Search results for: decision tree forest

Authors: Arzyana Sunkar, Yanto Santosa, Siti Badriyah Rushayati

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Indonesia has experienced annual forest fires that have rapidly destroyed and degraded its forests. Fires in the peat swamp forests of Riau Province, have set the stage for problems to worsen, this being the ecosystem most prone to fires (which are also the most difficult, to extinguish). Despite various efforts to curb deforestation, and forest degradation processes, severe forest fires are still occurring. To find an effective solution, the basic causes of the problems must be identified. It is therefore critical to have an in-depth understanding of the underlying causal factors that have contributed to deforestation and forest degradation as a whole, in order to attain reductions in their rates. An assessment of the drivers of deforestation and forest degradation was carried out, in order to design and implement measures that could slow these destructive processes. Research was conducted in Giam Siak Kecil–Bukit Batu Biosphere Reserve (GSKBB BR), in the Riau Province of Sumatera, Indonesia. A biosphere reserve was selected as the study site because such reserves aim to reconcile conservation with sustainable development. A biosphere reserve should promote a range of local human activities, together with development values that are in line spatially and economically with the area conservation values, through use of a zoning system. Moreover, GSKBB BR is an area with vast peatlands, and is experiencing forest fires annually. Various factors were analysed to assess the drivers of deforestation and forest degradation in GSKBB BR; data were collected from focus group discussions with stakeholders, key informant interviews with key stakeholders, field observation and a literature review. Landsat satellite imagery was used to map forest-cover changes for various periods. Analysis of landsat images, taken during the period 2010-2014, revealed that within the non-protected area of core zone, there was a trend towards decreasing peat swamp forest areas, increasing land clearance, and increasing areas of community oil-palm and rubber plantations. Fire was used for land clearing and most of the forest fires occurred in the most populous area (the transition area). The study found a relationship between the deforested/ degraded areas, and certain distance variables, i.e. distance from roads, villages and the borders between the core area and the buffer zone. The further the distance from the core area of the reserve, the higher was the degree of deforestation and forest degradation. Research findings suggested that agricultural expansion may be the direct cause of deforestation and forest degradation in the reserve, whereas socio-economic factors were the underlying driver of forest cover changes; such factors consisting of a combination of socio-cultural, infrastructural, technological, institutional (policy and governance), demographic (population pressure) and economic (market demand) considerations. These findings indicated that local factors/problems were the critical causes of deforestation and degradation in GSKBB BR. This research therefore concluded that reductions in deforestation and forest degradation in GSKBB BR could be achieved through ‘local actor’-tailored approaches such as community empowerment

Keywords: Actor-led solution, community empowerment, drivers of deforestation and forest degradation, Giam Siak Kecil – Bukit Batu Biosphere Reserve

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Authors: Marcelo Araujo da Nóbrega, Ariel Moura Vilas Boas

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This study aims to characterize the physicochemical aspects of the soils of southeastern Chapada Diamantina - Bahia related to the phytophysiognomies of this area, rupestrian field, small savanna (savanna fields), small dense savanna (savanna fields), savanna (Cerrado), dry thorny forest (Caatinga), dry thorny forest/savanna, scrub (Carrasco - ecotone), forest island (seasonal semi-deciduous forest - Capão) and seasonal semi-deciduous forest. To achieve the research objective, soil samples were collected in each plant formation and analyzed in the soil laboratory of ESALQ - USP in order to identify soil fertility through the determination of pH, organic matter, phosphorus, potassium, calcium, magnesium, potential acidity, sum of bases, cation exchange capacity and base saturation. The composition of soil particles was also checked; that is, the texture, step made in the terrestrial ecosystems laboratory of the Department of Ecology of USP and in the soil laboratory of ESALQ. Another important factor also studied was to show the variations in the vegetation cover in the region as a function of soil moisture in the different existing physiographic environments. Another study carried out was a comparison between the average soil moisture data with precipitation data from three locations with very different phytophysiognomies. The soils found in this part of Bahia can be classified into 5 classes, with a predominance of oxisols. All of these classes have a great diversity of physical and chemical properties, as can be seen in photographs and in particle size and fertility analyzes. The deepest soils are located in the Central Pediplano of Chapada Diamantina where the dirty field, the clean field, the executioner and the semideciduous seasonal forest (Capão) are located, and the shallower soils were found in the rupestrian field, dry thorny forest, and savanna fields, the latter located on a hillside. As for the variations in water in the region's soil, the data indicate that there were large spatial variations in humidity in both the rainy and dry periods.

Keywords: Bahia, Brazil, chapada diamantina, phytophysiognomies, soils

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Authors: Tigabu Dagne Akal

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While advances in computer and communications technology have made the network ubiquitous, they have also rendered networked systems vulnerable to malicious attacks devised from a distance. These attacks or intrusions start with attackers infiltrating a network through a vulnerable host and then launching further attacks on the local network or Intranet. Nowadays, system administrators and network professionals can attempt to prevent such attacks by developing intrusion detection tools and systems using data mining technology. In this study, the experiments were conducted following the Knowledge Discovery in Database Process Model. The Knowledge Discovery in Database Process Model starts from selection of the datasets. The dataset used in this study has been taken from Massachusetts Institute of Technology Lincoln Laboratory. After taking the data, it has been pre-processed. The major pre-processing activities include fill in missed values, remove outliers; resolve inconsistencies, integration of data that contains both labelled and unlabelled datasets, dimensionality reduction, size reduction and data transformation activity like discretization tasks were done for this study. A total of 21,533 intrusion records are used for training the models. For validating the performance of the selected model a separate 3,397 records are used as a testing set. For building a predictive model for intrusion detection J48 decision tree and the Naïve Bayes algorithms have been tested as a classification approach for both with and without feature selection approaches. The model that was created using 10-fold cross validation using the J48 decision tree algorithm with the default parameter values showed the best classification accuracy. The model has a prediction accuracy of 96.11% on the training datasets and 93.2% on the test dataset to classify the new instances as normal, DOS, U2R, R2L and probe classes. The findings of this study have shown that the data mining methods generates interesting rules that are crucial for intrusion detection and prevention in the networking industry. Future research directions are forwarded to come up an applicable system in the area of the study.

Keywords: intrusion detection, data mining, computer science, data mining

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Authors: B. Zerouali, M. Kara, B. Hamaidi, H. Mahdjoub, S. Rouabhia

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In this paper, a safety analysis for crude oil tanks to prevent undesirable events that may cause catastrophic accidents. The estimation of the probability of damage to industrial systems is carried out through a series of steps, and in accordance with a specific methodology. In this context, this work involves developing an assessment tool and risk analysis at the level of crude oil tanks system, based primarily on identification of various potential causes of crude oil tanks fire and explosion by the use of Fault Tree Analysis (FTA), then improved risk modelling by Bayesian Networks (BNs). Bayesian approach in the evaluation of failure and quantification of risks is a dynamic analysis approach. For this reason, have been selected as an analytical tool in this study. Research concludes that the Bayesian networks have a distinct and effective method in the safety analysis because of the flexibility of its structure; it is suitable for a wide variety of accident scenarios.

Keywords: bayesian networks, crude oil tank, fault tree, prediction, safety

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Authors: Barbora Chmelíková

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Making sound financial decisions is an essential skill which can have an impact on life of each consumer of financial products. The aim of this paper is to examine decision-making concerning financial matters and personal finance. The selected target group was university students majoring in finance related fields. The study was conducted in the Czech Republic at Masaryk University in 2015. In order to analyze financial decision-making questions related to basic finance decisions were developed to address the research objective. The results of the study suggest gaps in detecting best solutions to given financial decision-making questions among finance students. The analysis results indicate relation between financial decision-making and own experience with holding and using concrete financial products.

Keywords: financial decision-making, financial literacy, personal finance, university students

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Authors: Y. Cochet, A. Achim, Tom Flatman, J-C. Domec, J. Ogée, L. Wingate, Ram Oren

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It is accepted that atmospheric CO2 concentration will increase in the future. For the past 30 years, researchers have used FACE (Free-Air Carbon Dioxide Enrichment) facilities to study the development of terrestrial ecosystems under elevated CO2 (eCO2). Forest responses to eCO2 are likely to impact timber industries with potential feedbacks towards the atmosphere. The main objectives of this study were to examine whether eCO2 alone or in combination with N-fertilization alter wood properties and to identify changes in wood anatomy related to water transport. Wood disks were sampled at breast height from mature loblolly pine trees (Pinus taeda L.) harvested at the Duke FACE site (NC, USA). By measuring ring width and intra-ring changes in density (X-ray densitometry) and tracheid size (lumen and cell wall thickness) from pith to bark, the following hypotheses were tested: 1) eCO2 and N-fertilization interact positively to increase significantly above-ground primary productivity; 2) eCO2 and N-fertilization lead to a decrease in density; 3) eCO2 and N-fertilization increase lumen diameter and decrease cell wall thickness, thus affecting water transport capacity. Our results revealed a boost in earlywood tracheid production induced by eCO2 lasting a few years. The following decrease seemed to be buffered by N-fertilization. X-ray profiles did not show a marked decrease in wood density under eCO2 or N-fertilization, although there were changes in cell anatomical properties such as a reduction in cell-wall thickness and an increase in lumen diameter. If such effects of eCO2 are confirmed, forest management strategies for example N-fertilization should be redesigned.

Keywords: wood density, Duke FACE (free-air carbon dioxide enrichment), N fertilization, tree ring

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Authors: Marie Madeleine Sarzi Inacio, Ligiane Carolina Leite Dauzacker, Rodrigo Henriques Lopes Da Silva

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The climate changes resulting from human activity have increased interest in more sustainable production practices to reduce and offset pollutant emissions. Brazil, with its vast areas capable of carbon absorption, holds a significant advantage in this context. However, to optimize the country's sustainable potential, it is important to establish a robust carbon market with clear rules for the eligibility and validation of projects aimed at reducing and offsetting Greenhouse Gas (GHG) emissions. In this study, our objective is to conduct a feasibility analysis through a case study to evaluate the implementation of an urban forest credits standard in Brazil, using the Urban Forest Credits (UFC) model implemented in the United States as a reference. Thus, the city of Ribeirão Preto, located in Brazil, was selected to assess the availability of green areas. With the CO2 emissions value from the metalworking industry, it was possible to analyze information in the case study, considering the activity. The QGIS software was used to map potential urban forest areas, which can connect to various types of geospatial databases. Although the chosen municipality has little vegetative coverage, the mapping identified at least eight areas that fit the standard definitions within the delimited urban perimeter. The outlook was positive, and the implementation of projects like Urban Forest Credits (UFC) adapted to the Brazilian reality has great potential to benefit the country in the carbon market and contribute to achieving its Greenhouse Gas (GHG) emission reduction goals.

Keywords: carbon neutrality, metalworking industry, carbon credits, urban forestry credits

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Authors: Evagelos Korobilis-Magas, Adekunle Oke

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The constantly increasing tendency towards a global economy and the subsequent increase in exporting, as a result, has inevitably led to a growing interest in the topic of export success as well. Numerous studies, particularly in the past three decades, have examined a plethora of determinants to export performance. However, to the authors' best knowledge, no study up to date has ever considered decision quality as a potential antecedent to export success by attempting to test the relationship between decision quality and export performance. This is a surprising deficiency given that the export marketing literature has long ago suggested that quality decisions are regarded as the crucial intervening variable between sound decision–making and export performance. This study integrates the different definitions of decision quality proposed in the literature and the key themes incorporated therein and adapts it to an export context. Apart from laying the conceptual foundations for the delineation of this elusive but very important construct, this study is the first ever to test the relationship between decision quality and export performance. Based on survey data from a sample of 189 British export decision-makers and within a contingency theory framework, the results reveal that there is a direct, positive link between decision quality and export performance. This finding opens significant future research avenues and has very important implications for both theory and practice.

Keywords: export performance, decision quality, mixed methods, contingency theory

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Authors: Bundit Pungnirund

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This research aimed to study the relationship between marketing mix attitudes, motivation of buying decision and tendency of consumer decision making in buying the condominiums in Thailand. This study employed by survey and quantitative research. The questionnaire was used to collect the data from 400 sampled of customers who interested in buying condominium in Bangkok. The descriptive statistics and Pearson’s correlation coefficient analysis were used to analyze data. The research found that marketing mixed factors in terms of product and price were related to buying decision making tendency in terms of price and room size. Marketing mixed factors in terms of price, place and promotion were related to buying decision making tendency in term of word of mouth. Consumers’ buying motivation in terms of social acceptance, self-esteemed and self-actualization were related to buying decision making tendency in term of room size. In addition, motivation in self-esteemed was related to buying decision making tendency within a year.

Keywords: condominium, marketing mix, motivation, tendency of consumer decision making

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Authors: Soheila Sadeghi

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In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

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Authors: Sony Baral, Bijendra Basnyat, Kalyan Gauli

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Forests are depleted throughout the world in the 1990s, and since then, various efforts have been undertaken for the restoration of the forest. A government of Nepal promoted various community based forest management in which leasehold forestry was the one introduce in 1990s, aiming to restore degraded forests land. However, few attempts have been made to systematically evaluate its cost effectiveness. Hence the study assesses the cost effectiveness of leasehold forestry intervention in the mid-hill district of Nepal following the cost and benefit analysis approach. The study followed quasi-experimental design and collected costs and benefits information from 320 leasehold forestry groups (with intervention) and 154 comparison groups (without intervention) through household survey, forest inventory and then validated with the stakeholders’ consultative workshop. The study found that both the benefits and costs from intervention outweighed without situation. The members of leasehold forestry groups were generating multiple benefits from the forests, such as firewood, grasses, fodder, and fruits, whereas those from comparison groups were mostly getting a single benefit. Likewise, extent of soil carbon is high in leasehold forests. Average expense per unit area is high in intervention sites due to high government investment for capacity building. Nevertheless, positive net present value and internal rate of return was observed for both situations. However, net present value from intervention, i.e., leasehold forestry, is almost double compared to comparison sites, revealing that community are getting higher benefits from restoration. The study concludes that leasehold forestry is a highly cost-effective intervention that contributes towards forest restoration that brings multiple benefits to rural poor.

Keywords: cost effectiveness, economic efficiency, intervention, restoration, leasehold forestry, nepal

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Authors: Tatiana Tunon, Gottfried Pestal

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Government agencies responsible for the management of fisheries resources publish many types of grey literature, and these materials are increasingly accessible to the public on agency websites. However, scope and quality vary considerably, and end-users need meta-data about the report series when deciding whether to use the information (e.g. apply the methods, include the results in a systematic review), or when prioritizing materials for archiving (e.g. library holdings, reference databases). A proposed taxonomy for these report series was developed based on a review of 41 report series from 6 government agencies in 4 countries (Canada, New Zealand, Scotland, and United States). Each report series was categorized according to multiple criteria describing peer-review process, content, and purpose. A robust classification tree was then fitted to these descriptions, and the resulting taxonomic groups were used to compare agency output from 4 countries using reports available in their online repositories.

Keywords: classification tree, fisheries, government, grey literature

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Authors: Shahram Payandeh

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This paper presents an overview of a model which can be used as a part of a decision support system when fusing information from multiple sensing environment. Data fusion has been widely studied in the past few decades and numerous frameworks have been proposed to facilitate decision making process under uncertainties. Multi-sensor data fusion technology plays an increasingly significant role during people tracking and activity recognition. This paper presents an overview of a quantum model as a part of a decision-making process in the context of multi-sensor data fusion. The paper presents basic definitions and relationships associating the decision-making process and quantum model formulation in the presence of uncertainties.

Keywords: quantum model, sensor space, sensor network, decision support

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Authors: Tulio Sulbaran, Sandeep Langar

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Architects are an integral part of the construction industry and are continuously incorporating decisions that influence projects during their life cycle. These decisions aim at selecting best alternative from the ones available. Unfortunately, this decision making process is mainly unexplored in the construction industry. No instrument to measure construction decision, based on knowledgebase of decision-makers, has existed. Additionally, limited literature is available on the topic. Recently, an instrument to gain an understanding of the construction decision-making process was developed by Dr. Tulio Sulbaran from the University of Texas, San Antonio. The instrument’s name is 'Construction Decision Making Inventory (CDMI)'. The CDMI is an innovative idea to measure the 'What? When? How? Moreover, Who?' of the construction decision-making process. As an innovative idea, its statistical validity (accuracy of the assessment) is yet to be assessed. Thus, the purpose of this paper is to describe the results of a case study with architects in the south-east of the United States aimed to determine the CDMI validity. The results of the case study are important because they assess the validity of the tool. Furthermore, as the architects evaluated each question within the measurements, this study is also guiding the enhancement of the CDMI.

Keywords: decision, support, inventory, architect

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Authors: M. A. Amezcua-Allieri, E. Torres, J. A. Zermeño Eguía-Lis, M. Magdaleno, L. A. Melgarejo, E. Palmerín, A. Rosas, D. López, J. Aburto

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The use of biomass to produce renewable energy is one of the forms that can be used to reduce the impact of energy production. Like any other energy resource, there are limitations for biomass use, and it must compete not only with fossil fuels but also with other renewable energy sources such as solar or wind energy. Combustion is currently the most efficient and widely used waste-to-energy process, in the areas where direct use of biomass is possible, without the need to make large transfers of raw material. Many industrial facilities can use agricultural or forestry waste, straw, chips, bagasse, etc. in their thermal systems without making major transformations or adjustments in the feeding to the ovens, making this waste an attractive and cost-effective option in terms of availability, access, and costs. In spite of the facilities and benefits, the environmental reasons (emission of gases and particulate material) are decisive for its use for energy purpose. This paper describes a valorization of residues from forest industry to generate energy, using a case study.

Keywords: bioenergy, forest waste, life-cycle assessment, waste-to-energy, electricity

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Authors: Prem Bahadur Giri, Mathinee Yucharoen

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Community Forest User Groups (CFUGs) have been the core stone of forest management efforts in Nepal. Due to the lack of a smooth transition into the local governance structure in 2017, policy instruments have not been effectively cascaded to the local level, creating ambiguity and inconsistency in forest governance. Descriptive mixed-method research was performed with community users and stakeholders of Tarpakha community forest, Siranchowk Rural Municipality, to understand the role of the political economy in CFUG management. A household survey was conducted among 100 households (who also are existing members of the Tarpakha CFUG) to understand and document their energy consumption preferences and practices. Likewise, ten key informant interviews and five focus group discussions with the municipality and forest management officials were also conducted to have a wider overview of the factors and political, socio-economic, and religious contexts behind the utilization of renewable energy for sustainable development. Findings from our study suggest only 3% of households use biogas as the main source of energy. The rest of the households mention liquid petroleum gas (LPG), electricity and firewood as major sources of energy for domestic purposes. Community members highlighted the difficulty in accessing firewood due to strict regulations from the CFUG, lack of cattle and manpower to rear cattle to produce cow dung (for biogas), lack of technical expertise at the community level for the operation and maintenance of solar energy, among others as challenges of resource. Likewise, key informants have mentioned policy loopholes at both the federal and local levels, especially with regard to the promotion of alternative or renewable energy, as there are no clear mandates and provisions to regulate the renewable energy industry. The study recommends doing an in-depth study on the feasibility of renewable energy sources, especially in the context of CFUGs, where biodiversity conservation aspects need to be equally taken into consideration while thinking of the promotion and expansion of renewable energy sources.

Keywords: community forest, renewable energy, sustainable development, Nepal

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Authors: P. J. Kagosi, M. Mndolwa, E. Japhate

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The study was conducted to communities adjacent MNR, Lushoto district, Tanzania. The MNR is one of the nine nature reserves in the Eastern Arc Mountains of Tanzania with an area of 8,700ha with high biological diversity. However, biodiversity in MNR have been threatened by increasing human activities for livelihood in 1970s. The AF systems in the study area was practised since 1980s however, no study was conducted on AF impacts. This paper presents the influence of AF on livelihood of communities adjacent MNR and biodiversity conservation. Qualitative and quantitative data were collected using socio-economic survey and botanical surveys. Data were analysed using Statistical Packages for Social Sciences and content analysis. The study found that in 1970s free livestock grazing caused considerable surface runoff, soil erosion and reduction of crop production. Since 1980s, the study area received various interventions based on the land conservations and improved livelihood through practising AF systems. It was further found that the AF farming improved crop productivity, reduced soil erosion, increased firewood (80.2%) and other forest products availability and AF encouraged community members practicing indoor livestock keeping.The dominant agroforestry tree found in the study area is grevillea reported by 74.1% of respondents planting an average of 40 trees. The study found that the AF reduced pressure to MNR as forest products and fodders were obtained from community's farms in turn, currently water flow from MNR has been increased. Thus AF products support livelihood needs and conserve biodiversity. The study recommends continuity education on new AF technology packages.

Keywords: impact of agroforestry, biodiversity management, communities’ livelihoods, Magamba nature reserve

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Authors: Ji-Suk Kim, Dong-Pil Kim, Kee-Rae Gang, Yoon Ho Choi

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This study was conducted to investigate the characteristics of butterfly communities according to the habitat characteristics of Korean veins. The survey sites were 12 mountains located in the vein, and 12~30 quadrats (200 in total) were set. The species richness and biodiversity were different according to land use type. Two types of land use (forest and graveyard) showed lower species diversity index values ​​than other land use types. The species abundance was low in the forest and graveyards, and grasslands, forest tops, cultivated areas and urban areas showed relatively high species richness. The altitude was not statistically significant with the number of species of butterflies and biodiversity index. The degree of canopy closure showed a negative correlation. As a result of interspecific correlation analysis, it was confirmed that there was a very high correlation (R2=0.746) between Lycaena phlaeas and Pseudozizeeria maha argia, Choaspes benjaminii japonica and Argyronome ruslana.

Keywords: land use type, species diversity index, correlation, canopy closure

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Authors: Dua Hişam, Serhat İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

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Authors: Jiameng Wang, Yunfei Yin, Xiyu Deng

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Parallel optimization is one of the important research topics of data mining at this stage. Taking Classification and Regression Tree (CART) parallelization as an example, this paper proposes a parallel data mining algorithm based on SSP-OGini-PCCP. Aiming at the problem of choosing the best CART segmentation point, this paper designs an S-SP model without data association; and in order to calculate the Gini index efficiently, a parallel OGini calculation method is designed. In addition, in order to improve the efficiency of the pruning algorithm, a synchronous PCCP pruning strategy is proposed in this paper. In this paper, the optimal segmentation calculation, Gini index calculation, and pruning algorithm are studied in depth. These are important components of parallel data mining. By constructing a distributed cluster simulation system based on SPARK, data mining methods based on SSP-OGini-PCCP are tested. Experimental results show that this method can increase the search efficiency of the best segmentation point by an average of 89%, increase the search efficiency of the Gini segmentation index by 3853%, and increase the pruning efficiency by 146% on average; and as the size of the data set increases, the performance of the algorithm remains stable, which meets the requirements of contemporary massive data processing.

Keywords: classification, Gini index, parallel data mining, pruning ahead

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Anshu Siwach, Qianlai Zhuang, Ratul Baishya

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Context: This study focuses on the influence of moss cover and seasonality on soil microbial biomass and enzymatic activity in different Central Himalayan temperate forest types. Soil microbial biomass and enzymes are key indicators of microbial communities in soil and provide information on soil properties, microbial status, and organic matter dynamics. The activity of microorganisms in the soil varies depending on the vegetation type and environmental conditions. Therefore, this study aims to assess the effects of moss cover, seasons, and different forest types on soil microbial biomass carbon (SMBC), soil microbial biomass nitrogen (SMBN), and soil enzymatic activity in the Central Himalayas, Uttarakhand, India. Research Aim: The aim of this study is to evaluate the levels of SMBC, SMBN, and soil enzymatic activity in different temperate forest types under the influence of two ground covers (soil with and without moss cover) during the rainy and winter seasons. Question Addressed: This study addresses the following questions: 1. How does the presence of moss cover and seasonality affect soil microbial biomass and enzymatic activity? 2. What is the influence of different forest types on SMBC, SMBN, and enzymatic activity? Methodology: Soil samples were collected from different forest types during the rainy and winter seasons. The study utilizes the chloroform-fumigation extraction method to determine SMBC and SMBN. Standard methodologies are followed to measure enzymatic activities, including dehydrogenase, acid phosphatase, aryl sulfatase, β-glucosidase, phenol oxidase, and urease. Findings: The study reveals significant variations in SMBC, SMBN, and enzymatic activity under different ground covers, within the rainy and winter seasons, and among the forest types. Moss cover positively influences SMBC and enzymatic activity during the rainy season, while soil without moss cover shows higher values during the winter season. Quercus-dominated forests, as well as Cupressus torulosa forests, exhibit higher levels of SMBC and enzymatic activity, while Pinus roxburghii forests show lower levels. Theoretical Importance: The findings highlight the importance of considering mosses in forest management plans to improve soil microbial diversity, enzymatic activity, soil quality, and health. Additionally, this research contributes to understanding the role of lower plants, such as mosses, in influencing ecosystem dynamics. Conclusion: The study concludes that moss cover during the rainy season significantly influences soil microbial biomass and enzymatic activity. Quercus and Cupressus torulosa dominated forests demonstrate higher levels of SMBC and enzymatic activity, indicating the importance of these forest types in sustaining soil microbial diversity and soil health. Including mosses in forest management plans can improve soil quality and overall ecosystem dynamics.

Keywords: moss cover, seasons, soil enzymes, soil microbial biomass, temperate forest types

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Authors: Karrar Raoof Kareem Kamoona, Enas Fathi Taher AlHares, Zeynep Isik

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The various disciplines in the construction industry and the co-existence of the people in the various disciplines are what builds well-developed, closely-knit interpersonal skills at various hierarchical levels thus leading to a varied way of leadership. The varied decision making aspects during the lifecycle of a project include: autocratic, participatory and last but not least, free-rein. We can classify some of the decision makers in the construction industry in a hierarchical manner as follows: project executive, project manager, superintendent, office engineer and finally the field engineer. This survey looked at how decisions are made during the construction period by the key stakeholders in the project. From the paper it is evident that the three decision making aspects can be used at different times or at times together in order to bring out the best leadership decision. A blend of different leadership styles should be used to enhance the success rate during the project lifecycle.

Keywords: leadership style, construction, decision-making, built environment

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Authors: W. Promprom, W. Chatan

Abstract:

Leguminosae is a large plant family and its members are important for local people utilization in the Northeast of Thailand. This research aimed to survey medicinal plants in this family in Kantharalak Community forest. The plant collection and exploration were made from October 2017 to September 2018. Folk medicinal uses were studied by interviewing villagers and folk medicine healers living around the community forest by asking about local names, using parts, preparation and properties. The results showed that 65 species belonging to 40 genera were found. Among these, 30 species were medicinal plant. The most used plant parts were leaf. Decoction and drinking were mostly preparation method and administration mode used. All medicinal plants could be categorized into 17 diseases/symptoms. Most plant (56.66%) were used for fever. The voucher specimens were deposited in Department of Biology, Faculty of Science, Mahasarakham University, Thailand. Therefore, the data from this study might be widely used by the local area and further scientific study.

Keywords: ethnobotany, ethnophamacology, medicinal plant, taxonomy, utilization

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Authors: Yeliz Sarı Nayim, B. Niyami Nayim

Abstract:

Biotopes such as forest areas rich in biodiversity, wetlands, hedgerows and woodlands play important ecological roles in agricultural landscapes. Of these semi-natural areas and features, hedgerows are the most common landscape elements. Their most significant features are that they serve as a barrier between the agricultural lands, serve as shelter, add aesthetical value to the landscape and contribute significantly to the wildlife and biodiversity. Hedgerows surrounding agricultural landscapes also provide an important habitat for pollinators which are important for agricultural production. This study looks into the identification of hedgerows in agricultural lands in the Mugada rural area within Bartın province, Turkey. From field data and-and satellite images, it is clear that in this area, especially around rural settlements, large forest areas have been cleared for settlement and agriculture. A network of hedgerows is also apparent, which might potentially play an important role in the otherwise open agricultural landscape. We found that these hedgerows serve as an ecological and biological corridor, linking forest ecosystems. Forest patches of different sizes and creating a habitat network across the landscape. Some examples of this will be presented. The overall conclusion from the study is that ecologically, biologically and aesthetically important hedge biotopes should be maintained in the long term in agricultural landscapes such as this. Some suggestions are given for how they could be managed sustainably into the future.

Keywords: agricultural biotopes, Hedgerows, landscape ecology, Turkey

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Authors: Noppadol Sritragool

Abstract:

The objectives of this research were to investigate the decision of passengers who choose to fry with low cost airlines and to study marketing factors which have the influence to the decision to choose each low cost airlines. This paper was a quantitative research technique. A total of 400 low cost airlines’ passengers were interviewed via English questionnaire to collect the respondents’ opinions. The findings revealed that respondents were male and female at a similar proportion. The majority had at least an undergraduate degree, have a lower management level jobs, and had income in the range of 25,000 -35,000 baht per month.. In addition, the findings also revealed that the first three marketing factors influencing the decision of the respondents to choose low-cost airlines were low price, direct flight, and online system.

Keywords: decision to choose, marketing factors, low-cost airlines

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Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

Abstract:

Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

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Authors: Narissara Nuthammachot, Dimitris Stratoulias

Abstract:

Fire is one of the main causes of environmental and ecosystem change. Therefore, it is a challenging task for fire risk assessment fire potential mapping. The study area is Hua Sai district, Nakorn Sri Thammarat province, which covers in a part of peat swamp forest areas. 55 fire points in peat swamp areas were reported from 2012 to 2016. Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) methods were selected for this study. The risk fire area map was arranged on these factors; elevation, slope, aspect, precipitation, distance from the river, distance from town, and land use. The results showed that the predicted fire risk areas are found to be in appreciable reliability with past fire events. The fire risk map can be used for the planning and management of fire areas in the future.

Keywords: analytic hierarchy process, fire risk assessment, geographic information system, peat swamp forest

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Authors: Kevin Soubly, Kaysara Khatun

Abstract:

This study applies the existing Middle-Out Perspective (MOP) as a complementing analytical alternative to the customary dichotomous options of top-down vs. bottom-up strategies of international development and commons governance. It expands the framework by applying it to a new context of land management and environmental change, enabling fresh understandings of decision making around land use. Using a case study approach in Central Kalimantan, Indonesia among a village of indigenous Dayak, this study explores influences from both internal and external middle actors, utilizing qualitative empirical evidence and incorporating responses across 25 village households and 11 key stakeholders. Applying the factors of 'agency' and 'capacity' specific to the MOP, this study demonstrates middle actors’ unique capabilities and criticality to change due to their influence across various levels of decision-making. Study results indicate that middle actors play a large role, both passively and actively, both directly and indirectly, across various levels of decision-making, perception-shaping, and commons governance. In addition, the prominence of novel 'passive' middle actors, such as the internet, can provide communities themselves with a level of agency beyond that provided by other middle actors such as NGOs and palm oil industry entities – which often operate at the behest of the 'top' or out of self-interest. Further, the study posits that existing development and decision-making frameworks may misidentify the 'bottom' as the 'middle,' raising questions about traditional development and livelihood discourse, strategies, and support, from agricultural production to forest management. In conclusion, this study provides recommendations including that current policy preconceptions be reevaluated to engage middle actors in locally-adapted, integrative manners in order to improve governance and rural development efforts more broadly.

Keywords: environmental management, governance, Indonesia, land use, middle actors, middle-out perspective

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Authors: Benlakhdar Mohamed Larbi, Yagoub Asma

Abstract:

Purpose: This study aims at highlighting the significance and importance of utilizing marketing information system (MKIS) on decision-making, by clarifying the need for quick and efficient decision-making due to time saving and preventing of duplication of work. Design, methodology, approach: The study shows the roles of each part of MKIS for developing marketing strategy, which present a real challenge to individuals and institutions in an era characterized by uncertainty and clarifying the importance of each part separately, depending on decision type and the nature of the situation. The empirical research method was evaluated by specialized experts, conducted by means of questionnaires. Correlation analysis was employed to test the validity of the procedure. Results: The empirical study findings confirmed positive relationships between the level of utilizing and adopting ‘decision support system and marketing intelligence’ and the success of an organizational decision-making, and provide the organization with a competitive advantage as it allows the organization to solve problems. Originality/value: The study offer better understanding of performance- increasing market share as an organizational decision making based on marketing information system.

Keywords: database, marketing research, marketing intelligence, decision support system, decision-making

Procedia PDF Downloads 306