Search results for: t tree representation
1802 Third Eye: A Hybrid Portrayal of Visuospatial Attention through Eye Tracking Research and Modular Arithmetic
Authors: Shareefa Abdullah Al-Maqtari, Ruzaika Omar Basaree, Rafeah Legino
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A pictorial representation of hybrid forms in science-art collaboration has become a crucial issue in the course of exploring a new painting technique development. This is straight related to the reception of an invisible-recognition phenomenology. In hybrid pictorial representation of invisible-recognition phenomenology, the challenging issue is how to depict the pictorial features of indescribable objects from its mental source, modality and transparency. This paper proposes the hybrid technique of painting Demonstrate, Resemble, and Synthesize (DRS) through a combination of the hybrid aspect-recognition representation of understanding picture, demonstrative mod, the number theory, pattern in the modular arithmetic system, and the coherence theory of visual attention in the dynamic scenes representation. Multi-methods digital gaze data analyses, pattern-modular table operation design, and rotation parameter were used for the visualization. In the scientific processes, Eye-trackingvideo-sections based was conducted using Tobii T60 remote eye tracking hardware and TobiiStudioTM analysis software to collect and analyze the eye movements of ten participants when watching the video clip, Alexander Paulikevitch’s performance’s ‘Tajwal’. Results: we found that correlation of fixation count in section one was positively and moderately correlated with section two Person’s (r=.10, p < .05, 2-tailed) as well as in fixation duration Person’s (r=.10, p < .05, 2-tailed). However, a paired-samples t-test indicates that scores were significantly higher for the section one (M = 2.2, SD = .6) than for the section two (M = 1.93, SD = .6) t(9) = 2.44, p < .05, d = 0.87. In the visual process, the exported data of gaze number N was resembled the hybrid forms of visuospatial attention using the table-mod-analyses operation. The explored hybrid guideline was simply applicable, and it could be as alternative approach to the sustainability of contemporary visual arts.Keywords: science-art collaboration, hybrid forms, pictorial representation, visuospatial attention, modular arithmetic
Procedia PDF Downloads 3591801 Challenging Hegemonic Masculinity in Nigerian Hip Hop: An Evaluation of Gender Representation in Falz the Bahd Guy’s Moral Instruction Album
Authors: Adelaja O. Oriade
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The Nigerian hip-hop music genre, like the African American scene where it was adopted from, is riddled with musical lyrics that amplify and normalize hypermasculinity, homophobia, sexism, and objectification of women. Several factors are responsible for this anomaly; however, the greatest factor is the urge of hip-hop musicians to achieve the commercial success that is dependent on selling records and appealing to the established societal accepted norm for hip-hop music. Consequently, this paper presents a counter-narrative of this gender representation within the Nigerian hip-hop industry. This study analyzed the musical lyrics of the ‘Hypocrisy’ track on the 2019 album of famous Nigerian rapper, Falz the Bahd Guy; and argued that Falz in this album challenged the predominant ideas of hegemonic masculinity by singing in favor of LGBT people and women. Also, based on the success of this album, this paper argues that a hip-hop album can achieve commercial success without aligning with predominant hip-hop parameters of gender representation. The study recommends that future studies should evaluate the reactions of Nigerians to these gender presentations by Falz the Bahd guy.Keywords: hegemonic masculinity, hypermasculinity, LGBT, misogyny, sexism
Procedia PDF Downloads 1151800 Cubical Representation of Prime and Essential Prime Implicants of Boolean Functions
Authors: Saurabh Rawat, Anushree Sah
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K Maps are generally and ideally, thought to be simplest form for obtaining solution of Boolean equations. Cubical Representation of Boolean equations is an alternate pick to incur a solution, otherwise to be meted out with Truth Tables, Boolean Laws, and different traits of Karnaugh Maps. Largest possible k- cubes that exist for a given function are equivalent to its prime implicants. A technique of minimization of Logic functions is tried to be achieved through cubical methods. The main purpose is to make aware and utilise the advantages of cubical techniques in minimization of Logic functions. All this is done with an aim to achieve minimal cost solution.rKeywords: K-maps, don’t care conditions, Boolean equations, cubes
Procedia PDF Downloads 3811799 A Similarity/Dissimilarity Measure to Biological Sequence Alignment
Authors: Muhammad A. Khan, Waseem Shahzad
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Analysis of protein sequences is carried out for the purpose to discover their structural and ancestry relationship. Sequence similarity determines similar protein structures, similar function, and homology detection. Biological sequences composed of amino acid residues or nucleotides provide significant information through sequence alignment. In this paper, we present a new similarity/dissimilarity measure to sequence alignment based on the primary structure of a protein. The approach finds the distance between the two given sequences using the novel sequence alignment algorithm and a mathematical model. The algorithm runs at a time complexity of O(n²). A distance matrix is generated to construct a phylogenetic tree of different species. The new similarity/dissimilarity measure outperforms other existing methods.Keywords: alignment, distance, homology, mathematical model, phylogenetic tree
Procedia PDF Downloads 1741798 Effect of Temperature on Germination and Seedlings Development of Moringa Oleifera Lam
Authors: Khater N., Rahmine S., Bougoffa C., Bouguenna T., Ouanes H.
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Moringa oleifera L. species is considered one of the most useful trees in the world, possessing many interesting properties that make it of great scientific interest. It has been described as the miracle tree, the tree of a thousand virtues, the tree of life and God's gift to man. The present study aims to introduce, produce, and develop Moringa Oleifera as a species with high ecological potential (resistance to biotic and abiotic stresses and productivity), high added value, and multiple virtues. The aim of this work is to study the germination potential of this species under different temperature conditions. In this study, the germination assay was tested in two different temperature ranges: internal (laboratory ambient temperature between 22°c and 25°c) and external (seasonal temperature between 4°c and 8°c). Morphological and physiological analyses were carried out by Shoot length (SL), root length (RL), diameter at the crown (DC), fresh weight of shoots (FWS), fresh weight of roots (FWR), dry weight of shoots (DWS) and dry weight of roots (DWS). For all these variables, the results of the study reveal a significant difference between the two temperature intervals, with a high germination rate of 81. 81% and plant growth was rapid (7cm during 24h) in the laboratory temperature; in contrast to the external temperatures, a germination rate value of around 27% was recorded, and germination took place after 20 days of sowing, with slower plant growth. The results obtained show that a temperature greater than or equal to 25° is the ideal temperature for the germination and growth of moringa seeds and has a positive influence on the speed and percentage of germination.Keywords: moringa oleifera, temperature, germination rate, growth, biomass
Procedia PDF Downloads 531797 Performance Analysis with the Combination of Visualization and Classification Technique for Medical Chatbot
Authors: Shajida M., Sakthiyadharshini N. P., Kamalesh S., Aswitha B.
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Natural Language Processing (NLP) continues to play a strategic part in complaint discovery and medicine discovery during the current epidemic. This abstract provides an overview of performance analysis with a combination of visualization and classification techniques of NLP for a medical chatbot. Sentiment analysis is an important aspect of NLP that is used to determine the emotional tone behind a piece of text. This technique has been applied to various domains, including medical chatbots. In this, we have compared the combination of the decision tree with heatmap and Naïve Bayes with Word Cloud. The performance of the chatbot was evaluated using accuracy, and the results indicate that the combination of visualization and classification techniques significantly improves the chatbot's performance.Keywords: sentimental analysis, NLP, medical chatbot, decision tree, heatmap, naïve bayes, word cloud
Procedia PDF Downloads 671796 Unveiling Electrical Treeing Mechanisms in Epoxy Resin Insulation Degradation
Authors: Chien-Kuo Chang, You-Syuan Wu, Min-Chiu Wu, Bharath-Kumar Boyanapalli
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The electrical treeing mechanism in epoxy resin insulation is a critical area of study concerning the degradation of high-voltage electrical equipment. In this study, we conducted pressure-induced degradation experiments on epoxy resin specimens using a needle-plane electrode structure to simulate electrical treeing. The specimens featured two different defect spacings, allowing for detailed observation facilitated by time-lapse photography. Our investigation revealed four distinct stages of insulation degradation: initial dark tree growth, filamentary tree growth, reverse tree growth, and eventual insulation breakdown. The initial dark treeing stage, though shortest in duration, exhibited a thicker main branch and shorter branching, ceasing upon the appearance of filamentary treeing. Filamentary treeing manifested in two forms: dark filamentary treeing during the resin's glassy state, characterized by branching structures, and fuzzy filamentary treeing during the rubbery state, resembling white feathers. The channels formed by filamentary treeing were observed to be as narrow as a few micrometers and continued to grow until the end of the experiment. Additionally, the transition to reverse treeing occurred when filamentary treeing reached the ground electrode, with the earliest manifestation being growth from the ground electrode towards the high-voltage end.Keywords: epoxy resin insulation, high-voltage equipment, electrical treeing mechanism
Procedia PDF Downloads 661795 Allometric Models for Biomass Estimation in Savanna Woodland Area, Niger State, Nigeria
Authors: Abdullahi Jibrin, Aishetu Abdulkadir
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The development of allometric models is crucial to accurate forest biomass/carbon stock assessment. The aim of this study was to develop a set of biomass prediction models that will enable the determination of total tree aboveground biomass for savannah woodland area in Niger State, Nigeria. Based on the data collected through biometric measurements of 1816 trees and destructive sampling of 36 trees, five species specific and one site specific models were developed. The sample size was distributed equally between the five most dominant species in the study site (Vitellaria paradoxa, Irvingia gabonensis, Parkia biglobosa, Anogeissus leiocarpus, Pterocarpus erinaceous). Firstly, the equations were developed for five individual species. Secondly these five species were mixed and were used to develop an allometric equation of mixed species. Overall, there was a strong positive relationship between total tree biomass and the stem diameter. The coefficient of determination (R2 values) ranging from 0.93 to 0.99 P < 0.001 were realised for the models; with considerable low standard error of the estimates (SEE) which confirms that the total tree above ground biomass has a significant relationship with the dbh. The F-test value for the biomass prediction models were also significant at p < 0.001 which indicates that the biomass prediction models are valid. This study recommends that for improved biomass estimates in the study site, the site specific biomass models should preferably be used instead of using generic models.Keywords: allometriy, biomass, carbon stock , model, regression equation, woodland, inventory
Procedia PDF Downloads 4401794 The Forms of Representation in Architectural Design Teaching: The Cases of Politecnico Di Milano and Faculty of Architecture of the University of Porto
Authors: Rafael Sousa Santos, Clara Pimena Do Vale, Barbara Bogoni, Poul Henning Kirkegaard
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The representative component, a determining aspect of the architect's training, has been marked by an exponential and unprecedented development. However, the multiplication of possibilities has also multiplied uncertainties about architectural design teaching, and by extension, about the very principles of architectural education. In this paper, it is intended to present the results of a research developed on the following problem: the relation between the forms of representation and the architectural design teaching-learning processes. The research had as its object the educational model of two schools – the Politecnico di Milano (POLIMI) and the Faculty of Architecture of the University of Porto (FAUP) – and was led by three main objectives: to characterize the educational model followed in both schools focused on the representative component and its role; to interpret the relation between forms of representation and the architectural design teaching-learning processes; to consider their possibilities of valorisation. Methodologically, the research was conducted according to a qualitative embedded multiple-case study design. The object – i.e., the educational model – was approached in both POLIMI and FAUP cases considering its Context and three embedded unities of analysis: the educational Purposes, Principles, and Practices. In order to guide the procedures of data collection and analysis, a Matrix for the Characterization (MCC) was developed. As a methodological tool, the MCC allowed to relate the three embedded unities of analysis with the three main sources of evidence where the object manifests itself: the professors, expressing how the model is assumed; the architectural design classes, expressing how the model is achieved; and the students, expressing how the model is acquired. The main research methods used were the naturalistic and participatory observation, in-person-interview and documentary and bibliographic review. The results reveal the importance of the representative component in the educational model of both cases, despite the differences in its role. In POLIMI's model, representation is particularly relevant in the teaching of architectural design, while in FAUP’s model, it plays a transversal role – according to an idea of 'general training through hand drawing'. In fact, the difference between models relative to representation can be partially understood by the level of importance that each gives to hand drawing. Regarding the teaching of architectural design, the two cases are distinguished in the relation with the representative component: while in POLIMI the forms of representation serve essentially an instrumental purpose, in FAUP they tend to be considered also for their methodological dimension. It seems that the possibilities for valuing these models reside precisely in the relation between forms of representation and architectural design teaching. It is expected that the knowledge base developed in this research may have three main contributions: to contribute to the maintenance of the educational model of POLIMI and FAUP; through the precise description of the methodological procedures, to contribute by transferability to similar studies; through the critical and objective framework of the problem underlying the forms of representation and its relation with architectural design teaching, to contribute to the broader discussion concerning the contemporary challenges on architectural education.Keywords: architectural design teaching, architectural education, educational models, forms of representation
Procedia PDF Downloads 1141793 African Women in Power: An Analysis of the Representation of Nigerian Business Women in Television
Authors: Ifeanyichukwu Valerie Oguafor
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Women generally have been categorized and placed under the chain of business industry, sometimes highly regarded and other times merely. The social construction of womanhood does not in all sense support a woman going into business, let alone succeed in it because it is believed that it a man’s world. In a typical patriarchal setting, a woman is expected to know nothing more domestic roles. For some women, this is not the case as they have been able to break these barriers to excel in business amidst these social setting and stereotypes. This study examines media representation of Nigerians business women, using content analysis of TV interviews as media text, framing analysis as an approach in qualitative methodology, The study further aims to analyse media frames of two Nigerian business women: FolorunshoAlakija, a business woman in the petroleum industry with current net worth 1.1 billion U.S dollars, emerging as the richest black women in the world 2014. MosunmolaAbudu, a media magnate in Nigeria who launched the first Africa’s global black entertainment and lifestyle network in 2013. This study used six predefined frames: the business woman, the myth of business women, the non-traditional woman, women in leading roles, the family woman, the religious woman, and the philanthropist woman to analyse the representation of Nigerian business women in the media. The analysis of the aforementioned frames on TV interviews with these women reveals that the media perpetually reproduces existing gender stereotype and do not challenge patriarchy. Women face challenges in trying to succeed in business while trying to keep their homes stable. This study concludes that the media represent and reproduce gender stereotypes in spite of the expectation of empowering women. The media reduces these women’s success insignificant rather than a role model for women in society.Keywords: representation of business women in the media, business women in Nigeria, framing in the media, patriarchy, women's subordination
Procedia PDF Downloads 1551792 Spatial Relationship of Drug Smuggling Based on Geographic Information System Knowledge Discovery Using Decision Tree Algorithm
Authors: S. Niamkaeo, O. Robert, O. Chaowalit
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In this investigation, we focus on discovering spatial relationship of drug smuggling along the northern border of Thailand. Thailand is no longer a drug production site, but Thailand is still one of the major drug trafficking hubs due to its topographic characteristics facilitating drug smuggling from neighboring countries. Our study areas cover three districts (Mae-jan, Mae-fahluang, and Mae-sai) in Chiangrai city and four districts (Chiangdao, Mae-eye, Chaiprakarn, and Wienghang) in Chiangmai city where drug smuggling of methamphetamine crystal and amphetamine occurs mostly. The data on drug smuggling incidents from 2011 to 2017 was collected from several national and local published news. Geo-spatial drug smuggling database was prepared. Decision tree algorithm was applied in order to discover the spatial relationship of factors related to drug smuggling, which was converted into rules using rule-based system. The factors including land use type, smuggling route, season and distance within 500 meters from check points were found that they were related to drug smuggling in terms of rules-based relationship. It was illustrated that drug smuggling was occurred mostly in forest area in winter. Drug smuggling exhibited was discovered mainly along topographic road where check points were not reachable. This spatial relationship of drug smuggling could support the Thai Office of Narcotics Control Board in surveillance drug smuggling.Keywords: decision tree, drug smuggling, Geographic Information System, GIS knowledge discovery, rule-based system
Procedia PDF Downloads 1621791 Empirical and Indian Automotive Equity Portfolio Decision Support
Authors: P. Sankar, P. James Daniel Paul, Siddhant Sahu
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A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.Keywords: Indian automotive sector, stock market decisions, equity portfolio analysis, decision tree classifiers, statistical data analysis
Procedia PDF Downloads 4761790 Sparse Representation Based Spatiotemporal Fusion Employing Additional Image Pairs to Improve Dictionary Training
Authors: Dacheng Li, Bo Huang, Qinjin Han, Ming Li
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Remotely sensed imagery with the high spatial and temporal characteristics, which it is hard to acquire under the current land observation satellites, has been considered as a key factor for monitoring environmental changes over both global and local scales. On a basis of the limited high spatial-resolution observations, challenged studies called spatiotemporal fusion have been developed for generating high spatiotemporal images through employing other auxiliary low spatial-resolution data while with high-frequency observations. However, a majority of spatiotemporal fusion approaches yield to satisfactory assumption, empirical but unstable parameters, low accuracy or inefficient performance. Although the spatiotemporal fusion methodology via sparse representation theory has advantage in capturing reflectance changes, stability and execution efficiency (even more efficient when overcomplete dictionaries have been pre-trained), the retrieval of high-accuracy dictionary and its response to fusion results are still pending issues. In this paper, we employ additional image pairs (here each image-pair includes a Landsat Operational Land Imager and a Moderate Resolution Imaging Spectroradiometer acquisitions covering the partial area of Baotou, China) only into the coupled dictionary training process based on K-SVD (K-means Singular Value Decomposition) algorithm, and attempt to improve the fusion results of two existing sparse representation based fusion models (respectively utilizing one and two available image-pair). The results show that more eligible image pairs are probably related to a more accurate overcomplete dictionary, which generally indicates a better image representation, and is then contribute to an effective fusion performance in case that the added image-pair has similar seasonal aspects and image spatial structure features to the original image-pair. It is, therefore, reasonable to construct multi-dictionary training pattern for generating a series of high spatial resolution images based on limited acquisitions.Keywords: spatiotemporal fusion, sparse representation, K-SVD algorithm, dictionary learning
Procedia PDF Downloads 2531789 Characteristics of Old-Growth and Secondary Forests in Relation to Age and Typhoon Disturbance
Authors: Teng-Chiu Lin, Pei-Jen Lee Shaner, Shin-Yu Lin
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Both forest age and physical damages due to weather events such as tropical cyclones can influence forest characteristics and subsequently its capacity to sequester carbon. Detangling these influences is therefore a pressing issue under climate change. In this study, we compared the compositional and structural characteristics of three forests in Taiwan differing in age and severity of typhoon disturbances. We found that the two forests (one old-growth forest and one secondary forest) experiencing more severe typhoon disturbances had shorter stature, higher wood density, higher tree species diversity, and lower typhoon-induced tree mortality than the other secondary forest experiencing less severe typhoon disturbances. On the other hand, the old-growth forest had a larger amount of woody debris than the two secondary forests, suggesting a dominant role of forest age on woody debris accumulation. Of the three forests, only the two experiencing more severe typhoon disturbances formed new gaps following two 2015 typhoons, and between these two forests, the secondary forest gained more gaps than the old-growth forest. Consider that older forests generally have more gaps due to a higher background tree mortality, our findings suggest that the age effects on gap dynamics may be reversed by typhoon disturbances. This study demonstrated the effects of typhoons on forest characteristics, some of which could negate the age effects and rejuvenate older forests. If cyclone disturbances were to intensity under climate change, the capacity of older forests to sequester carbon may be reduced.Keywords: typhoon, canpy gap, coarse woody debris, forest stature, forest age
Procedia PDF Downloads 2621788 Comparative Evaluation of Accuracy of Selected Machine Learning Classification Techniques for Diagnosis of Cancer: A Data Mining Approach
Authors: Rajvir Kaur, Jeewani Anupama Ginige
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With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.Keywords: artificial neural networks, breast cancer, classifiers, cervical cancer, f-score, machine learning, precision, recall
Procedia PDF Downloads 2701787 A Green Method for Selective Spectrophotometric Determination of Hafnium(IV) with Aqueous Extract of Ficus carica Tree Leaves
Authors: A. Boveiri Monji, H. Yousefnia, M. Haji Hosseini, S. Zolghadri
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A clean spectrophotometric method for the determination of hafnium by using a green reagent, acidic extract of Ficus carica tree leaves is developed. In 6-M hydrochloric acid, hafnium reacts with this reagent to form a yellow product. The formed product shows maximum absorbance at 421 nm with a molar absorptivity value of 0.28 × 104 l mol⁻¹ cm⁻¹, and the method was linear in the 2-11 µg ml⁻¹ concentration range. The detection limit value was found to be 0.312 µg ml⁻¹. Except zirconium and iron, the selectivity was good, and most of the ions did not show any significant spectral interference at concentrations up to several hundred times. The proposed method was green, simple, low cost, and selective.Keywords: spectrophotometric determination, Ficus caricatree leaves, synthetic reagents, hafnium
Procedia PDF Downloads 2001786 Appraising the Evolution of Architecture as the Representation of Material Culture: The Nigerian Digest
Authors: Ikenna Emmanuel Idoko
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Evolution and evolutionary processes are phenomena that have come to stay in the fabrics of the universal living, hence expressions such as universal evolution. These evolutions in the universe cut across all facets of human accomplishments, which architecture is a part of. There is a notion in political sciences that politics and the act of politicking are local, meaning that politics and political processes are unique and peculiar to a people, all dependent on their sociocultural makeup. The notion is also applicable in architecture because the architecture of a people is mostly dependent on several factors such as climatic conditions, material availability, socio-cultural beliefs and religious inclinations. Stemming from the cultural dimension, it is of course common knowledge that every society is driven by its own unique culture. The fusion of architecture and culture creates the actual uniqueness which underlines the “archi-cultural” representation of a people’s material culture. This paper is aimed at appraising architectural evolution as it affects the representation of the material culture of a people. For effective systemization of the aim, various spectacular kinds of literature were reviewed, coupled with the visitation and study of existing buildings in Nigeria to properly understand the live peculiarity in the architecture of the selected area. Since architecture needs a lot of pictorial pieces of evidence, pictures and graphical representations were extensively utilized, and channelled to aid a better understanding of the study. Amongst all, an important part of this paper is that it adds to the body of existing knowledge in the Arts and Humanities by speaking extensively to the tenets of cultural representation on buildings. Similarly, the field of architecture, specifically, traditional architecture, would be gaining some extra knowledge owing to the study of some important almost-neglected or forgotten architectural elements of various traditional buildings.Keywords: evolution, architecture, material, culture
Procedia PDF Downloads 461785 Efficient Credit Card Fraud Detection Based on Multiple ML Algorithms
Authors: Neha Ahirwar
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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree
Procedia PDF Downloads 541784 BART Matching Method: Using Bayesian Additive Regression Tree for Data Matching
Authors: Gianna Zou
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Propensity score matching (PSM), introduced by Paul R. Rosenbaum and Donald Rubin in 1983, is a popular statistical matching technique which tries to estimate the treatment effects by taking into account covariates that could impact the efficacy of study medication in clinical trials. PSM can be used to reduce the bias due to confounding variables. However, PSM assumes that the response values are normally distributed. In some cases, this assumption may not be held. In this paper, a machine learning method - Bayesian Additive Regression Tree (BART), is used as a more robust method of matching. BART can work well when models are misspecified since it can be used to model heterogeneous treatment effects. Moreover, it has the capability to handle non-linear main effects and multiway interactions. In this research, a BART Matching Method (BMM) is proposed to provide a more reliable matching method over PSM. By comparing the analysis results from PSM and BMM, BMM can perform well and has better prediction capability when the response values are not normally distributed.Keywords: BART, Bayesian, matching, regression
Procedia PDF Downloads 1391783 A Machine Learning Approach to Detecting Evasive PDF Malware
Authors: Vareesha Masood, Ammara Gul, Nabeeha Areej, Muhammad Asif Masood, Hamna Imran
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The universal use of PDF files has prompted hackers to use them for malicious intent by hiding malicious codes in their victim’s PDF machines. Machine learning has proven to be the most efficient in identifying benign files and detecting files with PDF malware. This paper has proposed an approach using a decision tree classifier with parameters. A modern, inclusive dataset CIC-Evasive-PDFMal2022, produced by Lockheed Martin’s Cyber Security wing is used. It is one of the most reliable datasets to use in this field. We designed a PDF malware detection system that achieved 99.2%. Comparing the suggested model to other cutting-edge models in the same study field, it has a great performance in detecting PDF malware. Accordingly, we provide the fastest, most reliable, and most efficient PDF Malware detection approach in this paper.Keywords: PDF, PDF malware, decision tree classifier, random forest classifier
Procedia PDF Downloads 831782 Crude Palm Oil Antioxidant Extraction and the Antioxidation Activity
Authors: Supriyono Supriyono, Sumardiyono Sumardiyono, Peni Pujiastuti, Dian Indriana Hapsari
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Crude palm oil (CPO) is a vegetable oil that came from a palm tree bunch. The productivity of the oil is 12 ton/hectare/year. Thus palm oil tree was known as highest vegetable oil yield. It was grown across Equatorial County, especially in Malaysia and Indonesia. The greenish-red color on CPO was come from carotenoid. Carotenoid is one of the antioxidants that could be extracted. Carotenoid could be used as functional food and other purposes. Another antioxidant that also found in CPO is tocopherol. The aim of the research work is to find antioxidant activity on CPO comparing to the synthetic antioxidant that available in a market. In this research work, antioxidant was extracted by a mixture of acetone and n.hexane, while the activity of the antioxidant extract was determined by DPPH method. Antioxidant activity of the extracted compound about 46% compared to pure tocopherol. While the solvent mixture compose by 90% acetone and 10% n. hexane meet the best on the antioxidant activity.Keywords: antioxidant, beta carotene, crude palm oil, DPPH, tocopherol
Procedia PDF Downloads 2021781 Conservation Status of a Lowland Tropical Forest in South-West, Nigeria
Authors: Lucky Dartsa Wakawa, Friday Nwabueze Ogana, Temitope Elizabeth Adeniyi
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Timely and reliable information on the status of a forest is essential for assessing the extent of regeneration and degradation. However, when such information is lacking effective forest management practices becomes impossible. Therefore, this study assessed the tree species composition, richness, diversity, structure of Oluwa forest reserve with the view of ascertaining it conservation status. A systematic line transect was used in the laying of eight (8) temporary sample plots (TSPs) of size 50m x 50m. Trees with Dbh ≥ 10cm in the selected plots were enumerated, identified and measured. The results indicate that 535 individual trees were enumerated cutting across 26 families and 58 species. The family Sterculiaceae recorded the highest number of species (10) and occurrence (112) representing 17.2% and 20.93% respectively. Celtis zenkeri is the species with the highest number of occurrence of tree per hectare and importance value index (IVI) of 59 and 53.81 respectively. The reserve has the Margalef's index of species richness, Shannon-Weiner diversity Index (H') and Pielou's Species Evenness Index (EH) of 9.07, 3.43 and 0.84 respectively. The forest has a mean Dbh (cm), mean height (m), total basal area/ha (m2) and total volume/ha (m3) of 24.7, 16.9, 36.63 and 602.09 respectively. The important tropical tree species identified includes Diospyros crassiflora Milicia excels, Mansonia altisima, Triplochiton scleroxylon. Despite the level of exploitation in the forest, the forest seems to be resilience. Given the right attention, it could regenerate and replenish to save some of the original species composition of the reserve.Keywords: forest conservation, forest structure, Lowland tropical forest, South-west Nigeria
Procedia PDF Downloads 3371780 The Effects of Stand Density, Standards and Species Composition on Biomass Production in Traditional Coppices
Authors: Marek Mejstřík, Radim Matula, Martin Šrámek
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Traditional coppices and coppice-with-standards were widely used throughout Europe and Asia for centuries but were largely abandoned in the second half of the 19th century, especially in central and northwestern Europe. In the last decades, there has been a renewed interest in traditional coppicing for nature conservation and most often, for rapid woody biomass production. However, there is little information on biomass productivity of traditional coppices and what affects it. Here, we focused on the effects of stand density, standards and tree species composition on sprout biomass production in newly restored coppices in the Czech Republic. We measured sprouts and calculated sprout biomass 7 years after the harvest from 2013 resprouting stumps in two 4 ha experimental plots. Each plot was divided into 64 subplots with different densities of standards and sprouting stumps. Total sprout biomass declined with increasing density of standards, but the effect of standards differed significantly among studied species. Whereas increasing density of standards decreased sprout biomass in Quercus petraea and Carpinus betulus, it did not affect sprout biomass productivity in Acer campestre and Tilia cordata. Sprout biomass on stand-level increased linearly with an increasing number of sprouting stumps and we observed no leveling of this relationship even in the highest densities of stumps. We also found a significant shift in tree species composition with the steeply declining relative abundance of Quercus in favor of other studied tree species.Keywords: traditional coppice, coppice with standards, sprout biomass, forest management
Procedia PDF Downloads 1561779 Stock Movement Prediction Using Price Factor and Deep Learning
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The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.Keywords: classification, machine learning, time representation, stock prediction
Procedia PDF Downloads 1401778 Machine Learning for Aiding Meningitis Diagnosis in Pediatric Patients
Authors: Karina Zaccari, Ernesto Cordeiro Marujo
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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research
Procedia PDF Downloads 1451777 Determining of the Performance of Data Mining Algorithm Determining the Influential Factors and Prediction of Ischemic Stroke: A Comparative Study in the Southeast of Iran
Authors: Y. Mehdipour, S. Ebrahimi, A. Jahanpour, F. Seyedzaei, B. Sabayan, A. Karimi, H. Amirifard
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Ischemic stroke is one of the common reasons for disability and mortality. The fourth leading cause of death in the world and the third in some other sources. Only 1/3 of the patients with ischemic stroke fully recover, 1/3 of them end in permanent disability and 1/3 face death. Thus, the use of predictive models to predict stroke has a vital role in reducing the complications and costs related to this disease. Thus, the aim of this study was to specify the effective factors and predict ischemic stroke with the help of DM methods. The present study was a descriptive-analytic study. The population was 213 cases from among patients referring to Ali ibn Abi Talib (AS) Hospital in Zahedan. Data collection tool was a checklist with the validity and reliability confirmed. This study used DM algorithms of decision tree for modeling. Data analysis was performed using SPSS-19 and SPSS Modeler 14.2. The results of the comparison of algorithms showed that CHAID algorithm with 95.7% accuracy has the best performance. Moreover, based on the model created, factors such as anemia, diabetes mellitus, hyperlipidemia, transient ischemic attacks, coronary artery disease, and atherosclerosis are the most effective factors in stroke. Decision tree algorithms, especially CHAID algorithm, have acceptable precision and predictive ability to determine the factors affecting ischemic stroke. Thus, by creating predictive models through this algorithm, will play a significant role in decreasing the mortality and disability caused by ischemic stroke.Keywords: data mining, ischemic stroke, decision tree, Bayesian network
Procedia PDF Downloads 1651776 Heart Failure Identification and Progression by Classifying Cardiac Patients
Authors: Muhammad Saqlain, Nazar Abbas Saqib, Muazzam A. Khan
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Heart Failure (HF) has become the major health problem in our society. The prevalence of HF has increased as the patient’s ages and it is the major cause of the high mortality rate in adults. A successful identification and progression of HF can be helpful to reduce the individual and social burden from this syndrome. In this study, we use a real data set of cardiac patients to propose a classification model for the identification and progression of HF. The data set has divided into three age groups, namely young, adult, and old and then each age group have further classified into four classes according to patient’s current physical condition. Contemporary Data Mining classification algorithms have been applied to each individual class of every age group to identify the HF. Decision Tree (DT) gives the highest accuracy of 90% and outperform all other algorithms. Our model accurately diagnoses different stages of HF for each age group and it can be very useful for the early prediction of HF.Keywords: decision tree, heart failure, data mining, classification model
Procedia PDF Downloads 3981775 Risk Analysis of Leaks from a Subsea Oil Facility Based on Fuzzy Logic Techniques
Authors: Belén Vinaixa Kinnear, Arturo Hidalgo López, Bernardo Elembo Wilasi, Pablo Fernández Pérez, Cecilia Hernández Fuentealba
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The expanded use of risk assessment in legislative and corporate decision-making has increased the role of expert judgement in giving data for security-related decision-making. Expert judgements are required in most steps of risk assessment: danger recognizable proof, hazard estimation, risk evaluation, and examination of choices. This paper presents a fault tree analysis (FTA), which implies a probabilistic failure analysis applied to leakage of oil in a subsea production system. In standard FTA, the failure probabilities of items of a framework are treated as exact values while evaluating the failure probability of the top event. There is continuously insufficiency of data for calculating the failure estimation of components within the drilling industry. Therefore, fuzzy hypothesis can be used as a solution to solve the issue. The aim of this paper is to examine the leaks from the Zafiro West subsea oil facility by using fuzzy fault tree analysis (FFTA). As a result, the research has given theoretical and practical contributions to maritime safety and environmental protection. It has been also an effective strategy used traditionally in identifying hazards in nuclear installations and power industries.Keywords: expert judgment, probability assessment, fault tree analysis, risk analysis, oil pipelines, subsea production system, drilling, quantitative risk analysis, leakage failure, top event, off-shore industry
Procedia PDF Downloads 1871774 Discerning Divergent Nodes in Social Networks
Authors: Mehran Asadi, Afrand Agah
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In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables.Keywords: online social networks, data mining, social cloud computing, interaction and collaboration
Procedia PDF Downloads 1471773 A Comprehensive Method of Fault Detection and Isolation based on Testability Modeling Data
Authors: Junyou Shi, Weiwei Cui
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Testability modeling is a commonly used method in testability design and analysis of system. A dependency matrix will be obtained from testability modeling, and we will give a quantitative evaluation about fault detection and isolation. Based on the dependency matrix, we can obtain the diagnosis tree. The tree provides the procedures of the fault detection and isolation. But the dependency matrix usually includes built-in test (BIT) and manual test in fact. BIT runs the test automatically and is not limited by the procedures. The method above cannot give a more efficient diagnosis and use the advantages of the BIT. A Comprehensive method of fault detection and isolation is proposed. This method combines the advantages of the BIT and Manual test by splitting the matrix. The result of the case study shows that the method is effective.Keywords: fault detection, fault isolation, testability modeling, BIT
Procedia PDF Downloads 326