Search results for: decision tree classifier

Authors: Marielcio Lacerda, Angelo Paulino, Elcio Shiguemori, Alvaro Damiao, Lamartine Guimaraes, Camila Anjos

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Accurate information about the terrain is extremely important in disaster management activities or conflict. This paper proposes the use of the Unmanned Aircraft Systems (UAS) at the identification of Airdrop Zones (AZs) and Helicopter Landing Zones (HLZs). In this paper we consider the AZs the zones where troops or supplies are dropped by parachute, and HLZs areas where victims can be rescued. The use of digital image processing enables the automatic generation of an orthorectified mosaic and an actual Digital Surface Model (DSM). This methodology allows obtaining this fundamental information to the terrain’s comprehension post-disaster in a short amount of time and with good accuracy. In order to get the identification and classification of AZs and HLZs images from DJI drone, model Phantom 4 have been used. The images were obtained with the knowledge and authorization of the responsible sectors and were duly registered in the control agencies. The flight was performed on May 24, 2017, and approximately 1,300 images were obtained during approximately 1 hour of flight. Afterward, new attributes were generated by Feature Extraction (FE) from the original images. The use of multispectral images and complementary attributes generated independently from them increases the accuracy of classification. The attributes of this work include the Declivity Map and Principal Component Analysis (PCA). For the classification four distinct classes were considered: HLZ 1 – small size (18m x 18m); HLZ 2 – medium size (23m x 23m); HLZ 3 – large size (28m x 28m); AZ (100m x 100m). The Decision Tree method Random Forest (RF) was used in this work. RF is a classification method that uses a large collection of de-correlated decision trees. Different random sets of samples are used as sampled objects. The results of classification from each tree and for each object is called a class vote. The resulting classification is decided by a majority of class votes. In this case, we used 200 trees for the execution of RF in the software WEKA 3.8. The classification result was visualized on QGIS Desktop 2.12.3. Through the methodology used, it was possible to classify in the study area: 6 areas as HLZ 1, 6 areas as HLZ 2, 4 areas as HLZ 3; and 2 areas as AZ. It should be noted that an area classified as AZ covers the classifications of the other classes, and may be used as AZ, HLZ of large size (HLZ3), medium size (HLZ2) and small size helicopters (HLZ1). Likewise, an area classified as HLZ for large rotary wing aircraft (HLZ3) covers the smaller area classifications, and so on. It was concluded that images obtained through small UAV are of great use in calamity situations since they can provide data with high accuracy, with low cost, low risk and ease and agility in obtaining aerial photographs. This allows the generation, in a short time, of information about the features of the terrain in order to serve as an important decision support tool.

Keywords: disaster management, unmanned aircraft systems, helicopter landing zones, airdrop zones, random forest

Procedia PDF Downloads 177

Authors: Youhang Zhou, Weimin Zeng, Qi Xie

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Abstract: In order to solve the wear recognition problem of the machine tool guide surface, a new machine tool guide surface recognition method based on the radar-graph barycentre feature is presented in this paper. Firstly, the gray mean value, skewness, projection variance, flat degrees and kurtosis features of the guide surface image data are defined as primary characteristics. Secondly, data Visualization technology based on radar graph is used. The visual barycentre graphical feature is demonstrated based on the radar plot of multi-dimensional data. Thirdly, a classifier based on the support vector machine technology is used, the radar-graph barycentre feature and wear original feature are put into the classifier separately for classification and comparative analysis of classification and experiment results. The calculation and experimental results show that the method based on the radar-graph barycentre feature can detect the guide surface effectively.

Keywords: guide surface, wear defects, feature extraction, data visualization

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Authors: Afshin Veisi

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In this study, the physical properties of wood in poplar wood (Populous sp.) were analyzed in longitudinal and radial directions of the stem. Three Populous Alba tree were cut in chaloos zone and from each tree, 3 discs were selected at 130cm, half of tree and under of crown. The test samples from pith to bark (heartwood to sapwood) were prepared from these discs for measuring the involved properties such as, wet, dry and critical specific gravity, porosity, volume shrinkage and swelling based on the ASTM standard, and data in two radial and longitudinal directions in the trank were statistically analyzed. Such as, variations of wet, dry and critical specific gravity had in radial direction respectively: irregular increase, increase and increase, and in longitudinal direction respectively: irregular decrease, irregular increase and increase. Results of variations to moisture content and porosity show that in radial direction respectively: irregular increasing and decreasing, and in longitudinal direction from down to up respectively: irregular decreasing and stability. Volume shrinkage and swelling variations show in radial direction irregular and in longitudinal axial regular decreasing.

Keywords: poplar wood, physical properties, shrinkage, swelling, critical specific gravity, wet specific gravity, dry specific gravity

Procedia PDF Downloads 278

Authors: Navdeep Kaur

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The primary focus of this study is to answer “What is the role of corporate governance in corporate failures? Does poor corporate governance lead to corporate failures? If so, how?”. In doing so, the study examines the literature from multiple fields, including corporate governance, corporate failures and organizational decision making, and presents a research gap to analyze and explore the relationship between corporate governance practices and corporate failures through a behavioral lens. In approaching this, a qualitative research methodology is adopted to analyze the failure of Enron Corporation (United States). The research considered the case study organizations as the primary unit of analysis and the decision-makers as the secondary unit of analysis. Based on this research approach, the study reports the analytical results drawn from extensive and triangulated secondary data. The study then interprets the results in the context of the theoretical synthesis. The study contributes towards filling a gap in the research and presents a behaviourally plausible decision centered model of the role of corporate governance in corporate failures. The model highlights the critical role of the behavioral aspects of corporate governance decision making in corporate failures and focuses attention on the under-explored aspects of corporate governance decision making. The study also suggests a further understanding of ‘A Behavioral Theory of the Firm’ in relation to corporate failures.

Keywords: behavior, corporate failure, corporate governance, decision making, values

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Authors: Babak Daneshvar Rouyendegh

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The aim of this study is to develop and describe a new methodology for the Multi-Criteria Decision-Making (MCDM) problem using Intuitionistic Fuzzy Elimination Et Choix Traduisant La REalite (IFELECTRE) model. The proposed models enable Decision-Makers (DMs) on the assessment and use Intuitionistic Fuzzy numbers (IFN). A numerical example is provided to demonstrate and clarify the proposed analysis procedure. Also, an empirical experiment is conducted to validation the effectiveness.

Keywords: Decision-Makers (DMs), Multi-Criteria Decision-Making (MCDM), Intuitionistic Fuzzy Elimination Et Choix Traduisant La REalite (IFELECTRE), Intuitionistic Fuzzy Numbers (IFN)

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Authors: Mikel Alonso López

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In order to begin the process of studying the task of making consumer decisions, the main decision models must be analyzed. The objective of this task is to see if there is a presence of emotions in those models, and analyze how authors that have created them consider their impact in consumer choices. In this paper, the most important models of consumer behavior are analysed. This review is useful to consider an unproblematic background knowledge in the literature. The order that has been established for this study is chronological.

Keywords: consumer behaviour, emotions, decision making, consumer psychology

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Authors: Jessica M. Black

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Although much of natural and social science research aims to enhance human flourishing and address social problems, the training within the two fields is significantly different across theory, methodology, and implementation of results. Social scientists are trained in social, psychological, and to the extent that it is relevant to their discipline, spiritual development, theory, and accompanying methodologies. They tend not to receive training or learn about accompanying methodology related to interrogating human development and social problems from a biological perspective. On the other hand, those in the natural sciences, and for the purpose of this work, human biological sciences specifically – biology, neuroscience, genetics, epigenetics, and physiology – are often trained first to consider cellular development and related methodologies, and may not have opportunity to receive formal training in many of the foundational principles that guide human development, such as systems theory or person-in-environment framework, methodology related to tapping both proximal and distal psycho-social-spiritual influences on human development, and foundational principles of equity, justice and inclusion in research design. There is a need for disciplines heretofore siloed to know one another, to receive streamlined, easy to access training in theory and methods from one another and to learn how to build interdisciplinary teams that can speak and act upon a shared research language. Team science is more essential than ever, as are transdisciplinary approaches to training and research design. This study explores the use of a methodological toolbox that natural and social scientists can use by employing a decision-making tree regarding project aims, costs, and participants, among other important study variables. The decision tree begins with a decision about whether the researcher wants to learn more about social sciences approaches or biological approaches to study design. The toolbox and platform are flexible, such that users could also choose among modules, for instance, reviewing epigenetics or community-based participatory research even if those are aspects already a part of their home field. To start, both natural and social scientists would receive training on systems science, team science, transdisciplinary approaches, and translational science. Next, social scientists would receive training on grounding biological theory and the following methodological approaches and tools: physiology, (epi)genetics, non-invasive neuroimaging, invasive neuroimaging, endocrinology, and the gut-brain connection. Natural scientists would receive training on grounding social science theory, and measurement including variables, assessment and surveys on human development as related to the developing person (e.g., temperament and identity), microsystems (e.g., systems that directly interact with the person such as family and peers), mesosystems (e.g., systems that interact with one another but do not directly interact with the individual person, such as parent and teacher relationships with one another), exosystems (e.g., spaces and settings that may come back to affect the individual person, such as a parent’s work environment, but within which the individual does not directly interact, macrosystems (e.g., wider culture and policy), and the chronosystem (e.g., historical time, such as the generational impact of trauma). Participants will be able to engage with the toolbox and one another to foster increased transdisciplinary work

Keywords: methodology, natural science, social science, transdisciplinary

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Authors: Tanmay Bisen, Aastha Shayla

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This study introduces an advanced approach to tree canopy detection in urban environments and a regional analysis of Northern and Central Kolkata that delves into the intricate relationship between urban development and environmental conservation. Leveraging high-resolution drone imagery from diverse urban green spaces in Kolkata, we fine-tuned the deep forest model to enhance its precision and accuracy. Our results, characterized by an impressive Intersection over Union (IoU) score of 0.90 and a mean average precision (mAP) of 0.87, underscore the model's robustness in detecting and classifying tree crowns amidst the complexities of aerial imagery. This research not only emphasizes the importance of model customization for specific datasets but also highlights the potential of drone-based remote sensing in urban forestry studies. The study investigates the spatial distribution, density, and environmental impact of trees in Northern and Central Kolkata. The findings underscore the significance of urban green spaces in met-ropolitan cities, emphasizing the need for sustainable urban planning that integrates green infrastructure for ecological balance and human well-being.

Keywords: urban greenery, advanced spatial distribution analysis, drone imagery, deep learning, tree detection

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Authors: Gizelle K. Vianna, Gustavo S. Oliveira, Gabriel V. Cunha

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The use of decision support systems in agriculture may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. In our work, we designed and implemented a decision support system for small tomatoes producers. This work investigates ways to recognize the late blight disease from the analysis of digital images of tomatoes, using a pair of multilayer perceptron neural networks. The networks outputs are used to generate repainted tomato images in which the injuries on the plant are highlighted, and to calculate the damage level of each plant. Those levels are then used to construct a situation map of a farm where a cellular automata simulates the outbreak evolution over the fields. The simulator can test different pesticides actions, helping in the decision on when to start the spraying and in the analysis of losses and gains of each choice of action.

Keywords: artificial neural networks, cellular automata, decision support system, pattern recognition

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Authors: Muhammad Shoaib, Hassan M Al-Swaidan

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Saudi Arabia is the major date producer in the world. In order to maximize the production from date tree, pruning of the date trees is required annually. Large amount of this agriculture waste material (palm tree fronds) is available in Saudi Arabia and considered as an ideal source as a precursor for production of activated carbon (AC). The single step procedure for the preparation of micro porous activated carbon (AC) from Saudi date tree fronds using mixture of gases (N2 and CO2) is carried out at carbonization/activation temperature at 850°C and at different ramp rates of 10, 20 and 30 degree per minute. Alloy 330 horizontal reactor is used for tube furnace. Flow rate of nitrogen and carbon dioxide gases are kept at 150 ml/min and 50 ml/min respectively during the preparation. Characterization results reveal that the BET surface area, pore volume, and average pore diameter of the resulting activated carbon generally decreases with the increase in ramp rate. The activated carbon prepared at a ramp rate of 10 degrees/minute attains larger surface area and can offer higher potential to produce activated carbon of greater adsorption capacity from agriculture wastes such as date fronds. The BET surface areas of the activated carbons prepared at a ramp rate of 10, 20 and 30 degree/minute after 30 minutes activation time are 1094, 1020 and 515 m2/g, respectively. Scanning electron microscopy (SEM) for surface morphology, and FTIR for functional groups was carried out that also verified the same trend. Moreover, by increasing the ramp rate from 10 and 20 degrees/min the yield remains same, i.e. 18%, whereas at a ramp rate of 30 degrees/min the yield increases from 18 to 20%. Thus, it is feasible to produce high-quality micro porous activated carbon from date frond agro waste using N2 carbonization followed by physical activation with CO2 and N2 mixture. This micro porous activated carbon can be used as adsorbent of heavy metals from wastewater, NOx SOx emission adsorption from ambient air and electricity generation plants, purification of gases, sewage treatment and many other applications.

Keywords: activated carbon, date tree fronds, agricultural waste, applied chemistry

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Authors: Onur Özdemir, M.Erhan ORHAN

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Knowledge Development (KD) is just one of the important phases of Knowledge Management (KM). KD is the phase in which intelligence is used to see the big picture. In order to understand whether information is important or not, we have to use the intelligence cycle that includes four main steps: aiming, collecting data, processing and utilizing. KD also needs these steps. To make a precise decision, the decision maker has to be aware of his subordinates’ ideas. If the decision maker ignores the ideas of his subordinates or participants of the organization, it is not possible for him to get the target. KD is a way of using wisdom to accumulate the puzzle. If the decision maker does not bring together the puzzle pieces, he cannot get the big picture, and this shows its effects on the battlefield. In order to understand the battlefield, the decision maker has to use the intelligence cycle. To convert information to knowledge, KD is the main means for the intelligence cycle. On the other hand, the “Z Generation” born after the millennium are really the game changers. They have different attitudes from their elders. Their understanding of life is different - the definition of freedom and independence have different meanings to them than others. Decision makers have to consider these factors and rethink their decisions accordingly. This article tries to explain the relation between KD and Generation Z. KD is the main method of target managing. But if leaders neglect their people, the world will be seeing much more movements like the Arab Spring and other insurgencies.

Keywords: knowledge development, knowledge management, generation Z, intelligence cycle

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Authors: Sally Heyeon Hwang

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Within decision theory, there are normative principles that dictate how one should act in addition to empirical theories of actual behavior. As a normative guide to one’s actual behavior, evidential or causal decision-theoretic equations allow one to identify outcomes with maximal utility values. The choice that each person makes, however, will, of course, differ according to varying assignments of weight and probability values. Regarding these different choices, it remains a subject of considerable philosophical controversy whether individual subjects have the capacity to exercise free will with respect to the assignment of probabilities, or whether instead the assignment is in some way constrained. A version of this question is given a precise form in Richard Jeffrey’s assumption that free will is necessary for Newcomb’s paradox to count as a decision problem. This paper will argue, against Jeffrey, that decision theory does not require the assumption of libertarian freedom. One of the hallmarks of decision-making is its application across a wide variety of contexts; the implications of a background assumption of free will is similarly varied. One constant across the contexts of decision is that there are always at least two levels of choice for a given agent, depending on the degree of prior constraint. Within the context of Newcomb’s problem, when the predictor is attempting to guess the choice the agent will make, he or she is analyzing the determined aspects of the agent such as past characteristics, experiences, and knowledge. On the other hand, as David Lewis’ backtracking argument concerning the relationship between past and present events brings to light, there are similarly varied ways in which the past can actually be dependent on the present. One implication of this argument is that even in deterministic settings, an agent can have more free will than it may seem. This paper will thus argue against the view that a stable background assumption of free will or determinism in decision theory is necessary, arguing instead for a compatibilist decision theory yielding a novel treatment of Newcomb’s problem.

Keywords: decision theory, compatibilism, free will, Newcomb’s problem

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Authors: Mohamed El-Shikh

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Phaseolus vulgaris L. is the world’s second most important bean after soybeans; used for human food and animal feed. It has generally been linked to reduced risk of cardiovascular disease, diabetes mellitus, obesity, cancer and diseases of digestive tract. The effectiveness of ISSR in achievement of the genetic diversity among 60 common bean accessions; represent various germplasms around the world was investigated. In general, the studied Phaseolus vulgaris accessions were divided into 2 major groups. All of the South-American accessions were separated into the second major group. These accessions may have different genetic features that are distinct from the rest of the accessions clustered in the major group. Asia and Europe accessions (1-20) seem to be more genetically similar (99%) to each other as they clustered in the same sub-group. The American and African varieties showed similarities as well and clustered in the same sub-tree group. In contrast, Asian and American accessions No. 22 and 23 showed a high level of genetic similarities, although these were isolated from different regions. The phylogenetic tree showed that all the Asian accessions (along with Australian No. 59 and 60) were similar except Indian and Yemen accessions No. 9 and 20. Only Netherlands accession No. 3 was different from the rest of European accessions. Morocco accession No. 52 was genetically different from the rest of the African accessions. Canadian accession No. 44 seems to be different from the other North American accessions including Guatemala, Mexico and USA.

Keywords: phylogenetic tree, Phaseolus vulgaris, ISSR technique, genetics

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Authors: Xudong Guan, Ainong Li, Gaohuan Liu, Chong Huang, Wei Zhao

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Due to the low spatial resolution of MODIS data, the accuracy of small-area plaque extraction with a high degree of landscape fragmentation is greatly limited. To this end, the study combines Landsat data with higher spatial resolution and MODIS data with higher temporal resolution for decision-level fusion. Considering the importance of the land heterogeneity factor in the fusion process, it is superimposed with the weighting factor, which is to linearly weight the Landsat classification result and the MOIDS classification result. Three levels were used to complete the process of data fusion, that is the pixel of MODIS data, the pixel of Landsat data, and objects level that connect between these two levels. The multilevel decision fusion scheme was tested in two sites of the lower Mekong basin. We put forth a comparison test, and it was proved that the classification accuracy was improved compared with the single data source classification results in terms of the overall accuracy. The method was also compared with the two-level combination results and a weighted sum decision rule-based approach. The decision fusion scheme is extensible to other multi-resolution data decision fusion applications.

Keywords: image classification, decision fusion, multi-temporal, remote sensing

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Authors: Elnaz Lashgari, Emel Demircan

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Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: electromyography, manifold learning, ISOMAP, Laplacian Eigenmaps, locally linear embedding

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Authors: Shaowei Sun

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Let G be a graph with vertex set V(G)={v_1,v_2,...,v_n} and edge set E(G). For any vertex v belong to V(G), let d_v denote the degree of v. The normalized Laplacian matrix of the graph G is the matrix where the non-diagonal (i,j)-th entry is -1/(d_id_j) when vertex i is adjacent to vertex j and 0 when they are not adjacent, and the diagonal (i,i)-th entry is the di. In this paper, we discuss some bounds on the largest and the second smallest normalized Laplacian eigenvalue of trees and graphs. As following, we found some new bounds on the second smallest normalized Laplacian eigenvalue of tree T in terms of graph parameters. Moreover, we use Sage to give some conjectures on the second largest and the third smallest normalized eigenvalues of graph.

Keywords: graph, normalized Laplacian eigenvalues, normalized Laplacian matrix, tree

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Authors: Yuliya Lipshits-Braziler

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For many young adults, career decision making is a difficult and complex process that may lead to indecision. Indecision is frequently associated with great psychological distress and low levels of well-being. One important resource for dealing with indecision is career decision self-efficacy (CDSE), which refers to people’s beliefs about their ability to successfully accomplish certain tasks involved in career choice. Drawing from Social Cognitive Theory, it has been hypothesized that CDSE correlates with (a) people’s likelihood to engage in or avoid career decision making tasks, (b) the amount of effort put into the decision making process, (c) the people’s persistence in decision making efforts when faced with difficulties, and (d) the eventual success in arriving at career decisions. Based on these assumptions, the present study examines the associations between the CDSE and 14 strategies for coping with career indecision among young adults. Using the structural equation modeling (SEM), the results showed that CDSE is positively associated with the use of productive coping strategies, such as information-seeking, problem-solving, positive thinking, and self-regulation. In addition, CDSE was negatively associated with nonproductive coping strategies, such as avoidance, isolation, ruminative thinking, and blaming others. Contrary to our expectations, CDSE was not significantly correlated with instrumental help-seeking, while it was negatively correlated with emotional help-seeking. The results of this study can be used to facilitate the development of interventions aiming to reinforce young adults’ career decision making self-efficacy, which may provide them with a basis for overcoming career indecision more effectively.

Keywords: career decision self-efficacy, career indecision, coping strategies, career counseling

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Authors: Dhanuj M. Gandikota

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Sensor-derived three-dimensional (3D) point clouds of trees are invaluable in remote sensing analysis for the accurate measurement of key structural metrics, bio-inventory values, spatial planning/visualization, and ecological modeling. Machine learning (ML) holds the potential in addressing the restrictive tradeoffs in cost, spatial coverage, resolution, and information gain that exist in current point cloud sensing methods. Terrestrial laser scanning (TLS) remains the highest fidelity source of both canopy and below-canopy structural features, but usage is limited in both coverage and cost, requiring manual deployment to map out large, forested areas. While aerial laser scanning (ALS) remains a reliable avenue of LIDAR active remote sensing, ALS is also cost-restrictive in deployment methods. Space-borne photogrammetry from high-resolution satellite constellations is an avenue of passive remote sensing with promising viability in research for the accurate construction of vegetation 3-D point clouds. It provides both the lowest comparative cost and the largest spatial coverage across remote sensing methods. However, both space-borne photogrammetry and ALS demonstrate technical limitations in the capture of valuable below-canopy point cloud data. Looking to minimize these tradeoffs, we explored a class of powerful ML algorithms called Deep Learning (DL) that show promise in recent research on 3-D point cloud reconstruction and interpolation. Our research details the efficacy of applying these DL techniques to reconstruct accurate below-canopy point clouds from space-borne and aerial remote sensing through learned patterns of tree species fractal symmetry properties and the supplementation of locally sourced bio-inventory metrics. From our dataset, consisting of tree point clouds obtained from TLS, we deconstructed the point clouds of each tree into those that would be obtained through ALS and satellite photogrammetry of varying resolutions. We fed this ALS/satellite point cloud dataset, along with the simulated local bio-inventory metrics, into the DL point cloud reconstruction architectures to generate the full 3-D tree point clouds (the truth values are denoted by the full TLS tree point clouds containing the below-canopy information). Point cloud reconstruction accuracy was validated both through the measurement of error from the original TLS point clouds as well as the error of extraction of key structural metrics, such as crown base height, diameter above root crown, and leaf/wood volume. The results of this research additionally demonstrate the supplemental performance gain of using minimum locally sourced bio-inventory metric information as an input in ML systems to reach specified accuracy thresholds of tree point cloud reconstruction. This research provides insight into methods for the rapid, cost-effective, and accurate construction of below-canopy tree 3-D point clouds, as well as the supported potential of ML and DL to learn complex, unmodeled patterns of fractal tree growth symmetry.

Keywords: deep learning, machine learning, satellite, photogrammetry, aerial laser scanning, terrestrial laser scanning, point cloud, fractal symmetry

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Authors: Muhammad Zaheer Awan, Adnan Qadir, Zeeshan Anjum

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Housefly, Musca domestica Linnaeus is ubiquitous and hazardous for Homo sapiens and livestock in sundry venerations. Musca domestica cart 100 different pathogens, such as typhoid, salmonella, bacillary dysentery, tuberculosis, anthrax and parasitic worms. The flies in rural areas usually carry more pathogens. Houseflies feed on liquid or semi-liquid substances besides solid materials which are softened by saliva. Neem botanically known as Azadirachta indica belongs to the family Meliaceae and is an indigenous tree to Pakistan. The neem tree is also one such tree which has been revered by the Pakistanis and Kashmiris for its medicinal properties. Present study showed neem seed extract has potentially toxic ability that affect Total Haemocyte Count (THC) and Differential Haemocytes Count (DHC) in insect’s blood cells, of the housefly. A significant variation in haemolymph density was observed just after application, 30 minutes and 60 minutes post treatment in term of THC and DHC in comparison with novastar. The study strappingly acclaim use of neem seed extract as insecticide as compare to artificial insecticides.

Keywords: neem, Azadirachta indica, Musca domestica, differential haemocyte count (DHC), total haemocytes count (DHC), novastar

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Authors: Syed Talib Abbas Raza, Tahseen Ahmed Jilani, Saleem Abdullah

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This paper presents interval-valued intuitionistic fuzzy soft sets based TOPSIS method for group decision making. The interval-valued intuitionistic fuzzy soft set is a mutation of an interval-valued intuitionistic fuzzy set and soft set. In group decision making problems IVIFSS makes the process much more algebraically elegant. We have used weighted arithmetic averaging operator for aggregating the information and define a new Hybrid Score Function as metric tool for comparison between interval-valued intuitionistic fuzzy values. In an illustrative example we have applied the developed method to a criminological problem. We have developed a group decision making model for integrating the imprecise and hesitant evaluations of multiple law enforcement agencies working on target killing cases in the country.

Keywords: group decision making, interval-valued intuitionistic fuzzy soft set, TOPSIS, score function, criminology

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Authors: Gladys Maame Akua Setordzie

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Decision on one’s future career is said to have both beneficial and detrimental effects on one’s mental health, social and economic standing later in life, making it an important developmental problem for young people. In this light, the study’s overarching goal was to assess how different personality traits serve as a determinant of career decision-making difficulties experienced by university students in Ghana. Specifically, for the purpose of shaping the future of individualized career counselling support, the study investigated whether the “Big Five” personality traits influenced the difficulties students at the University of Ghana encounter while making career decisions. Cross-sectional survey design using a stratified random sampling technique, sampled 494 undergraduate students from the University of Ghana, who completed the Big Five Questionnaire and the Career Decision-making Difficulties Questionnaire. Hierarchical multiple regression analyses indicated that neuroticism, consciousness, and openness, accounted for a significant proportion of the variance in career decision-making difficulties. This study provides empirical evidence to support the idea that neuroticism is not necessarily a negative emotion when it comes to career decisionmaking, as has been suggested in previous studies, but rather it allows students to perform better in career decision-making. These results suggests that personality traits play a significant role in the career decision-making process of students of the University of Ghana. Therefore, a better understanding of how different personal and interpersonal factors impact career indecision in students could help career counsellors develop more focused vocational and career guidance interventions.

Keywords: career decision-making difficulties, dysfunctional career beliefs, personality traits, young people

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Authors: Oluwatobi Emmanuel Olaniyi, Babafemi George Ogunjemite

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The high rate of infrastructural development in Ikogosi warm spring towards harnessing her great ecotourism potentials calls for a serious concern, as more forest areas are been opened up for public access and the landscape is modified. On this note, we investigated the implication of ecotourism development on the floristic composition and forest structure in Ikogosi. The study aimed at identifying the past and present status of infrastructural development, assessing and comparing the floristic composition and structure of the built- up/ recreational areas and undisturbed forested areas, to infer on the impact of ecotourism development on the study site. We conducted stakeholder interview and field observation to identify the past and present status of infrastructural development respectively. A total of ten quadrants were employed in the vegetation assessment to characterize the woody tree species composition, diameter at breast height and height, to obtain mean indices characterizing each part of the site. These indices were compared using T – test analysis. A total of 49 different woody tree species distributed in 21 families were identified in the built-in/ recreational areas while 67 different woody tree species belonging to 25 families were recorded in the undeveloped forested areas. Although, the latter has a higher mean diameter at breast height of woody trees, it was not significantly different from the former (T-test = -0.74, p = 0.46). On the contrary, the built-up area had a higher mean trees height than the undeveloped areas, but the difference was not statistically significant (T-test= 1.04, p = 0.30). Despite these, the slight reduction in richness and diversity of the woody tree species in the built- up/ recreational areas implies mitigating the negative effects of infrastructural development on the warm spring's vegetation.

Keywords: ecosystem services, forest structure, vegetation assessment, warm-spring

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Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

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Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

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Authors: ZhenlinJu, Christopher P. Vellano, RehanAkbani, Yiling Lu, Gordon B. Mills

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The epithelial–mesenchymal transition (EMT) is a process by which epithelial cells acquire mesenchymal characteristics, such as profound disruption of cell-cell junctions, loss of apical-basolateral polarity, and extensive reorganization of the actin cytoskeleton to induce cell motility and invasion. A hallmark of EMT is its capacity to promote metastasis, which is due in part to activation of several transcription factors and subsequent downregulation of E-cadherin. Unfortunately, current approaches have yet to uncover robust protein marker sets that can classify tumors as possessing strong EMT signatures. In this study, we utilize reverse phase protein array (RPPA) data and consensus clustering methods to successfully classify a subset of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumors into an EMT protein signaling group (EMT group). The overall survival (OS) of patients in the EMT group is significantly worse than those in the other Hormone and PI3K/AKT signaling groups. In addition to a shrinkage and selection method for linear regression (LASSO), we applied training/test set and Monte Carlo resampling approaches to identify a set of protein markers that predicts the EMT status of CESC tumors. We fit a logistic model to these protein markers and developed a classifier, which was fixed in the training set and validated in the testing set. The classifier robustly predicted the EMT status of the testing set with an area under the curve (AUC) of 0.975 by Receiver Operating Characteristic (ROC) analysis. This method not only identifies a core set of proteins underlying an EMT signature in cervical cancer patients, but also provides a tool to examine protein predictors that drive molecular subtypes in other diseases.

Keywords: consensus clustering, TCGA CESC, Silhouette, Monte Carlo LASSO

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Authors: Farzane Sadatnia, Bahram Fathi

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Today, all public and private organizations have found that the management of the world for key information related to the activities of a staff and its main essence and philosophy, though they constitute the management information systems are very helpful in this respect the right to apply systems can save a lot in terms of economic organizations including reducing the time decision - making, improve the quality of decision making, and cost savings to bring information systems is a backup system that can never be instead of logic and human reasoning, which can be used in the series is spreading, providing resources, and provide the necessary facilities, provide better services for users, balanced budget allocation, determine strengths and weaknesses and previous plans to review the current decisions and especially the decision . Hence; in this study attempts to the effect of an information system on a review of the organization.

Keywords: information system, planning, organization, coordination, control

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Authors: Khater Nadia, Garah Kenza

Abstract:

Moringa stenopetala is a rapidly growing, unappreciated tree regarded as the "miracle tree" for its food, feed, and medicinal benefits. It appears to be a versatile and promising species for use under changing conditions. To evaluate the effect of water stress on germination seeds of M. stenopetala, three different concentrations PEG- 6000 (4, 8, and 12 per cent) along with a control in a factorial experiment based on a completely randomized design with five replications. The results revealed that water potential significantly reduced germination rate (82.5%) and average germination time. Germination speed in T3 by 93%, kinetics germination in T2 (39), germination index in T2 (102) and germination vigor index in T2 (91.25) were increased in the osmotic potential of PEG solution. By following these steps, we can improve the chances of successful germination of M. stenopetala seeds under water stress conditions

Keywords: moringa stenopetala, PEG, water stress, rate

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Authors: Tomáš Vyčítal

Abstract:

In a simulation model of a railway system it is important, besides other crucial algorithms, to have correct behaviour of train overtaking in stochastic conditions. This problem is being addressed in many simulation tools focused on railway traffic, however these are not very human-realistic. The goal of this paper is to create a more human-realistic overtaking decision support system for the use in railway traffic simulations. A fuzzy system has been chosen for this task as fuzzy systems are well-suited for human-like decision making. The fuzzy system designed takes into account timetables, train positions, delays and buffer times as inputs and provides an instruction to overtake or not overtake.

Keywords: decision-making support, fuzzy systems, simulation, railway, transport

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Authors: Neelu Raina, Parvez Singh Slathia, Deepali Bhagat, Preeti Sharma

Abstract:

Pretreatment of lignocellulosic biomass for generating suitable substrates (starch/ sugars) for conversion to bioethanol is the most crucial step. In present study waste from furniture industry i.e sawdust from softwood Cedrus deodara (deodar) and hardwood Shorea robusta (sal) was used as lignocellulosic biomass. Thermochemical pretreatment was given by autoclaving at 121°C temperature and 15 psi pressure. Acids (H2SO4,HCl,HNO3,H3PO4), alkali (NaOH,NH4OH,KOH,Ca(OH)2) and organic acids (C6H8O7,C2H2O4,C4H4O4) were used at 0.1%, 0.5% and 1% concentration without giving any residence time. 1% HCl gave maximum sugar yield of 3.6587g/L in deodar and 6.1539 g/L in sal. For biological pretreatment a fungi isolated from decaying wood was used , sawdust from deodar tree species was used as a lignocellulosic substrate and before thermochemical pretreatment sawdust was treated with fungal culture at 37°C under submerged conditions with a residence time of one week followed by a thermochemical pretreatment methodology. Higher sugar yields were obtained with sal tree species followed by deodar tree species, i.e., 6.0334g/L in deodar and 8.3605g/L in sal was obtained by a combined biological and thermochemical pretreatment. Use of acids along with biological pretreatment is a favourable factor for breaking the lignin seal and thus increasing the sugar yield. Sugar estimation was done using Dinitrosalicyclic assay method. Result validation is being done by statistical analysis.

Keywords: lignocellulosic biomass, bioethanol, pretreatment, sawdust

Procedia PDF Downloads 414

Authors: Xinghang Lv, Tao Peng, Jia Chen, Junping Liu, Xinrong Hu, Ruhan He, Minghua Jiang, Wenli Cao

Abstract:

As one of the most dangerous vulnerabilities, effective detection of buffer overflow vulnerabilities is extremely necessary. Traditional detection methods are not accurate enough and consume more resources to meet complex and enormous code environment at present. In order to resolve the above problems, we propose the method for Buffer overflow detection based on Abstract syntax tree, Control flow graph, and Data dependency graph (BodeACD) in C/C++ programs with source code. Firstly, BodeACD constructs the function samples of buffer overflow that are available on Github, then represents them as code representation sequences, which fuse control flow, data dependency, and syntax structure of source code to reduce information loss during code representation. Finally, BodeACD learns vulnerability patterns for vulnerability detection through deep learning. The results of the experiments show that BodeACD has increased the precision and recall by 6.3% and 8.5% respectively compared with the latest methods, which can effectively improve vulnerability detection and reduce False-positive rate and False-negative rate.

Keywords: vulnerability detection, abstract syntax tree, control flow graph, data dependency graph, code representation, deep learning

Procedia PDF Downloads 170

Authors: Jacob Nortey

Abstract:

Introduction: In recent years, there has been an enormous success in discoveries of scientific knowledge in medicine coupled with the advancement of technology. Despite all these successes, diagnoses and treatment of diseases have become complex. According to the Ibero – American Study of Adverse Effects (IBEAS), about 10% of hospital patients suffer from secondary damage during the care process, and approximately 2% die from this process. Many clinical decision support systems have been developed to help mitigate some healthcare medical errors. Method: Relevant databases were searched, including ones that were peculiar to the clinical decision support system (that is, using google scholar, Pub Med and general google searches). The articles were then screened for a comprehensive overview of the functionality, consultative style and statistical usage of Dxplain Clinical decision support systems. Results: Inferences drawn from the articles showed high usage of Dxplain clinical decision support system for problem-based learning among students in developed countries as against little or no usage among students in Low – and Middle – income Countries. The results also indicated high usage among general practitioners. Conclusion: Despite the challenges Dxplain presents, the benefits of its usage to clinicians and students are enormous.

Keywords: dxplain, clinical decision support sytem, diagnosis, support systems

Procedia PDF Downloads 79