Search results for: oil palm tree census

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: Timine Suoware, Sylvester Edelugo, Charles Amgbari

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Natural fibre composites (NFC) are becoming very attractive especially for automotive interior and non-structural building applications because they are biodegradable, low cost, lightweight and environmentally friendly. NFC are known to release high combustible products during exposure to heat atmosphere and this behaviour has raised concerns to end users. To improve on their fire response, flame retardants (FR) such as aluminium tri-hydroxide (ATH) and ammonium polyphosphate (APP) are incorporated during processing to delay the start and spread of fire. In this paper, APP was modified with Gum Arabic powder (GAP) and synergized with carbon black (CB) to form new FR species. Four FR species at 0, 12, 15 and 18% loading ratio were added to oil palm fibre polyester composite (OPFC) panels as follows; OPFC12%APP-GAP, OPFC15%APP-GAP/CB, OPFC18%ATH/APP-GAP and OPFC18%ATH/APPGAP/CB. The panels were produced using hand lay-up compression moulding and cured at room temperature. Specimens were cut from the panels and these were tested for ignition time (Tig), peak heat released rate (HRRp), average heat release rate (HRRavg), peak mass loss rate (MLRp), residual mass (Rm) and average smoke production rate (SPRavg) using cone calorimeter apparatus as well as the available flame energy (ɸ) in driving the flame using radiant panel flame spread apparatus. From the ignitability data obtained at 50 kW/m2 heat flux (HF), it shows that the hybrid FR modified with APP that is OPFC18%ATH/APP-GAP exhibited superior flame retardancy and the improvement was based on comparison with those without FR which stood at Tig = 20 s, HRRp = 86.6 kW/m2, HRRavg = 55.8 kW/m2, MLRp =0.131 g/s, Rm = 54.6% and SPRavg = 0.05 m2/s representing respectively 17.6%, 67.4%, 62.8%, 50.9%, 565% and 62.5% improvements less than those without FR (OPFC0%). In terms of flame spread, the least flame energy (ɸ) of 0.49 kW2/s3 for OPFC18%ATH/APP-GAP caused early flame regression. This was less than 39.6 kW2/s3 compared to those without FR (OPFC0%). It can be concluded that hybrid FR modified with APP could be useful in the automotive and building industries to delay the start and spread of fire.

Keywords: flame retardant, flame regression, oil palm fibre, composite panel

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Authors: Yunfeng Shan, Xiaoliang Wang, Youjun Zhou

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Whole-genome data from two lungfish species, along with other species, present a valuable opportunity to reassess the longstanding debate regarding the evolutionary relationships among tetrapods, lungfishes, and coelacanths. However, the use of bootstrap support has become outdated for large-scale phylogenomic data. Without robust phylogenetic support, the phylogenetic trees become meaningless. Therefore, it is necessary to re-evaluate the phylogenies of tetrapods, lungfishes, and coelacanths using novel measures of phylogenetic support specifically designed for phylogenomic data, as the previous phylogenies were based on 100% bootstrap support. Our findings consistently provide strong evidence favoring lungfish as the closest living relative of tetrapods. This conclusion is based on high gene support confidence with confidence intervals exceeding 95%, high internode certainty, and high gene concordance factor. The evidence stems from two datasets containing recently deciphered whole genomes of two lungfish species, as well as five previous datasets derived from lungfish transcriptomes. These results yield fresh insights into the three hypotheses regarding the phylogenies of tetrapods, lungfishes, and coelacanths. Importantly, these hypotheses are not mere conjectures but are substantiated by a significant number of genes. Analyzing real biological data further demonstrates that the inclusion of additional taxa diminishes the number of orthologues and leads to more diverse tree topologies. Consequently, gene trees and species trees may not be identical even when whole-genome sequencing data is utilized. However, it is worth noting that many gene trees can accurately reflect the species tree if an appropriate number of taxa, typically ranging from six to ten, are sampled. Therefore, it is crucial to carefully select the number of taxa and an appropriate outgroup while excluding fast-evolving taxa as outgroups to mitigate the adverse effects of long-branch attraction (LBA) and achieve an accurate reconstruction of the species tree. This is particularly important as more whole-genome sequencing data becomes available.

Keywords: gene support confidence (GSC), origin of land vertebrates, coelacanth, two whole genomes of lungfishes, confidence intervals

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Authors: Liang Cheng Chang, Wei Ju Huang, You Cheng Chen

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Investigation and research are keys for conjunctive use of surface and groundwater resources. The hydrogeological structure is an important base for groundwater analysis and simulation. Traditionally, the hydrogeological structure is artificially determined based on geological drill logs, the structure of wells, groundwater levels, and so on. In Taiwan, groundwater observation network has been built and a large amount of groundwater-level observation data are available. The groundwater level is the state variable of the groundwater system, which reflects the system response combining hydrogeological structure, groundwater injection, and extraction. This study applies analytical tools to the observation database to develop a methodology for the identification of confined and unconfined aquifers. These tools include frequency analysis, cross-correlation analysis between rainfall and groundwater level, groundwater regression curve analysis, and decision tree. The developed methodology is then applied to groundwater layer identification of two groundwater systems: Zhuoshui River alluvial fan and Pingtung Plain. The abovementioned frequency analysis uses Fourier Transform processing time-series groundwater level observation data and analyzing daily frequency amplitude of groundwater level caused by artificial groundwater extraction. The cross-correlation analysis between rainfall and groundwater level is used to obtain the groundwater replenishment time between infiltration and the peak groundwater level during wet seasons. The groundwater regression curve, the average rate of groundwater regression, is used to analyze the internal flux in the groundwater system and the flux caused by artificial behaviors. The decision tree uses the information obtained from the above mentioned analytical tools and optimizes the best estimation of the hydrogeological structure. The developed method reaches training accuracy of 92.31% and verification accuracy 93.75% on Zhuoshui River alluvial fan and training accuracy 95.55%, and verification accuracy 100% on Pingtung Plain. This extraordinary accuracy indicates that the developed methodology is a great tool for identifying hydrogeological structures.

Keywords: aquifer identification, decision tree, groundwater, Fourier transform

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Authors: Katawut Kaewbanjong

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We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.

Keywords: prediction model, statistical analysis, software project, user satisfaction factor

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Authors: Melis Inalpulat, Levent Genc

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Land Use Land Cover (LULC) changes due to human activities and natural causes have become a major environmental concern. Assessment of temporal remote sensing data provides information about LULC impacts on environment. Land Surface Temperature (LST) is one of the important components for modeling environmental changes in climatological, hydrological, and agricultural studies. In this study, LULC changes (September 7, 1984 and July 8, 2014) especially in agricultural lands together with population changes (1985-2014) and LST status were investigated using remotely sensed and census data in South Marmara Watershed, Turkey. LULC changes were determined using Landsat TM and Landsat OLI data acquired in 1984 and 2014 summers. Six-band TM and OLI images were classified using supervised classification method to prepare LULC map including five classes including Forest (F), Grazing Land (G), Agricultural Land (A), Water Surface (W), and Residential Area-Bare Soil (R-B) classes. The LST image was also derived from thermal bands of the same dates. LULC classification results showed that forest areas, agricultural lands, water surfaces and residential area-bare soils were increased as 65751 ha, 20163 ha, 1924 ha and 20462 ha respectively. In comparison, a dramatic decrement occurred in grazing land (107985 ha) within three decades. The population increased % 29 between years 1984-2014 in whole study area. Along with the natural causes, migration also caused this increase since the study area has an important employment potential. LULC was transformed among the classes due to the expansion in residential, commercial and industrial areas as well as political decisions. In the study, results showed that agricultural lands around the settlement areas transformed to residential areas in 30 years. The LST images showed that mean temperatures were ranged between 26-32 °C in 1984 and 27-33 °C in 2014. Minimum temperature of agricultural lands was increased 3 °C and reached to 23 °C. In contrast, maximum temperature of A class decreased to 41 °C from 44 °C. Considering temperatures of the 2014 R-B class and 1984 status of same areas, it was seen that mean, min and max temperatures increased by 2 °C. As a result, the dynamism of population, LULC and LST resulted in increasing mean and maximum surface temperatures, living spaces/industrial areas and agricultural lands.

Keywords: census data, landsat, land surface temperature (LST), land use land cover (LULC)

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Authors: Orhun Aydin, Mark V. Janikas, Rodrigo Alves, Renato Assuncao

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In this paper, a tree-based perturbation methodology for regionalization inference is presented. Regionalization is a constrained optimization problem that aims to create groups with similar attributes while satisfying spatial contiguity constraints. Similar to any constrained optimization problem, the spatial constraint may hinder convergence to some global minima, resulting in spatially contiguous members of a group with dissimilar attributes. This paper presents a general methodology for rigorously perturbing spatial constraints through the use of random spanning trees. The general framework presented can be used to quantify the effect of the spatial constraints in the overall regionalization result. We compare several types of stochastic spanning trees used in inference problems such as fuzzy regionalization and determining the number of regions. Performance of stochastic spanning trees is juxtaposed against the traditional permutation-based hypothesis testing frequently used in spatial statistics. Inference results for fuzzy regionalization and determining the number of regions is presented on the Local Area Personal Incomes for Texas Counties provided by the Bureau of Economic Analysis.

Keywords: regionalization, constrained clustering, probabilistic inference, fuzzy clustering

Procedia PDF Downloads 218

Authors: Katia C. Bortoletto, Maria Isabel C. de Freitas, Rodrigo B. N. de Oliveira

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The environmental vulnerability studies are essential for priority actions to the reduction of disasters risk. The aim of this study is to analyze the socio-environmental vulnerability obtained through a Census survey, followed by both a statistical analysis (PCA/SPSS/IBM) and a spatial analysis by GIS (ArcGis/ESRI), taking as a case study the Municipality of Caraguatatuba-SP, Brazil. In the municipal development plan analysis the emphasis was given to the Special Zone of Social Interest (ZEIS), the Urban Expansion Zone (ZEU) and the Environmental Protection Zone (ZPA). For the mapping of the social and environmental vulnerabilities of the study area the exposure of people (criticality) and of the place (support capacity) facing disaster risk were obtained from the 2010 Census from the Brazilian Institute of Geography and Statistics (IBGE). Considering the criticality, the variables of greater influence were related to literate persons responsible for the household and literate persons with 5 or more years of age; persons with 60 years or more of age and income of the person responsible for the household. In the Support Capacity analysis, the predominant influence was on the good household infrastructure in districts with low population density and also the presence of neighborhoods with little urban infrastructure and inadequate housing. The results of the comparative analysis show that the areas with high and very high vulnerability classes cover the classes of the ZEIS and the ZPA, whose zoning includes: Areas occupied by low-income population, presence of children and young people, irregular occupations and land suitable to urbanization but underutilized. The presence of zones of urban sprawl (ZEU) in areas of high to very high socio-environmental vulnerability reflects the inadequate use of the urban land in relation to the spatial distribution of the population and the territorial infrastructure, which favors the increase of disaster risk. It can be concluded that the study allowed observing the convergence between the vulnerability analysis and the classified areas in urban zoning. The occupation of areas unsuitable for housing due to its characteristics of risk was confirmed, thus concluding that the methodologies applied are agile instruments to subsidize actions to the reduction disasters risk.

Keywords: socio-environmental vulnerability, urban zoning, reduction disasters risk, methodologies

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Authors: Mohammad Besharatloo

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Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these sub-populations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.

Keywords: intrusion detection system, differential evolution, firefly algorithm, support vector machine, decision tree

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Authors: Yavuz Unal, Kemal Polat, H. Erdinc Kocer

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In this study, a decision support system comprising three stages has been proposed to detect the disc abnormalities of the lumbar region. In the first stage named the feature extraction, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people and then 27 appearance and shape features were acquired from both sagittal and transverse images. In the second stage named the feature weighting process, k-means clustering based feature weighting (KMCBFW) proposed by Gunes et al. Finally, in the third stage named the classification process, the classifier algorithms including multi-layer perceptron (MLP- neural network), support vector machine (SVM), Naïve Bayes, and decision tree have been used to classify whether the subject has lumbar disc or not. In order to test the performance of the proposed method, the classification accuracy (%), sensitivity, specificity, precision, recall, f-measure, kappa value, and computation times have been used. The best hybrid model is the combination of k-means clustering based feature weighting and decision tree in the detecting of lumbar disc disease based on both sagittal and axial MR images.

Keywords: lumbar disc abnormality, lumbar MRI, lumbar spine, hybrid models, hybrid features, k-means clustering based feature weighting

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Authors: Temidayo James Aransiola, Vania Ceccato, Marcelo Justus

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This paper investigates the effect of absolute deprivation (proxy unemployment) and relative deprivation (proxy income inequality) on homicide levels in Brazil. A database from the Brazilian Information System about Mortality and Census of the year 2000 and 2010 was used to estimate negative binomial models of homicide levels controlling for socioeconomic, demographic and geographic factors. Findings show that unemployment and income inequality affect homicides levels and that the effect of the former is more pronounced compared to the latter. Moreover, the combination of income inequality and unemployment exacerbates the overall effect of deprivation on homicide levels.

Keywords: deprivation, inequality, interaction, unemployment, violence

Procedia PDF Downloads 139

Authors: Raymond Feng, Shadi Ghiasi

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An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy.

Keywords: EEG, electroencephalogram, machine learning, mood, music enjoyment, physiological signals

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Authors: Oustani Mabrouka, Halilat Med Tahar

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The physico-chemical properties of poultry droppings indicate that this waste can be an excellent way to enrich the soil with low fertility that is the case in arid soils (low organic matter content), but its concentrations in some microbial and chemical components make them potentially dangerous and toxic contaminants if they are used directly in fresh state. On other hand, the accumulation of plant residues in the crop areas can become a source of plant disease and affects the quality of the environment. The biotechnological processes that we have identified appear to alleviate these problems. It leads to the stabilization and processing of wastes into a product of good hygienic quality and high fertilizer value by the composting test. In this context, a trial was conducted in composting operations in the region of Ouargla located in southern Algeria. Composing test was conducted in a completely randomized design experiment. Three mixtures were prepared, in pits of 1 m3 volume for each mixture. Each pit is composed by mixture of poultry droppings and crushed plant residues in amount of 40 and 60% respectively: C1: Droppings + Straw (P.D +S) , C2: Poultry Droppings + Olive Wastes (P.D+O.W) , C3: Poultry Droppings + Date palm residues (P.D+D.P). Before and after the composting process, physico-chemical parameters (temperature, moisture, pH, electrical conductivity, total carbon and total nitrogen) were studied. The stability of the biological system was noticed after 90 days. The results of physico-chemical and microbiological compost obtained from three mixtures: C1: (P.D +S) , C2: (P.D+O.W) and C3: (P.D +D.P) shows at the end of composting process, three composts characterized by the final products were characterized by their high agronomic and environmental interest with a good physico chemical characteristics in particularly a low C/N ratio with 15.15, 10.01 and 15.36 % for (P.D + S), (P.D. + O.W) and (P.D. +D.P), respectively, reflecting a stabilization and maturity of the composts. On the other hand, a significant increase of temperature was recorded at the first days of composting for all treatments, which is correlated with a strong reduction of the pathogenic micro flora contained in poultry dropings.

Keywords: Arid environment, Composting, Date palm residues, Olive wastes, pH, Pathogenic microorganisms, Poultry Droppings, Straw

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Laila Shikaki

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‘Writings about Home’ discusses the poetry of Palestinian American female poets, especially ones who write about their homelands, living away from home, as well as their family ties to the land. This is a paper about poetry, but it is also about Palestinian American women who use English to convey issues pertaining to homesickness, family, and language. She study poems by Naomi Shihab Nye and Natalie Hanal. In ‘My Father and the Fig Tree,’ for example, Nye depicts her father’s life away from Palestine and his attachment to a tree that represents his homeland and nostalgia. Nye’s style is diverse and unified, and her attention is to details and images. While her words and imagery are usually simple, they are always rich in meaning. Nathalie Handal’s poetry, on the other hand, has a more complicated, multi-layered, and nuanced style as the poet herself lived in many areas and spoke multiple languages. ‘Bethlehem,’ for instance, depicts her city of origin, recalling her grandfather. Her poem ‘Blue Hours’ illustrates a persona’s difficulty in belonging, switching from one language to the next, and feeling a betrayal in both. This paper pays attention to language and how being bilingual adds another level of exile and pain to those who have fled or were forced to leave Palestine. This paper is very timely as the issue of Palestinian freedom and its right to autonomy and self-determination is the central stage for many Americans, seen in their protests, university encampments, and graduation ceremonies, not forgetting its effect on voters’ decisions for president and elected officials.

Keywords: Palestinian American, poetry, homeland, Nye, Handal

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Authors: Rikson Gultom

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Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

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Authors: Emiru Birhane, Tesfay Gidey, Haftu Abrha, Abrha Brhan, Amanuel Zenebe, Girmay Gebresamuel, Florent Noulèkoun

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Dracaena ombet and Dobera glabra are two of the most rare and endangered tree species in dryland areas. Unfortunately, their sustainability is being compromised by different anthropogenic and natural factors. However, the impacts of ongoing land use and climate change on the population structure and distribution of the species are less explored. This study was carried out in the grazing lands and hillside areas of the Desa'a dry Afromontane forest, northern Ethiopia, to characterize the population structure of the species and predict the impact of climate change on their potential distributions. In each land-use type, abundance, diameter at breast height, and height of the trees were collected using 70 sampling plots distributed over seven transects spaced one km apart. The geographic coordinates of each individual tree were also recorded. The results showed that the species populations were characterized by low abundance and unstable population structure. The latter was evinced by a lack of seedlings and mature trees. The study also revealed that the total abundance and dendrometric traits of the trees were significantly different between the two land uses. The hillside areas had a denser abundance of bigger and taller trees than the grazing lands. Climate change predictions using the MaxEnt model highlighted that future temperature increases coupled with reduced precipitation would lead to significant reductions in the suitable habitats of the species in northern Ethiopia. The species' suitable habitats were predicted to decline by 48–83% for D. ombet and 35–87% for D. glabra. Hence, to sustain the species populations, different strategies should be adopted, namely the introduction of alternative livelihoods (e.g., gathering NTFP) to reduce the overexploitation of the species for subsistence income and the protection of the current habitats that will remain suitable in the future using community-based exclosures. Additionally, the preservation of the species' seeds in gene banks is crucial to ensure their long-term conservation.

Keywords: grazing lands, hillside areas, land-use change, MaxEnt, range limitation, rare and endangered tree species

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Authors: Abduselam Faris, Rijalu Negash, Zera Kedir

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This study was initiated to identify constraints and opportunities of the wood production value chain in Southwest Ethiopia. About 385 wood trees growing farmers were randomly interviewed. Similarly, about 30 small-scale wood processors, 30 retailers, 15 local collectors and 5 wholesalers were purposively included in the study. The results of the study indicated that 98.96 % of the smallholder farmers that engaged in the production of wood trees which is used for wood were male-headed, with an average age of 46.88 years. The main activity that the household engaged was agriculture (crop and livestock) which accounts for about 61.56% of the sample respondents. Through value chain mapping of actors, the major value chain participant and supporting actors were identified. On average, the tree-growing farmers generated gross income of 9385.926 Ethiopian birr during the survey year. Among the critical constraints identified along the wood production value chain was limited supply of credit, poor market information dissemination, high interference of brokers, and shortage of machines, inadequate working area and electricity. The availability of forest resources is the leading opportunity in the wood production value chain. Reinforcing the linkage among wood production value chain actors, providing skill training for small-scale processors, and developing suitable policy for wood tree wise use is key recommendations forward.

Keywords: value chain analysis, wood production, southwest Ethiopia, constraints and opportunities

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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: Ghada Badr, Manar Hosny, Nuha Bintayyash, Eman Albilali, Souad Larabi Marie-Sainte

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The median problem is significantly applied to derive the most reasonable rearrangement phylogenetic tree for many species. More specifically, the problem is concerned with finding a permutation that minimizes the sum of distances between itself and a set of three signed permutations. Genomes with equal number of genes but different order can be represented as permutations. In this paper, an algorithm, namely BeamGA median, is proposed that combines a heuristic search approach (local beam) as an initialization step to generate a number of solutions, and then a Genetic Algorithm (GA) is applied in order to refine the solutions, aiming to achieve a better median with the smallest possible reversal distance from the three original permutations. In this approach, any genome rearrangement distance can be applied. In this paper, we use the reversal distance. To the best of our knowledge, the proposed approach was not applied before for solving the median problem. Our approach considers true biological evolution scenario by applying the concept of common intervals during the GA optimization process. This allows us to imitate a true biological behavior and enhance genetic approach time convergence. We were able to handle permutations with a large number of genes, within an acceptable time performance and with same or better accuracy as compared to existing algorithms.

Keywords: median problem, phylogenetic tree, permutation, genetic algorithm, beam search, genome rearrangement distance

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Authors: Nafisa Banu

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The present study is an attempt to analyze the regional disparities in the level of education in West Bengal. The data based on secondary sources obtained from a census of India. The study is divided into four sections. The first section presents introductions, objectives and brief descriptions of the study area, second part discuss the methodology and data base, while third and fourth comprise the empirical results, interpretation, and conclusion respectively. For showing the level of educational development, 8 indicators have been selected and Z- score and composite score techniques have been applied. The present study finds out there are large variations of educational level due to various historical, economical, socio-cultural factors of the study area.

Keywords: education, regional disparity, literacy rate, Z-score, composite score

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Authors: Bill Wason

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143 Crude Palm Oil Coop mills on Sumatra Island are participating in a program to transfer land from defaulted estates to small farmers while improving the sustainability of palm production to allow for biofuel & food production. GCarbon will be working with farmers to transfer technology, fertilizer, and trees to double the yield from the current baseline of 3.5 tons to at least 7 tons of oil per ha (25 tons of fruit bunches). This will be measured via evaluation of yield comparisons between participant and non-participant farms. We will also capture methane from Palm Oil Mill Effluent (POME)throughbelt press filtering. Residues will be weighed and a formula used to estimate methane emission reductions based on methodologies developed by other researchers. GCarbon will also cover mill ponds with a non-permeable membrane and collect methane for energy or steam production. A system for accelerating methane production involving ozone and electro-flocculation will be tested to intensifymethane generation and reduce the time for wastewater treatment. A meta-analysis of research on sweet potatoes and sorghum as rotation crops will look at work in the Rio Grande do Sul, Brazil where5 ha. oftest plots of industrial sweet potato have achieved yields of 60 tons and 40 tons per ha. from 2 harvests in one year (100 MT/ha./year). Field trials will be duplicated in Bom Jesus Das Selvas, Maranhaothat will test varieties of sweet potatoes to measure yields and evaluate disease risks in a very different soil and climate of NE Brazil. Hog methane will also be captured. GCarbon Brazil, Coop Sisal, and an Australian research partner will plant several varieties of agave and use agronomic procedures to get yields of 880 MT per ha. over 5 years. They will also plant new varieties expected to get 3500 MT of biomass after 5 years (176-700 MT per ha. per year). The goal is to show that the agave can adapt to Brazil’s climate without disease problems. The study will include a field visit to growing sites in Australia where agave is being grown commercially for biofuels production. Researchers will measure the biomass per hectare at various stages in the growing cycle, sugar content at harvest, and other metrics to confirm the yield of sugar per ha. is up to 10 times greater than sugar cane. The study will look at sequestration rates from measuring soil carbon and root accumulation in various plots in Australia to confirm carbon sequestered from 5 years of production. The agave developer estimates that 60-80 MT of sequestration per ha. per year occurs from agave. The three study efforts in 3 different countries will define a feedstock pathway for jet fuel that involves very high yield crops that can produce 2 to 10 times more biomass than current assumptions. This cost-effective and less land intensive strategy will meet global jet fuel demand and produce huge quantities of food for net zero aviation and feeding 9-10 billion people by 2050

Keywords: zero emission SAF, methane capture, food-fuel integrated refining, new crops for SAF

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Authors: Birendra Kumar Bhattachan

Abstract:

Organic soil media plays an important role for seed germination, growing, and producing organic jack fruits as the source of food such as vitamin A, C, and others for human health. An experiment was conducted to find out the appropriate organic soil medias to induce germination and seedling vigor of jack fruit seeds at the farm of Agriculture and Forestry University (AFU) Chitwan Nepal during June 2022 to October 2022. The organic soil medias used as treatments were as 1. soil collected under the Molingia tree; 2. soil, FYM and RH (2:1;1); 3. soil, FYM (1:1); 4. sand, FYM and RH (2:1:1), 5, sand, soil, FYM and RH (1:1:1:1) and 6. sand, soil and RH (1:2:1) under Completely Randomized Design (CRD) with four replications. Significantly highest germination of 88% was induced by soil media, followed by media of soil and FYM (!:1) i.e. 63% and the media of soil, FYM and RH (2:1;1) and the least media was sand, soil, FYM and RH (1:1:1:) to induce germination of 28%. Significantly highest seedling length of 73 cm was produced by soil media followed by the media soil, sand, and RH (1:2:1), i.e. 72 cm and the media soil, sand, FYM, and RH (1:1:1:1) and the least media was soil, FYM and RH (2:1:1) to produce 62 cm seedling length, Similarly, significantly highest seedling vigor of 6257 was produced by soil media followed by the media soil and FYM (1:1) i.e. 4253 and the least was the media sand, soil, FYM and RH (1:1:1:1) to produce seedling vigor of1916. Based on this experiment, it was concluded that soil media collected under the Moringia tree could induce the highest germinating capacity of jack fruit seeds and then seedling vigor.

Keywords: jack fruit seed, soil media, farm yard manure, sand media, rice husk

Procedia PDF Downloads 184

Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

Abstract:

Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

Procedia PDF Downloads 122

Authors: Walid Moudani

Abstract:

The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the coronary heart disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: multi-classifier decisions tree, features reduction, dynamic programming, rough sets

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Authors: José de Jesús Rivero Oliva, Jesús Salomón Llanes, Manuel Perdomo Ojeda, Antonio Torres Valle

Abstract:

Combinatorial explosion is a common problem to both predominant methods for solving fault trees: Minimal Cut Set (MCS) approach and Binary Decision Diagram (BDD). High memory consumption impedes the complete solution of very complex fault trees. Only approximated non-conservative solutions are possible in these cases using truncation or other simplification techniques. The paper proposes a method (CSolv+) for solving complex fault trees, without any possibility of combinatorial explosion. Each individual MCS is immediately discarded after its contribution to the basic events importance measures and the Top gate Upper Bound Probability (TUBP) has been accounted. An estimation of the Top gate Exact Probability (TEP) is also provided. Therefore, running in a computer cluster, CSolv+ will guarantee the complete solution of complex fault trees. It was successfully applied to 40 fault trees from the Aralia fault trees database, performing the evaluation of the top gate probability, the 1000 Significant MCSs (SMCS), and the Fussell-Vesely, RRW and RAW importance measures for all basic events. The high complexity fault tree nus9601 was solved with truncation probabilities from 10-²¹ to 10-²⁷ just to limit the execution time. The solution corresponding to 10-²⁷ evaluated 3.530.592.796 MCSs in 3 hours and 15 minutes.

Keywords: system reliability analysis, probabilistic risk assessment, fault tree analysis, basic events importance measures

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Authors: C. Manjula, Lilly Florence

Abstract:

Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: decision tree, genetic algorithm, machine learning, software defect prediction

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Authors: Hamdi Sana, Emna Bouazizi, Sami Faiz

Abstract:

In recent years, there is a huge increase in the use of spatio-temporal applications where data and queries are continuously moving. As a result, the need to process real-time spatio-temporal data seems clear and real-time stream data management becomes a hot topic. Sliding window model and frequent itemset mining over dynamic data are the most important problems in the context of data mining. Thus, sliding window model for frequent itemset mining is a widely used model for data stream mining due to its emphasis on recent data and its bounded memory requirement. These methods use the traditional transaction-based sliding window model where the window size is based on a fixed number of transactions. Actually, this model supposes that all transactions have a constant rate which is not suited for real-time applications. And the use of this model in such applications endangers their performance. Based on these observations, this paper relaxes the notion of window size and proposes the use of a timestamp-based sliding window model. In our proposed frequent itemset mining algorithm, support conditions are used to differentiate frequents and infrequent patterns. Thereafter, a tree is developed to incrementally maintain the essential information. We evaluate our contribution. The preliminary results are quite promising.

Keywords: real-time spatial big data, frequent itemset, transaction-based sliding window model, timestamp-based sliding window model, weighted frequent patterns, tree, stream query

Procedia PDF Downloads 149

Authors: Elmeliani M’Hammed

Abstract:

The work is carried out for the synthesis of antifungal effective against the fungus Fusarium oxysporum, Albedinis (Foa), the causative agent of bayoud, dates palm disease, through the use of raw clay as a green catalyst. The Aza-Michael reaction of amine addition to α, β-unsaturated alkene was carried out using the crude clay as a green catalyst to synthesize the antifungal agent bayoud. The reaction was carried out under favorable conditions, ambient temperature, without solvent, and a green catalyst "loves the environment" that the product that was synthesized gave us a high yield and excellent chemo selectivity.

Keywords: raw clay, amines, alkenes, environment, antifungal, bayoud, date palms

Procedia PDF Downloads 81

Authors: Serdar Yaman, Hanzade Haykiri-Acma

Abstract:

Biomass is a CO₂-neutral fuel that is renewable and sustainable along with having very huge global potential. Efficient use of biomass in power generation and production of biomass-based biofuels can mitigate the greenhouse gasses (GHG) and reduce dependency on fossil fuels. There are also other beneficial effects of biomass energy use such as employment creation and pollutant reduction. However, most of the biomass materials are not capable of competing with fossil fuels in terms of energy content. High moisture content and high volatile matter yields of biomass make it low calorific fuel, and it is very significant concern over fossil fuels. Besides, the density of biomass is generally low, and it brings difficulty in transportation and storage. These negative aspects of biomass can be overcome by thermal pretreatments that upgrade the fuel property of biomass. That is, torrefaction is such a thermal process in which biomass is heated up to 300ºC under non-oxidizing conditions to avoid burning of the material. The treated biomass is called as biochar that has considerably lower contents of moisture, volatile matter, and oxygen compared to the parent biomass. Accordingly, carbon content and the calorific value of biochar increase to the level which is comparable with that of coal. Moreover, hydrophilic nature of untreated biomass that leads decay in the structure is mostly eliminated, and the surface properties of biochar turn into hydrophobic character upon torrefaction. In order to investigate the effectiveness of torrefaction process on biomass properties, several biomass species such as olive milling residue (OMR), Rhododendron (small shrubby tree with bell-shaped flowers), and ash tree (timber tree) were chosen. The fuel properties of these biomasses were analyzed through proximate and ultimate analyses as well as higher heating value (HHV) determination. For this, samples were first chopped and ground to a particle size lower than 250 µm. Then, samples were subjected to torrefaction in a horizontal tube furnace by heating from ambient up to temperatures of 200, 250, and 300ºC at a heating rate of 10ºC/min. The biochars obtained from this process were also tested by the methods applied to the parent biomass species. Improvement in the fuel properties was interpreted. That is, increasing torrefaction temperature led to regular increases in the HHV in OMR, and the highest HHV (6065 kcal/kg) was gained at 300ºC. Whereas, torrefaction at 250ºC was seen optimum for Rhododendron and ash tree since torrefaction at 300ºC had a detrimental effect on HHV. On the other hand, the increase in carbon contents and reduction in oxygen contents were determined. Burning characteristics of the biochars were also studied using thermal analysis technique. For this purpose, TA Instruments SDT Q600 model thermal analyzer was used and the thermogravimetric analysis (TGA), derivative thermogravimetry (DTG), differential scanning calorimetry (DSC), and differential thermal analysis (DTA) curves were compared and interpreted. It was concluded that torrefaction is an efficient method to upgrade the fuel properties of biomass and the biochars from which have superior characteristics compared to the parent biomasses.

Keywords: biochar, biomass, fuel upgrade, torrefaction

Procedia PDF Downloads 365