Search results for: multivariate regression tree

Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe

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The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.

Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning

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Authors: Manel Youssef, Lotfi Belkacem

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This paper employs multivariate long memory GARCH models to simultaneously estimate mean and conditional variance spillover effects between oil prices and different financial markets. Since different financial assets are traded based on these market sector returns, it’s important for financial market participants to understand the volatility transmission mechanism over time and across these series in order to make optimal portfolio allocation decisions. We examine weekly returns from January 1, 2003 to November 30, 2012 and find evidence of significant transmission of shocks and volatilities between oil prices and some of the examined financial markets. The findings support the idea of cross-market hedging and sharing of common information by investors.

Keywords: oil prices, stock indices returns, oil volatility, contagion, DCC-multivariate (FI) GARCH

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Authors: Joserra Díez, Ainhoa Meñika, Iñaki Sanz-Azkue, Arritokieta Ortuzar

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Biodiversity provides humans with a great range of ecosystemic services; it is therefore an indispensable resource and a legacy to coming generations. However, in the last decades, the increasing exploitation of the Planet has caused a great loss of biodiversity and its acquaintance has decreased remarkably; especially in urbanized areas, due to the decreasing attachment of humans to nature. Yet, the Primary Education curriculum primes the identification of flora and fauna to guarantee the knowledge of children on their surroundings, so that they care for the environment as well as for themselves. In order to produce effective didactic material that meets the needs of both teachers and pupils, it is fundamental to diagnose the current situation. In the present work, the knowledge on biodiversity of 3^rd cycle Primary Education students in Biscay (n=98) and its relation to the size of the town/city of their school is discussed. Two tests have been used with such aim: one for tree identification and the other one so that the students enumerated the species of trees and animals they knew. Results reveal that knowledge of students on tree identification is scarce regardless the size of the city/town and of their school. On the other hand, animal species are better known than tree species.

Keywords: biodiversity, population, tree identification, animal identification

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Authors: Rajvir Kaur, Jeewani Anupama Ginige

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With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.

Keywords: artificial neural networks, breast cancer, classifiers, cervical cancer, f-score, machine learning, precision, recall

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Authors: Yasuyo Makido, Vivek Shandas, David J. Sailor, M. Salim Ferwati

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Mitigating extreme urban heat is challenging in a desert climate such as Doha, Qatar, since outdoor daytime temperature area often too high for the human body to tolerate. Recent studies demonstrate that cities in arid and semiarid areas can exhibit ‘urban cool islands’ - urban areas that are cooler than the surrounding desert. However, the variation of temperatures as a result of the time of day and factors leading to temperature change remain at the question. To address these questions, we examined the spatial and temporal variation of air temperature in Doha, Qatar by conducting multiple vehicle-base local temperature observations. We also employed three statistical approaches to model surface temperatures using relevant predictors: (1) Ordinary Least Squares, (2) Regression Tree Analysis and (3) Random Forest for three time periods. Although the most important determinant factors varied by day and time, distance to the coast was the significant determinant at midday. A 70%/30% holdout method was used to create a testing dataset to validate the results through Pearson’s correlation coefficient. The Pearson’s analysis suggests that the Random Forest model more accurately predicts the surface temperatures than the other methods. We conclude with recommendations about the types of development patterns that show the greatest potential for reducing extreme heat in air climates.

Keywords: desert cities, tree-structure regression model, urban cool Island, vehicle temperature traverse

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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: Bliss Singhal

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Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

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Authors: Kiomars Sefidi

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Dead and habitat tree such as fallen logs, snags, stumps and cracks and loos bark etc. is regarded as an important ecological component of forests on which many forest dwelling species depend, yet its relation to management history in Caspian forest has gone unreported. The aim of research was to compare the amounts of dead tree and habitat in the forests with historically different intensities of management, including: forests with the long term implication of management (PS), the short-term implication of management (NS) which were compared with semi virgin forest (GS). The number of 405 individual dead and habitat trees were recorded and measured at 109 sampling locations. ANOVA revealed volume of the dead tree in the form and decay classes significantly differ within sites and dead volume in the semi virgin forest significantly higher than managed sites. Comparing the amount of dead and habitat tree in three sites showed that dead tree volume related with management history and significantly differ in three study sites. Also, the numbers of habitat trees including cavities, Cracks and loose bark and Fork split trees significantly vary among sites. Reaching their highest in virgin site and their lowest in the site with the long term implication of management, it was concluded that forest management cause reduction of the amount of dead and habitat tree. Forest management history affect the forest's ability to generate dead tree especially in a large size, thus managing this forest according to ecological sustainable principles require a commitment to maintaining stand structure that allow, continued generation of dead tree in a full range of size.

Keywords: forest biodiversity, cracks trees, fork split trees, sustainable management, Fagus orientalis, Iran

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Authors: Mohamed Elkoushy, Sameer Munshi, Waseem Tayeb

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Background: Flexible ureteroscopy (fURS) has dramatically improved the minimally invasive management of complex nephrolithiasis. fUR is increasingly being used as the first-line treatment for patients with renal stones. Stone-free status (SFS) is the primary goal in the management of patients with urolithiasis. However, substantial variations exist in the reported SFS following fURS. Objectives: This study determines the predictors of SFS after a single session of fURS with holmium laser lithotripsy (HLL) for renal calculi. Methods: A retrospective review of prospectively collected data was performed for all consecutive patients undergoing fURS and HLL for renal calculi at a tertiary care center. Patients with previous ipsilateral URS for the same stones were excluded. All patients underwent JJ ureteral stent insertion at the end of the procedure. SFS was defined as the presence of no residuals or ≤4-mm non-obstructing stone and was assessed by CT/KUB imaging after 3-4 weeks post-operatively. Multivariate logistic regression was used to detect possible predictors of SFS. Results: A total of 212 patients were included with a mean age of 52.3±8.3 years and a stone burden <20 mm (49.1%), 20-30 mm (41.0%) and >30 mm (9.9%). Overall SFS after a single session of fURS was 71.7%, 92% and 52% for stones less and larger than 20 mm, respectively. Patients with stones> 20 mm need retreatment with a mean number of 1.8 (1.3-2.7) fURS. SFS was significantly associated with male gender, stone bulk <20 mm (95.7% vs. 56.2%), non-lower pole stones, hydronephrotic kidney, low stone intensity, ureteral access sheath, and preoperative stenting. SFS was associated with a lower readmission rate (5.9% vs. 38.9%) and urinary tract infections (3.8% vs. 25.9%). In multivariate regression analysis, SFS maintains its significant association with low stone burden of <20 mm (OR: 5.21), stone intensity <600 HFU (OR: 2.87), and non-lower caliceal stones (OR: 3.84). Conclusion: Best results after a single-session fURS for renal stone were obtained for the stone burden of less than 20 mm and low stone attenuation. Lower calyceal stones may influence stone clearance and need a different approach than fURS, especially for higher stone burden.

Keywords: ureteroscopy, kidney stone, lithotripsy, stone-free, predictors

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Authors: Himansu, Bedanga Talukdar

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The aim of present study is to investigate the prevalence of various health outcomes and quality of life and analyzes the moderating role of social capital on health outcomes (i.e., self-rated good health (SRH), depression, functional health and quality of life) among elderly in India. Using WHO Study on Global AGEing and adults health (SAGE) data, with sample of 6559 elderly between 50 and above (Mage=61.81, SD=9.00) age were selected for analysis. Multivariate analysis accessed the prevalence of SRH, depression, functional limitation and quality of life among older adults. Logistic regression evaluates the effect of social capital along with other co-founders on SRH, depression, and functional limitation, whereas linear regression evaluates the effect of social capital with other co-founders on quality of life (QoL) among elderly. Empirical results reveal that (74%) of respondents were married, (70%) having low social action, (46%) medium sociability, (45%) low trust-solidarity, (58%) high safety, (65%) medium civic engagement and 37% reported medium psychological resources. The multivariate analysis, explains (SRH) is associated with age, female, having education, higher social action great trust, safety and greater psychological resources. Depression among elderly is greatly related to age, sex, education and higher wealth, higher sociability, having psychological resources. QoL is negatively associated with age, sex, being Muslim, whereas positive associated with higher education, currently married, civic engagement, having wealth, social action, trust and solidarity, safeness, and strong psychological resources.

Keywords: depressive symptom, functional limitation, older adults, quality of life, self rated health, social capital

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Authors: Mansoureh Ghiasabadi Farahani, Samira Kalantary, Sara Taghi-Pour, Mahboubeh Shamsi

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Nowadays, the development of computers technology and its recent applications provide access to new types of data, which have not been considered by the traditional data analysts. Two particularly interesting characteristics of such data sets include their huge size and streaming nature .Incremental learning techniques have been used extensively to address the data stream classification problem. This paper presents a concise survey on the obstacles and the requirements issues classifying data streams with using decision tree. The most important issue is to maintain a balance between accuracy and efficiency, the algorithm should provide good classification performance with a reasonable time response.

Keywords: big data, data streams, classification, decision tree

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Authors: Kisan Sarda, Bhavika Shingote

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This paper aims to evaluate regression modelling for prediction of Energy storage of solar photovoltaic (PV) system using Semi parametric regression techniques because there are some parameters which are known while there are some unknown parameters like humidity, dust etc. Here irradiation of solar energy is different for different places on the basis of Latitudes, so by finding out areas which give more storage we can implement PV systems at those places and our need of energy will be fulfilled. This regression modelling is done for daily, monthly and seasonal prediction of solar energy storage. In this, we have used R modules for designing the algorithm. This algorithm will give the best comparative results than other regression models for the solar PV cell energy storage.

Keywords: semi parametric regression, photovoltaic (PV) system, regression modelling, irradiation

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Authors: M. Chumburidze, N. Salia, T. Namchevadze

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This work deal with case processing problems in business. The task of strategic credit requirements management of cases portfolio is discussed. The information model of credit requirements in a binary tree diagram is considered. The algorithms to solve issues of prioritizing clusters of cases in business have been investigated. An implementation of priority queues to support case management operations has been presented. The corresponding pseudo codes for the programming application have been constructed. The tools applied in this development are based on binary tree ordering algorithms, optimization theory, and business management methods.

Keywords: credit network, case portfolio, binary tree, priority queue, stack

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Authors: Muhammad Imran, Faheem Khadija, Zahoor Hussain, Raheel Anwar, M. Nawaz Khan, M. Raza Salik

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Amongst citrus species, sweet orange (Citrus sinensis L. Osbeck) occupies a dominant position in the orange producing countries in the world. Sweet orange is widely consumed both as fresh fruit as well as juice and its global demand is attributed due to higher vitamin C and antioxidants. Fruit quality is most important for the external appearance and marketability of sweet orange fruit, especially for fresh consumption. There are so many factors affecting fruit quality, tree age is the most important one, but remains unexplored so far. The present study, we investigated the role of tree age on fruit quality of different cultivars of sweet oranges. The difference between fruit quality of 5-year young and 15-year old trees was discussed in the current study. In case of fruit weight, maximum fruit weight (238g) was recorded in 15-year old sweet orange cv. Sallustiana cultivar while minimum fruit weight (142g) was recorded in 5-year young tree of Succari sweet orange fruit. The results of the fruit diameter showed that the maximum fruit diameter (77.142mm) was recorded in 15-year old Sallustiana orange but the minimum fruit diameter (66.046mm) was observed in 5-year young tree of sweet orange cv. Succari. The minimum value of rind thickness (4.142mm) was noted in 15-year old tree of cv. Red blood. On the other hand maximum value of rind thickness was observed in 5-year young tree of cv. Sallustiana. The data regarding total soluble solids (TSS), acidity (TA), TSS/TA, juice content, rind, flavedo thickness, pH and fruit diameter have also been discussed.

Keywords: age, cultivars, fruit, quality, sweet orange (Citrus Sinensis L. Osbeck)

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Authors: R. B. Patil, B. S. Kothavale, L. Y. Waghmode

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Today, the CNC turning center becomes an important machine tool for manufacturing industry worldwide. However, as the breakdown of a single CNC turning center may result in the production of an entire plant being halted. For this reason, operations and preventive maintenance have to be minimized to ensure availability of the system. Indeed, improving the availability of the CNC turning center as a whole, objectively leads to a substantial reduction in production loss, operating, maintenance and support cost. In this paper, fault tree analysis (FTA) method is used for reliability analysis of CNC turning center. The major faults associated with the system and the causes for the faults are presented graphically. Boolean algebra is used for evaluating fault tree (FT) diagram and for deriving governing reliability model for CNC turning center. Failure data over a period of six years has been collected and used for evaluating the model. Qualitative and quantitative analysis is also carried out to identify critical sub-systems and components of CNC turning center. It is found that, at the end of the warranty period (one year), the reliability of the CNC turning center as a whole is around 0.61628.

Keywords: fault tree analysis (FTA), reliability analysis, risk assessment, hazard analysis

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Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

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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: Bachir Nora

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Integrating vegetation into urban environments is crucial for enhancing quality of life, particularly through microclimate regulation and the reduction of urban heat islands. This study investigates the impact of different street tree species on the urban microclimate in the El-Houria neighborhood of Mostaganem, Algeria, using the ENVI-met software for advanced environmental simulations. it focused on three tree species—Robinia pseudo-acacia, Populus alba, and Jacaranda mimosifolia—to evaluate their effectiveness in influencing key meteorological parameters, including air temperature, mean radiant temperature, surface temperature, and the predicted percentage of dissatisfied (PPD) thermal comfort index. Statistical analyses were conducted to compare these parameters across different tree species, ensuring the robustness of our findings. Our results indicate that Robinia pseudo-acacia is the most effective species, capable of reducing air temperature by up to 1°C and surface temperature by up to 12°C. These findings underscore the importance of strategically selecting tree species to mitigate the effects of climate change, improve thermal comfort, and reduce energy consumption in urban settings. The study offers valuable insights for urban planners and policymakers, providing practical guidance for sustainable urban design practices tailored to the Algerian context.

Keywords: heat islands, microclimate simulation, street tree alignment, sustainable urban design, thermal comfort.

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Authors: Mohammadreza Mohebbi, Masoumeh Sanagou

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The ability to predict clinical outcomes is of great importance to physicians and clinicians. A number of different methods have been used in an effort to accurately predict these outcomes. These methods include the development of scoring systems based on multivariate statistical modelling, and models involving the use of classification and regression trees. The process usually consists of two consecutive phases, namely model development and external validation. The model development phase consists of building a multivariate model and evaluating its predictive performance by examining calibration and discrimination, and internal validation. External validation tests the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. A motivate example focuses on prediction modeling using a sample of patients undergone coronary artery bypass graft (CABG) has been used for illustrative purpose and a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study has been proposed.

Keywords: clinical prediction models, clinical decision rule, prognosis, external validation, model calibration, biostatistics

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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: Suparman

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Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation of piecewise linear regression models. The method used to estimate the parameters of picewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters of picewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of picewise linear regression models.

Keywords: regression, piecewise, Bayesian, reversible Jump MCMC

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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: 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: Idrissa Kayijuka

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The logistic regression and Cox regression models (proportional hazard model) at present are being employed in the analysis of prospective epidemiologic research looking into risk factors in their application on chronic diseases. However, a theoretical relationship between the two models has been studied. By definition, Cox regression model also called Cox proportional hazard model is a procedure that is used in modeling data regarding time leading up to an event where censored cases exist. Whereas the Logistic regression model is mostly applicable in cases where the independent variables consist of numerical as well as nominal values while the resultant variable is binary (dichotomous). Arguments and findings of many researchers focused on the overview of Cox and Logistic regression models and their different applications in different areas. In this work, the analysis is done on secondary data whose source is SPSS exercise data on BREAST CANCER with a sample size of 1121 women where the main objective is to show the application difference between Cox regression model and logistic regression model based on factors that cause women to die due to breast cancer. Thus we did some analysis manually i.e. on lymph nodes status, and SPSS software helped to analyze the mentioned data. This study found out that there is an application difference between Cox and Logistic regression models which is Cox regression model is used if one wishes to analyze data which also include the follow-up time whereas Logistic regression model analyzes data without follow-up-time. Also, they have measurements of association which is different: hazard ratio and odds ratio for Cox and logistic regression models respectively. A similarity between the two models is that they are both applicable in the prediction of the upshot of a categorical variable i.e. a variable that can accommodate only a restricted number of categories. In conclusion, Cox regression model differs from logistic regression by assessing a rate instead of proportion. The two models can be applied in many other researches since they are suitable methods for analyzing data but the more recommended is the Cox, regression model.

Keywords: logistic regression model, Cox regression model, survival analysis, hazard ratio

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

Abstract:

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

Procedia PDF Downloads 109

Authors: Rafael Zhindon Almeida

Abstract:

Surface water quality is an important concern for the evaluation and prediction of water quality conditions. The objective of this study is to develop a statistical model that can accurately predict the water quality of the El Macho estuary in the city of Machala, El Oro province. The methodology employed in this study is of a basic type that involves a thorough search for theoretical foundations to improve the understanding of statistical modeling for water quality analysis. The research design is correlational, using a multivariate statistical model involving multiple linear regression and principal component analysis. The results indicate that water quality parameters such as fecal coliforms, biochemical oxygen demand, chemical oxygen demand, iron and dissolved oxygen exceed the allowable limits. The water of the El Macho estuary is determined to be below the required water quality criteria. The multiple linear regression model, based on chemical oxygen demand and total dissolved solids, explains 99.9% of the variance of the dependent variable. In addition, principal component analysis shows that the model has an explanatory power of 86.242%. The study successfully developed a statistical model to evaluate the water quality of the El Macho estuary. The estuary did not meet the water quality criteria, with several parameters exceeding the allowable limits. The multiple linear regression model and principal component analysis provide valuable information on the relationship between the various water quality parameters. The findings of the study emphasize the need for immediate action to improve the water quality of the El Macho estuary to ensure the preservation and protection of this valuable natural resource.

Keywords: statistical modeling, water quality, multiple linear regression, principal components, statistical models

Procedia PDF Downloads 100

Authors: Raymond Awinbilla

Abstract:

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

Procedia PDF Downloads 58

Authors: Masahiro Ohmura, Koh Kakusho, Takeshi Okadome

Abstract:

This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.

Keywords: stock market prediction, social moods, regression model, DJIA

Procedia PDF Downloads 549