Search results for: logistic model tree

Authors: Birome Holo Ba, Christian Prins, Caroline Prodhon

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Optimization is an important aspect of logistics management. It can reduce significantly logistics costs and also be a good tool for decision support. In this paper, we address a planning problem specific to biomass supply chain. We propose a new mixed integer linear programming (MILP) model dealing with different feed stock production operations such as harvesting, packing, storage, pre-processing and transportation, with the objective of minimizing the total logistic cost of the system on a regional basis. It determines the optimal number of harvesting machine, the fleet size of trucks for transportation and the amount of each type of biomass harvested, stored and pre-processed in each period to satisfy demands of refineries in each period. We illustrate the effectiveness of the proposal model with a numerical example, a case study in Aube (France department), which gives preliminary and interesting, results on a small test case.

Keywords: biomass logistics, supply chain, modelling, optimization, bioenergy, biofuels

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Authors: Haolin Li, Eric Bair, Jane Monaco, Quefeng Li

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The temporomandibular disorder (TMD) is a series of musculoskeletal disorders ranging from jaw pain to chronic debilitating pain, and the risk factors for the onset and maintenance of TMD are still unclear. Prior researches have shown that the potential risk factors for chronic TMD are related to psychosocial factors, autonomic functions, and pain sensitivity. Using data from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study’s baseline case-control study, we examine whether the risk factors identified by prior researches are still statistically significant after taking all of the risk measures into account in one single model, and we also compare the relative influences of the risk factors in three different perspectives (psychosocial factors, autonomic functions, and pain sensitivity) on the chronic TMD. The statistical analysis is conducted by using ridge logistic regression and bootstrapping, in which the performance of the algorithms has been assessed using extensive simulation studies. The results support most of the findings of prior researches that there are many psychosocial and pain sensitivity measures that have significant associations with chronic TMD. However, it is surprising that most of the risk factors of autonomic functions have not presented significant associations with chronic TMD, as described by a prior research.

Keywords: autonomic function, OPPERA study, pain sensitivity, psychosocial measures, temporomandibular disorder

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Authors: Adel Khorramrouz, Monireh Ahmadi Bani, Ehsan Norouzi

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Background:-Due to the essential role of the foot in movement, foot disorders (FDs) have significant impacts on activity and quality of life. Many studies confirmed the association between FDs and demographic characteristics. On the other hand, recent advances in data collection and statistical analysis led to an increase in the volume of databases. Analysis of patient’s data through the decision tree can be used to explore the relationship between demographic characteristics and FDs. Significance of the study: This study aimed to investigate the relationship between demographic characteristics with common FDs. The second purpose is to better inform foot intervention, we classify FDs based on demographic variables. Methodologies: We analyzed 2323 subjects with pes-planus (PP), pes-cavus (PC), hallux-valgus (HV) and plantar-fasciitis (PF) who were referred to a foot therapy clinic between 2015 and 2021. Subjects had to fulfill the following inclusion criteria: (1) weight between 14 to 150 kilogram, (2) height between 30 to 220, (3) age between 3 to 100 years old, and (4) BMI between 12 to 35. Medical archives of 2323 subjects were recorded retrospectively and all the subjects examined by an experienced physician. Age and BMI were classified into five and four groups, respectively. 80% of the data were randomly selected as training data and 20% tested. We build a decision tree model to classify FDs using demographic characteristics. Findings: Results demonstrated 981 subjects from 2323 (41.9%) of people who were referred to the clinic with FDs were diagnosed as PP, 657 (28.2%) PC, 628 (27%) HV and 213 (9%) identified with PF. The results revealed that the prevalence of PP decreased in people over 18 years of age and in children over 7 years. In adults, the prevalence depends first on BMI and then on gender. About 10% of adults and 81% of children with low BMI have PP. There is no relationship between gender and PP. PC is more dependent on age and gender. In children under 7 years, the prevalence was twice in girls (10%) than boys (5%) and in adults over 18 years slightly higher in men (62% vs 57%). HV increased with age in women and decreased in men. Aging and obesity have increased the prevalence of PF. We conclude that the accuracy of our approach is sufficient for most research applications in FDs. Conclusion:-The increased prevalence of PP in children is probably due to the formation of the arch of the foot at this age. Increasing BMI by applying high pressure on the foot can increase the prevalence of this disorder in the foot. In PC, the Increasing prevalence of PC from women to men with age may be due to genetics and innate susceptibility of men to this disorder. HV is more common in adult women, which may be due to environmental reasons such as shoes, and the prevalence of PF in obese adult women may also be due to higher foot pressure and housekeeping activities.

Keywords: decision tree, demographic characteristics, foot disorders, machine learning

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Authors: Kazushi Murata, Fabian Watermann, O. B. Herve Gonroudobou, Le Thuy Hang, Toshiro Yamanaka, M. Larry Lopez C.

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Aims: Black pine coastal forests play an important role as a windbreak and as a natural barrier to sand and salt spray inland in Japan. The recent invasion of N₂-fxing black locust (Robinia pseudoacacia) trees in these forests is expected to have a nutritional contribution to black pine trees growth. Thus, the effect of this new source of N on black pine trees' N assimilation needs to be assessed. Methods: In order to evaluate this contribution, tree-ring isotopic composition (δ¹⁵N) and nitrogen content (%N) of black pine (Pinus thunbergii) trees in a pure stand (BPP) and a mixed stand (BPM) with black locust (BL) trees were measured for the period 2000–2019 for BPP and BL and 1990–2019 for BPM. The same measurements were conducted in plant tissues and in soil samples. Results: The tree ring δ15N values showed that for the last 30 years, BPM trees gradually switched from BPP to BL-derived soil N starting in the 1990s, becoming the dominant N source from 2000 as no significant diference was found between BPM and BL tree ring δ¹⁵N values from 2000 to 2019. No difference in root and sapwood BPM and BL δ¹⁵N values were found, but BPM foliage (−2.1‰) was different to BPP (−4.4‰) and BL (−0.3‰), which is related to the different N assimilation pathways between BP and BL. Conclusions: Based on the results of this study, the assimilation of BL-derived N inferred from the BPM tissues' δ¹⁵N values is the result of an increase in soil bioavailable N with a higher δ¹⁵N value.

Keywords: nitrogen-15, N₂-fxing species, mixed stand, soil, tree rings

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Authors: Zouhaier Nasr, Mohamed Nouri

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The increase in greenhouse gases since the pre-industrial period is a real threat to disrupting the balance of marine and terrestrial ecosystems. Along with the oceans, forest soils are considered to be the planet's second-largest carbon sink. North African forests have been subject to alarming degradation for several decades. The objective of this investigation is to determine and quantify the effect of thinning practiced in pine forests in northern Tunisia on the storage of organic carbon in the trees and in the soil. The plot planted in 1989 underwent thinning in 2005 on to plots; the density is therefore 1600 trees/ha in control and 400 trees/ha in thinning. Direct dendrometric measurements (diameter, height, branches, stem) were taken. In the soil part, six profiles of 1m / 1m / 1m were used for soil and root samples and biomass and organic matter measurements. The measurements obtained were statistically processed by appropriate software. The results clearly indicate that thinning improves tree growth, so the diameter increased from 24.3 cm to 30.1 cm. Carbon storage in the trunks was 35% more and 25% for the whole tree. At ground level, the thinned plot shows a slight increase in soil organic matter and quantity of carbon per tree, exceeding the control by 10 to 25%.

Keywords: forest, soil, carbon, climate change, Tunisia

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Authors: Retno A. Vinarti, Lucy M. Hederman

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This research describes a knowledge model for a system which give personalized alert to users about infectious disease risks in the context of weather, location and time. The knowledge model is based on established epidemiological concepts augmented by information gleaned from infection-related data repositories. The existing disease risk prediction research has more focuses on utilizing raw historical data and yield seasonal patterns of infectious disease risk emergence. This research incorporates both data and epidemiological concepts gathered from Atlas of Human Infectious Disease (AHID) and Centre of Disease Control (CDC) as basic reasoning of infectious disease risk prediction. Using CommonKADS methodology, the disease risk prediction task is an assignment synthetic task, starting from knowledge identification through specification, refinement to implementation. First, knowledge is gathered from AHID primarily from the epidemiology and risk group chapters for each infectious disease. The result of this stage is five major elements (Person, Infectious Disease, Weather, Location and Time) and their properties. At the knowledge specification stage, the initial tree model of each element and detailed relationships are produced. This research also includes a validation step as part of knowledge refinement: on the basis that the best model is formed using the most common features, Frequency-based Selection (FBS) is applied. The portion of the Infectious Disease risk model relating to Person comes out strongest, with Location next, and Weather weaker. For Person attribute, Age is the strongest, Activity and Habits are moderate, and Blood type is weakest. At the Location attribute, General category (e.g. continents, region, country, and island) results much stronger than Specific category (i.e. terrain feature). For Weather attribute, Less Precise category (i.e. season) comes out stronger than Precise category (i.e. exact temperature or humidity interval). However, given that some infectious diseases are significantly more serious than others, a frequency based metric may not be appropriate. Future work will incorporate epidemiological measurements of disease seriousness (e.g. odds ratio, hazard ratio and fatality rate) into the validation metrics. This research is limited to modelling existing knowledge about epidemiology and chain of infection concepts. Further step, verification in knowledge refinement stage, might cause some minor changes on the shape of tree.

Keywords: epidemiology, knowledge modelling, infectious disease, prediction, risk

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Authors: Abdullah H. Özcan, Dilara Hisar, Yetkin Sayar, Cem Ünsalan

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In this study, an automated building and tree detection method is proposed using DSM data and true orthophoto image. A multiscale matched filtering is used on DSM data. Therefore, first watershed transform is applied. Then, Otsu’s thresholding method is used as an adaptive threshold to segment each watershed region. Detected objects are masked with NDVI to separate buildings and trees. The proposed method is able to detect buildings and trees without entering any elevation threshold. We tested our method on ISPRS semantic labeling dataset and obtained promising results.

Keywords: building detection, local maximum filtering, matched filtering, multiscale

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Authors: Hurgesa Hundera, Samuel Shibeshibikeko, Tarike Daba, Tesfaye Ganamo

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The study aimed at examining the ongoing adaptation strategies used by smallholder farmers in response to climate variability in Boset district. It also assessed the socioeconomic factors that influence the choice of adaptation strategies of smallholder farmers to climate variability risk. For attaining the objectives of the study, both primary and secondary sources of data were employed. The primary data were obtained through a household questionnaire, key informant interviews, focus group discussions, and observations, while secondary data were acquired through desk review. Questionnaires were distributed and filled by 328 respondents, and they were identified through systematic random sampling technique. Descriptive statistics and binary logistic regression model were applied in this study as the main analytical methods. The findings of the study reveal that the sample households have utilized multiple adaptation strategies in response to climate variability, such as cropping early mature crops, planting drought resistant crops, growing mixed crops on the same farm lands, and others. The results of the binary logistic model revealed that education, sex, age, family size, off farm income, farm experience, access to climate information, access to farm input, and farm size were significant and key factors determining farmers’ choice of adaptation strategies to climate variability in the study area. To enable effective adaptation measures, Ministry of Agriculture and Natural Resource, with its regional bureaus and offices and concerned non–governmental organizations, should consider climate variability in their planning and budgeting in all levels of decision making.

Keywords: adaptation strategies, boset district, climate variability, smallholder farmers

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Authors: Guang Yang, Zhiwei Bai, Bo Sun

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This paper analyzes the influence of failure mode and harmfulness of rotative machine according to FMECA (Failure Mode, Effects, and Criticality Analysis) method, and finds out the weak links that affect the reliability of this equipment. Also in this paper, fault tree analysis software is used for quantitative and qualitative analysis, pointing out the main factors of failure of this equipment. Based on the experimental results, this paper puts forward corresponding measures for prevention and improvement, and fundamentally improves the inherent reliability of this rotative machine, providing the basis for the formulation of technical conditions for the safe operation of industrial applications.

Keywords: rotative machine, reliability test, fault tree analysis, FMECA

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Authors: Rahma Al Maawali, Hameed Sulaiman

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Air pollution both from point and non-point sources is difficult to control once released in to the atmosphere. There is no engineering method known available to ameliorate the dispersed pollutants. The only suitable approach is the ecological method of constructing green belts in and around the pollution sources. Air pollution in Muscat, Oman is a serious concern due to ever increasing vehicles on roads. Identifying the air pollution tolerance levels of species is important for implementing pollution control strategies in the urban areas of Muscat. Hence, in the present study, Air Pollution Tolerance Index (APTI) for ten avenue tree species was evaluated by analyzing four bio-chemical parameters, plus their Anticipated Performance Index (API) in field conditions. Based on the two indices, Ficus benghalensis was the most suitable one with the highest performance score. Conocarpus erectuse, Phoenix dactylifera, and Pithcellobium dulce were found to be good performers and are recommended for extensive planting. Azadirachta indica which is preferred for its dense canopy is qualified in the moderate category. The rest of the tree species expressed lower API score of less than 51, hence cannot be considered as suitable species for pollution mitigation plantation projects.

Keywords: air pollution tolerance index (APTI), avenue tree species, bio-chemical parameters, muscat

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Authors: Meng-Tzu Cheng, Louisa Rosenheck, Chen-Yen Lin, Eric Klopfer

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The purpose of the research is to explore some of the ways in which gameplay data can be analyzed to yield results that feedback into the learning ecosystem. Back-end data for all users as they played an MMOG, The Radix Endeavor, was collected, and this study reports the analyses on a specific genetics quest by using the data mining techniques, including the decision tree method. In the study, different reasons for quest failure between participants who eventually succeeded and who never succeeded were revealed. Regarding the in-game tools use, trait examiner was a key tool in the quest completion process. Subsequently, the results of decision tree showed that a lack of trait examiner usage can be made up with additional Punnett square uses, displaying multiple pathways to success in this quest. The methods of analysis used in this study and the resulting usage patterns indicate some useful ways that gameplay data can provide insights in two main areas. The first is for game designers to know how players are interacting with and learning from their game. The second is for players themselves as well as their teachers to get information on how they are progressing through the game, and to provide help they may need based on strategies and misconceptions identified in the data.

Keywords: MMOG, decision tree, genetics, gaming-learning interaction

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Authors: Man-Yun Liu, Emily Chia-Yu Su

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Alzheimer's disease (AD) is the public health crisis of the 21st century. AD is a degenerative brain disease and the most common cause of dementia, a costly disease on the healthcare system. Unfortunately, the cause of AD is poorly understood, furthermore; the treatments of AD so far can only alleviate symptoms rather cure or stop the progress of the disease. Currently, there are several ways to diagnose AD; medical imaging can be used to distinguish between AD, other dementias, and early onset AD, and cerebrospinal fluid (CSF). Compared with other diagnostic tools, blood (plasma) test has advantages as an approach to population-based disease screening because it is simpler, less invasive also cost effective. In our study, we used blood biomarkers dataset of The Alzheimer’s disease Neuroimaging Initiative (ADNI) which was funded by National Institutes of Health (NIH) to do data analysis and develop a prediction model. We used independent analysis of datasets to identify plasma protein biomarkers predicting early onset AD. Firstly, to compare the basic demographic statistics between the cohorts, we used SAS Enterprise Guide to do data preprocessing and statistical analysis. Secondly, we used logistic regression, neural network, decision tree to validate biomarkers by SAS Enterprise Miner. This study generated data from ADNI, contained 146 blood biomarkers from 566 participants. Participants include cognitive normal (healthy), mild cognitive impairment (MCI), and patient suffered Alzheimer’s disease (AD). Participants’ samples were separated into two groups, healthy and MCI, healthy and AD, respectively. We used the two groups to compare important biomarkers of AD and MCI. In preprocessing, we used a t-test to filter 41/47 features between the two groups (healthy and AD, healthy and MCI) before using machine learning algorithms. Then we have built model with 4 machine learning methods, the best AUC of two groups separately are 0.991/0.709. We want to stress the importance that the simple, less invasive, common blood (plasma) test may also early diagnose AD. As our opinion, the result will provide evidence that blood-based biomarkers might be an alternative diagnostics tool before further examination with CSF and medical imaging. A comprehensive study on the differences in blood-based biomarkers between AD patients and healthy subjects is warranted. Early detection of AD progression will allow physicians the opportunity for early intervention and treatment.

Keywords: Alzheimer's disease, blood-based biomarkers, diagnostics, early detection, machine learning

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Authors: M. Varin, A. M. Dubois, R. Gadbois-Langevin, B. Chalghaf

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Good knowledge of the physical environment is essential for an integrated forest planning. This information enables better forecasting of operating costs, determination of cutting volumes, and preservation of ecologically sensitive areas. The use of satellite images in stereoscopic pairs gives the capacity to generate high precision 3D models, which are scale-adapted for harvesting operations. These models could represent an alternative to 3D LiDAR data, thanks to their advantageous cost of acquisition. The objective of the study was to assess the quality of stereo-derived canopy height models (CHM) in comparison to a traditional LiDAR CHM and ground tree-height samples. Two study sites harboring two different forest stand types (broadleaf and conifer) were analyzed using stereo pairs and tri-stereo images from the WorldView-3 satellite to calculate CHM. Acquisition of multispectral images from an Unmanned Aerial Vehicle (UAV) was also realized on a smaller part of the broadleaf study site. Different algorithms using two softwares (PCI Geomatica and Correlator3D) with various spatial resolutions and band selections were tested to select the 3D modeling technique, which offered the best performance when compared with LiDAR. In the conifer study site, the CHM produced with Corelator3D using only the 50-cm resolution panchromatic band was the one with the smallest Root-mean-square deviation (RMSE: 1.31 m). In the broadleaf study site, the tri-stereo model provided slightly better performance, with an RMSE of 1.2 m. The tri-stereo model was also compared to the UAV, which resulted in an RMSE of 1.3 m. At individual tree level, when ground samples were compared to satellite, lidar, and UAV CHM, RMSE were 2.8, 2.0, and 2.0 m, respectively. Advanced analysis was done for all of these cases, and it has been noted that RMSE is reduced when the canopy cover is higher when shadow and slopes are lower and when clouds are distant from the analyzed site.

Keywords: very high spatial resolution, satellite imagery, WorlView-3, canopy height models, CHM, LiDAR, unmanned aerial vehicle, UAV

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Authors: Sungchul Ha, Hyunwoo Kim

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In mobile ad hoc networks, all nodes in a network only have limited resources and calculation ability. Therefore communication topology which have long lifetime is good for all nodes in mobile ad hoc networks. There are a variety of researches on security problems in wireless ad hoc networks. The existing many researches try to make efficient security schemes to reduce network power consumption and enhance network lifetime. Because a new node can join the network at any time, the wireless ad hoc networks are exposed to various threats and can be destroyed by attacks. Resource consumption is absolutely necessary to secure networks, but more resource consumption can be a critical problem to network lifetime. This paper focuses on efficient monitoring node distribution to enhance network lifetime in wireless ad hoc networks. Since the wireless ad hoc networks cannot use centralized infrastructure and security systems of wired networks, a new special IDS scheme is necessary. The scheme should not only cover all nodes in a network but also enhance the network lifetime. In this paper, we propose an efficient IDS node distribution scheme using minimum spanning tree (MST) method. The simulation results show that the proposed algorithm has superior performance in comparison with existing algorithms.

Keywords: MANETs, IDS, power control, minimum spanning tree

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Authors: Saba Ebrahimi, Hedieh Ashrafi

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Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations.

Keywords: classification methods, support vector machine, decision tree, forest cover-type dataset

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Authors: Maryam Vaezjalali, Koroush Rahimian, Maryam Asli, Tahmineh Kandelouei, Foad Davoodbeglou, Amir H. Kashi

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Objectives: The present study was conducted to assess the HBV molecular profiles including genotypes, subgenotypes, subtypes & mutations in hepatitis B genes. Materials/Patients and Methods: This study was conducted on 229 intravenous drug users who referred to three Drop- in-Centers and a hospital in Tehran. HBV DNA was extracted from HBsAg positive serum samples and amplified by Nested PCR. HBV genotype, subgenotypes, subtype and genes mutation were determined by direct sequencing. Phylogenetic tree was constructed using neighbor- joining (NJ) method. Statistical analyses were carried out by SPSS 20. Results: HBV DNA was found in 3 HBsAg positive cases. Phylogenetic tree of derived HBV DNAs showed the existence of genotype D (subgenotype D1, subtype ayw2). Also immune escape mutations were determined in S gene. Conclusion: There were a few variations and genotypes and subtypes among infected intravenous drug users. This study showed the predominance of genotype D among intravenous drug users. Our study concurs with other reports from Iran, that all showing currently only genotype D is the only detectable genotype in Iran.

Keywords: drug users, genotype, HBV, phylogenetic tree

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Authors: Nurten Akgun, Dilum Dissanayake, Neil Thorpe, Margaret C. Bell

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Cycling has gained a great attention with comparable speeds, low cost, health benefits and reducing the impact on the environment. The main challenge associated with cycling is the provision of safety for the people choosing to cycle as their main means of transport. From the road safety point of view, cyclists are considered as vulnerable road users because they are at higher risk of serious casualty in the urban network but more specifically at roundabouts. This research addresses the development of an enhanced mathematical model by including a broad spectrum of casualty related variables. These variables were geometric design measures (approach number of lanes and entry path radius), speed limit, meteorological condition variables (light, weather, road surface) and socio-demographic characteristics (age and gender), as well as contributory factors. Contributory factors included driver’s behavior related variables such as failed to look properly, sudden braking, a vehicle passing too close to a cyclist, junction overshot, failed to judge other person’s path, restart moving off at the junction, poor turn or manoeuvre and disobeyed give-way. Tyne and Wear in the UK were selected as a case study area. The cyclist casualty data was obtained from UK STATS19 National dataset. The reference categories for the regression model were set to slight and serious cyclist casualties. Therefore, binary logistic regression was applied. Binary logistic regression analysis showed that approach number of lanes was statistically significant at the 95% level of confidence. A higher number of approach lanes increased the probability of severity of cyclist casualty occurrence. In addition, sudden braking statistically significantly increased the cyclist casualty severity at the 95% level of confidence. The result concluded that cyclist casualty severity was highly related to approach a number of lanes and sudden braking. Further research should be carried out an in-depth analysis to explore connectivity of sudden braking and approach number of lanes in order to investigate the driver’s behavior at approach locations. The output of this research will inform investment in measure to improve the safety of cyclists at roundabouts.

Keywords: binary logistic regression, casualty severity, cyclist safety, roundabout

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Authors: R. Gary Black, Abolhassan Astaneh-Asl

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The International Building Code (IBC) and the California Building Code (CBC) both recognize four basic types of steel seismic resistant frames; moment frames, concentrically braced frames, shear walls and eccentrically braced frames. Based on specified geometries and detailing, the seismic performance of these steel frames is well understood. In 2011, the authors designed an innovative steel braced frame system with tapering members in the general shape of a branching tree as a seismic retrofit solution to an existing four story “lift-slab” building. Located in the seismically active San Francisco Bay Area of California, a frame of this configuration, not covered by the governing codes, would typically require model or full scale testing to obtain jurisdiction approval. This paper describes how the theories, protocols, and code requirements of eccentrically braced frames (EBFs) were employed to satisfy the 2009 International Building Code (IBC) and the 2010 California Building Code (CBC) for seismically resistant steel frames and permit construction of these nonconforming geometries.

Keywords: eccentrically braced frame, lift slab construction, seismic retrofit, shear link, steel design

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Authors: Jeongyeob Hong, Myeonghoon Park, Taeson Yoon

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After the advent of Achaeans which utilize different metabolism pathway and contain conspicuously different cellular structure, they have been recognized as possible materials for developing quality of human beings. Among diverse Achaeans, in this paper, we compared 16s RNA Sequences of four different species of Thermococcus: Achaeans genus specialized in sulfur-dealing metabolism. Four Species, Barophilus, Kodakarensis, Hydrothermalis, and Onnurineus, live near the hydrothermal vent that emits extreme amount of sulfur and heat. By comparing ribosomal sequences of aforementioned four species, we found similarities in their sequences and expressed protein, enabling us to expect that certain ribosomal sequence or proteins are vital for their survival. Apriori algorithms and Decision Tree were used. for comparison.

Keywords: Achaeans, Thermococcus, apriori algorithm, decision tree

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Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Wassana Naiyapo, Atichat Sangtong

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The processes in software development by Object Oriented methodology have many stages those take time and high cost. The inconceivable error in system analysis process will affect to the design and the implementation process. The unexpected output causes the reason why we need to revise the previous process. The more rollback of each process takes more expense and delayed time. Therefore, the good test process from the early phase, the implemented software is efficient, reliable and also meet the user’s requirement. Unified Modelling Language (UML) is the tool which uses symbols to describe the work process in Object Oriented Analysis (OOA). This paper presents the approach for automatically generated UML use case diagram and test cases. UML use case diagram is generated from the event table and test cases are generated from use case specifications and Graphic User Interfaces (GUI). Test cases are derived from the Classification Tree Method (CTM) that classify data to a node present in the hierarchy structure. Moreover, this paper refers to the program that generates use case diagram and test cases. As the result, it can reduce work time and increase efficiency work.

Keywords: classification tree method, test case, UML use case diagram, use case specification

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Authors: Farshad Ghassemi Toosi, Nikola S. Nikolov

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We present a visualization technique for radial drawing of trees consisting of two slightly different algorithms. Both of them make use of node-link diagrams for visual encoding. This visualization creates clear drawings without edge crossing. One of the algorithms is suitable for real-time visualization of large trees, as it requires minimal recalculation of the layout if leaves are inserted or removed from the tree; while the other algorithm makes better utilization of the drawing space. The algorithms are very similar and follow almost the same procedure but with different parameters. Both algorithms assign angular coordinates for all nodes which are then converted into 2D Cartesian coordinates for visualization. We present both algorithms and discuss how they compare to each other.

Keywords: Radial drawing, Visualization, Algorithm, Use of node-link diagrams

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Authors: Hamidreza Ghazvinian, Khosro Ghazvinian, Touba Khodaiean

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The importance of evaporation estimation in water resources and agricultural studies is undeniable. Pan evaporation are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. In this research, intelligent models were investigated in estimating pan evaporation on a daily basis. Shahroud and Mayamey were considered as the studied cities. These two cities are located in Semnan province in Iran. The mentioned cities have dry weather conditions that are susceptible to high evaporation potential. Meteorological data of 11 years of synoptic stations of Shahrood and Mayamey cities were used. The intelligent models used in this study are Artificial Neural Network (ANN), Least Squares Support Vector Machine (LSSVM), and M5 tree models. Meteorological parameters of minimum and maximum air temperature (Tmax, Tmin), wind speed (WS), sunshine hours (SH), air pressure (PA), relative humidity (RH) as selected input data and evaporation data from pan (EP) to The output data was considered. 70% of data is used at the education level, and 30 % of the data is used at the test level. Models used with explanation coefficient evaluation (R2) Root of Mean Squares Error (RMSE) and Mean Absolute Error (MAE). The results for the two Shahroud and Mayamey stations showed that the above three models' operations are rather appropriate.

Keywords: pan evaporation, intelligent methods, shahroud, mayamey

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Authors: Mohammad H. Yarmohammadian, Fatemeh Rezaei

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Introduction: Improving patient safety in health systems is one of the main priorities in healthcare systems, so clinical risk management in organizations has become increasingly significant. Although several tools have been developed for clinical risk management, each has its own limitations. Aims: This study aims to develop a comprehensive tool that can complete the limitations of each risk assessment and management tools with the advantage of other tools. Methods: Procedure was determined in two main stages included development of an initial model during meetings with the professors and literature review, then implementation and verification of final model. Subjects and Methods: This study is a quantitative − qualitative research. In terms of qualitative dimension, method of focus groups with inductive approach is used. To evaluate the results of the qualitative study, quantitative assessment of the two parts of the fourth phase and seven phases of the research was conducted. Purposive and stratification sampling of various responsible teams for the selected process was conducted in the operating room. Final model verified in eight phases through application of activity breakdown structure, failure mode and effects analysis (FMEA), healthcare risk priority number (RPN), root cause analysis (RCA), FT, and Eindhoven Classification model (ECM) tools. This model has been conducted typically on patients admitted in a day-clinic ward of a public hospital for surgery in October 2012 to June. Statistical Analysis Used: Qualitative data analysis was done through content analysis and quantitative analysis done through checklist and edited RPN tables. Results: After verification the final model in eight-step, patient's admission process for surgery was developed by focus discussion group (FDG) members in five main phases. Then with adopted methodology of FMEA, 85 failure modes along with its causes, effects, and preventive capabilities was set in the tables. Developed tables to calculate RPN index contain three criteria for severity, two criteria for probability, and two criteria for preventability. Tree failure modes were above determined significant risk limitation (RPN > 250). After a 3-month period, patient's misidentification incidents were the most frequent reported events. Each RPN criterion of misidentification events compared and found that various RPN number for tree misidentification reported events could be determine against predicted score in previous phase. Identified root causes through fault tree categorized with ECM. Wrong side surgery event was selected by focus discussion group to purpose improvement action. The most important causes were lack of planning for number and priority of surgical procedures. After prioritization of the suggested interventions, computerized registration system in health information system (HIS) was adopted to prepare the action plan in the final phase. Conclusion: Complexity of health care industry requires risk managers to have a multifaceted vision. Therefore, applying only one of retrospective or prospective tools for risk management does not work and each organization must provide conditions for potential application of these methods in its organization. The results of this study showed that the integrated clinical risk management model can be used in hospitals as an efficient tool in order to improve clinical governance.

Keywords: failure modes and effective analysis, risk management, root cause analysis, model

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Authors: Hina Kausher, Sangita Srivastava

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In India, there is a lack of standard, systematic sizing approach for producing readymade garments. Garments manufacturing companies use their own created size tables by modifying international sizing charts of ready-made garments. The purpose of this study is to tabulate the anthropometric data which covers the variety of figure proportions in both height and girth. 3,000 data has been collected by an anthropometric survey undertaken over females between the ages of 16 to 80 years from some states of India to produce the sizing system suitable for clothing manufacture and retailing. This data is used for the statistical analysis of body measurements, the formulation of sizing systems and body measurements tables. Factor analysis technique is used to filter the control body dimensions from a large number of variables. Decision tree-based data mining is used to cluster the data. The standard and structured sizing system can facilitate pattern grading and garment production. Moreover, it can exceed buying ratios and upgrade size allocations to retail segments.

Keywords: anthropometric data, data mining, decision tree, garments manufacturing, sizing systems, ready-made garments

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Authors: Mina Adel Shokry Fahim, Jūratė Sužiedelytė Visockienė

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With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realisation often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.

Keywords: air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter

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Authors: Amira Zrelli, Tahar Ezzedine

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Routing protocol may enhance the lifetime of sensor network, it has a highly importance, especially in wireless sensor network (WSN). Therefore, routing protocol has a big effect in these networks, thus the choice of routing protocol must be studied before setting up our network. In this work, we implement the routing protocol collect tree protocol (CTP) which is one of the hierarchic protocols used in structural health monitoring (SHM). Therefore, to evaluate the performance of this protocol, we choice to work with Contiki system and Cooja simulator. By throughput and RSSI evaluation of each node, we will deduce about the utility of CTP in structural monitoring system.

Keywords: CTP, WSN, SHM, routing protocol

Procedia PDF Downloads 288

Authors: Laetitia Lemiere, Marie Gosme, Gerard Subsol, Marc Jaeger

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Agroforestry is recognized as a way of developing sustainable and resilient agriculture that can fight against climate change. However, the number of species combinations, spatial configurations, and management options for trees and crops is vast. These choices must be adapted to the pedoclimatic and socio-economic contexts and to the objectives of the farmer, who therefore needs support in designing his system. Participative design workshops are a good way to integrate the knowledge of several experts in order to design such complex systems. The design of agroforestry systems should take into account both spatial aspects (e.g., spacing of trees within the lines and between lines, tree line orientation, tree-crop distance, species spatial patterns) and temporal aspects (e.g., crop rotations, tree thinning and pruning, tree planting in the case of successional agroforestry). Furthermore, the interactions between trees and crops evolve as the trees grow. However, agroforestry design workshops generally emphasize the spatial aspect only through the use of static tokens to represent the different species when designing the spatial configuration of the system. Augmented reality (AR) may overcome this limitation, allowing to visualize dynamic representations of trees and crops, and also their interactions, while at the same time retaining the possibility to physically interact with the system being designed (i.e., move trees, add or remove species, etc.). We propose an ergonomic digital solution capable of assisting a group of agroforestry experts to design an agroforestry system and to represent it. We investigated the use of web-based marker-based AR that does not require specific hardware and does not require specific installation so that all users could use their own smartphones right out of the pocket. We developed a prototype mobilizing the AR.js, ArToolKit.js, and Three.js open source libraries. In our implementation, we gradually build a virtual agroforestry system pattern scene from the users' interactions. A specific set of markers initialize the scene properties, and the various plant species are added and located during the workshop design session. The full virtual scene, including the trees positions with their neighborhood, are saved for further uses, such as virtual, augmented instantiation in the farmer fields. The number of tree species available in the application is gradually increasing; we mobilize 3D digital models for walnut, poplar, wild cherry, and other popular species used in agroforestry systems. The prototype allows shadow computations and the representation of trees at various growth stages, as well as different tree generations, and is thus able to visualize the dynamics of the system over time. Future work will focus on i) the design of complex patterns mobilizing several tree/shrub organizations, not restricted to lines; ii) the design of interfaces related to cultural practices, such as clearing or pruning; iii) the representation of tree-crop interactions. Beside tree shade (light competition), our objective is to represent also below-ground competitions (water, nitrogen) or other variables of interest for the design of agroforestry systems (e.g., predicted crop yield).

Keywords: agroforestry system design, augmented reality, marker-based AR, participative design, web-based AR

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Authors: Julio Albuja, David Zaldumbide

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Data analysis is an important step before taking a decision about money. The aim of this work is to analyze the factors that influence the final price of the houses through data mining algorithms. To our best knowledge, previous work was researched just to compare results. Furthermore, before using the data of the data set, the Z-Transformation were used to standardize the data in the same range. Hence, the data was classified into two groups to visualize them in a readability format. A decision tree was built, and graphical data is displayed where clearly is easy to see the results and the factors' influence in these graphics. The definitions of these methods are described, as well as the descriptions of the results. Finally, conclusions and recommendations are presented related to the released results that our research showed making it easier to apply these algorithms using a customized data set.

Keywords: algorithms, data, decision tree, transformation

Procedia PDF Downloads 368