF Downloads 157

Authors: P. Defossez, J. M. Bonnefond, D. Garrigou, P. Trichet, F. Danjon

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Windstorm damage to European forests has ecological, social and economic consequences of major importance. Most trees during storms are uprooted. While a large amount of work has been done over the last decade on understanding the aerial tree response to turbulent wind flow, much less is known about the root-soil interface, and the impact of soil moisture and root-soil system fatiguing on tree uprooting. Anchorage strength is expected to be reduced by water-logging and heavy rain during storms due to soil strength decrease with soil water content. Our paper is focused on the maritime pine cultivated on sandy soil, as a representative species of the Forêt des Landes, the largest cultivated forest in Europe. This study aims at providing knowledge on the effects of soil saturation on root anchorage. Pulling experiments on trees were performed to characterize the resistance to wind by measuring the critical bending moment (Mc). Pulling tests were performed on 12 maritime pines of 13-years old for two unsaturated soil conditions that represent the soil conditions expected in winter when wind storms occur in France (w=11.46 to 23.34 % gg⁻¹). A magnetic field digitizing technique was used to characterize the three-dimensional architecture of root systems. The soil mechanical properties as function of soil water content were characterized by laboratory mechanical measurements as function of soil water content and soil porosity on remolded samples using direct shear tests at low confining pressure ( < 15 kPa). Remarkably Mc did not depend on w but mainly on the root system morphology. We suggested that the importance of soil water conditions on tree anchorage depends on the tree size. This study gives a new insight on young tree anchorage: roots may sustain by themselves anchorage, whereas adhesion between roots and surrounding soil may be negligible in sandy soil.

Keywords: roots, sandy soil, shear strength, tree anchorage, unsaturated soil

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Authors: Abderrahmane Soufi

Abstract:

The building sector is the largest consumer of energy in France, accounting for 44% of French consumption. To reduce energy consumption and improve energy efficiency, France implemented an energy transition law targeting 40% energy savings by 2030 in the tertiary building sector. Building simulation tools are used to predict the energy performance of buildings but the reliability of these tools is hampered by discrepancies between the real and simulated energy performance of a building. This performance gap lies in the simplified assumptions of certain factors, such as the behavior of occupants on air conditioning and heating, which is considered deterministic when setting a fixed operating schedule and a fixed interior comfort temperature. However, the behavior of occupants on air conditioning and heating is stochastic, diverse, and complex because it can be affected by many factors. Probabilistic models are an alternative to deterministic models. These models are usually derived from statistical data and express occupant behavior by assuming a probabilistic relationship to one or more variables. In the literature, logistic regression has been used to model the behavior of occupants with regard to heating and air conditioning systems by considering univariate logistic models in residential buildings; however, few studies have developed multivariate models for higher education and vocational training buildings in a Mediterranean climate. Therefore, in this study, occupant behavior on heating and air conditioning systems was modeled using logistic regression. Occupant behavior related to the turn-on heating and air conditioning systems was studied through experimental measurements collected over a period of one year (June 2023–June 2024) in three classrooms occupied by several groups of students in engineering schools and professional training. Instrumentation was provided to collect indoor temperature and indoor relative humidity in 10-min intervals. Furthermore, the state of the heating/air conditioning system (off or on) and the set point were determined. The outdoor air temperature, relative humidity, and wind speed were collected as weather data. The number of occupants, age, and sex were also considered. Logistic regression was used for modeling an occupant turning on the heating and air conditioning systems. The results yielded a proposed model that can be used in building simulation tools to predict the energy performance of teaching buildings. Based on the first months (summer and early autumn) of the investigations, the results illustrate that the occupant behavior of the air conditioning systems is affected by the indoor relative humidity and temperature in June, July, and August and by the indoor relative humidity, temperature, and number of occupants in September and October. Occupant behavior was analyzed monthly, and univariate and multivariate models were developed.

Keywords: occupant behavior, logistic regression, behavior model, mediterranean climate, air conditioning, heating

Procedia PDF Downloads 33

Authors: Ebru Turgal, Beyza Doganay Erdogan

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Machine learning aims to model the relationship between the response and features. Medical decision-making researchers would like to make decisions about patients’ course and treatment, by examining the repeated measurements over time. Boosting approach is now being used in machine learning area for these aims as an influential tool. The aim of this study is to show the usage of multivariate tree boosting in this field. The main reason for utilizing this approach in the field of decision-making is the ease solutions of complex relationships. To show how multivariate tree boosting method can be used to identify important features and feature-time interaction, we used the data, which was collected retrospectively from Ankara University Chest Diseases Department records. Dataset includes repeated PF ratio measurements. The follow-up time is planned for 120 hours. A set of different models is tested. In conclusion, main idea of classification with weighed combination of classifiers is a reliable method which was shown with simulations several times. Furthermore, time varying variables will be taken into consideration within this concept and it could be possible to make accurate decisions about regression and survival problems.

Keywords: boosted multivariate trees, longitudinal data, multivariate regression tree, panel data

Procedia PDF Downloads 173

Authors: Thilini Hathurusinghe, Uthpalie Siriwardhana, W. M. Ediri Arachchi, Ranga Thudugala, Indeewari Herath, Gayani Senanayake

Abstract:

The foramen magnum is more prone to protect than other skeletal remains during high impact and severe disruptive injuries. Therefore, it is worthwhile to explore whether these measurements can be used to determine the human gender which is vital in forensic and anthropological studies. The idea was to find out the ability to use quantitative measurements of foramen magnum as an anatomical indicator for human gender estimation and to evaluate the gender-dependent variations of foramen magnum using quantitative measurements. Randomly selected 113 subjects who underwent CT head scans at Sri Jayawardhanapura General Hospital of Sri Lanka within a period of six months, were included in the study. The sample contained 58 males (48.76 ± 14.7 years old) and 55 females (47.04 ±15.9 years old). Maximum length of the foramen magnum (LFM), maximum width of the foramen magnum (WFM), minimum distance between occipital condyles (MnD) and maximum interior distance between occipital condyles (MxID) were measured. Further, AreaT and AreaR were also calculated. The gender was estimated using binomial logistic regression. The mean values of all explanatory variables (LFM, WFM, MnD, MxID, AreaT, and AreaR) were greater among male than female. All explanatory variables except MnD (p=0.669) were statistically significant (p < 0.05). Significant bivariate correlations were demonstrated by AreaT and AreaR with the explanatory variables. The results evidenced that WFM and MxID were the best measurements in predicting gender according to binomial logistic regression. The estimated model was: log (p/1-p) =10.391-0.136×MxID-0.231×WFM, where p is the probability of being a female. The classification accuracy given by the above model was 65.5%. The quantitative measurements of foramen magnum can be used as a reliable anatomical marker for human gender estimation in the Sri Lankan context.

Keywords: foramen magnum, forensic and anthropological studies, gender estimation, logistic regression

Procedia PDF Downloads 125

Authors: Adesuyi Ayodeji Steve, Zahn Munch

Abstract:

This study investigates the use of MODIS NDVI to identify agricultural land cover change areas on an annual time step (2007 - 2012) and characterize the trend in the study area. An ISODATA classification was performed on the MODIS imagery to select only the agricultural class producing 3 class groups namely: agriculture, agriculture/semi-natural, and semi-natural. NDVI signatures were created for the time series to identify areas dominated by cereals and vineyards with the aid of ancillary, pictometry and field sample data. The NDVI signature curve and training samples aided in creating a decision tree model in WEKA 3.6.9. From the training samples two classification models were built in WEKA using decision tree classifier (J48) algorithm; Model 1 included ISODATA classification and Model 2 without, both having accuracies of 90.7% and 88.3% respectively. The two models were used to classify the whole study area, thus producing two land cover maps with Model 1 and 2 having classification accuracies of 77% and 80% respectively. Model 2 was used to create change detection maps for all the other years. Subtle changes and areas of consistency (unchanged) were observed in the agricultural classes and crop practices over the years as predicted by the land cover classification. 41% of the catchment comprises of cereals with 35% possibly following a crop rotation system. Vineyard largely remained constant over the years, with some conversion to vineyard (1%) from other land cover classes. Some of the changes might be as a result of misclassification and crop rotation system.

Keywords: change detection, land cover, modis, NDVI

Procedia PDF Downloads 372