Search results for: tree rings

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

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Authors: Hailu Dessalegn, T. Srinivas

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We propose new multiple-channel piezoelectric (PZT) actuated tunable optical filter based on racetrack multi-ring resonators for wavelength de-multiplexing network applications. We design tunable eight-channel wavelength de-multiplexer consisting of eight cascaded PZT actuated tunable multi-ring resonator filter with a channel spacing of 1.6 nm. The filter for each channel is basically structured on a suspended beam, sandwiched with piezoelectric material and built in integrated ring resonators which are placed on the middle of the beam to gain uniform stress and linearly varying longitudinal strain. A reference single mode serially coupled multi stage racetrack ring resonator with the same radii and coupling length is designed with a line width of 0.8974 nm with a flat top pass band at 1dB of 0.5205 nm and free spectral range of about 14.9 nm. In each channel, a small change in the perimeter of the rings is introduced to establish the shift in resonance wavelength as per the defined channel spacing. As a result, when a DC voltage is applied, the beams will elongate, which involves mechanical deformation of the ring resonators that induces a stress and a strain, which brings a change in refractive index and perimeter of the rings leading to change in the output spectrum shift providing the tunability of central wavelength in each channel. Simultaneous wave length shift as high as 45.54 pm/V has been achieved with negligible tunability variation in the eight channel tunable optical filter proportional to the DC voltage applied in the structure, and it is capable of tuning up to 3.45 nm in each channel with a maximum loss difference of 0.22 dB in the tuning range and out of band rejection ratio of 35 dB, with a low channel crosstalk ≤ 30 dB.

Keywords: optical MEMS, piezoelectric (PZT) actuation, tunable optical filter, wavelength de-multiplexer

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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

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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

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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

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Authors: C. S. Jeba Samuel

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Soil, especially from oil field has posed a great hazard for terrestrial and marine ecosystems. The traditional treatment of oil contaminated soil cannot degrade the crude oil completely. So far, biodegradation proves to be an efficient method. During biodegradation, crude oil is used as the carbon source and addition of nitrogenous compounds increases the microbial growth, resulting in the effective breakdown of crude oil components to low molecular weight components. The present study was carried out to evaluate the biodegradation of crude oil by hydrocarbon-degrading microorganism Pseudomonas aeruginosa isolated from natural environment like oil contaminated soil. Pseudomonas aeruginosa, an oil degrading microorganism also called as hydrocarbon utilizing microorganism (or “HUM” bug) can utilize crude oil as sole carbon source. In this study, the biodegradation of crude oil was conducted with modified mineral basal salt medium and nitrogen sources so as to increase the degradation. The efficacy of the plasmid from the isolated strain was incorporated into E.coli DH5 α host to speed up the degradation of oil. The usage of molecular techniques has increased oil degradation which was confirmed by the degradation of aromatic and aliphatic rings of hydrocarbons and was inferred by the lesser number of peaks in Fourier Transform Infrared Spectroscopy (FTIR). The gas chromatogram again confirms better degradation by transformed cells by the lesser number of components obtained in the oil treated with transformed cells. This study demonstrated the technical feasibility of using direct inoculation of transformed cells onto the oil contaminated region thereby leading to the achievement of better oil degradation in a shorter time than the degradation caused by the wild strain.

Keywords: biodegradation, aromatic rings, plasmid, hydrocarbon, Fourier Transform Infrared Spectroscopy (FTIR)

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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

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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

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Authors: Adriana Stefan, Cristina-Elisabeta Pelin, George Pelin, Maria Daniela Stelescu, Elena Manaila

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Elastomeric composites have been known for a long time, but, to our knowledge, space and the aeronautic community has been directing a special attention to them only in the last decade. The required properties of advanced elastomeric materials used in space applications (such as O-rings) are sealing, abrasion, low-temperature flexibility, the long-term compression set properties, impact resistance and low-temperature thermal stability in different environments, such as ionized radiations. Basically, the elastomeric nanocomposites are composed of a rubber matrix and a wide and varied range of nanofillers, added with the aim of improving the physico-mechanical and elasticity modulus properties of the materials as well as their stability in different environments. The paper presents a partial synthesis of the research regarding the use of silicon carbide in nanometric form and/or organophylized montmorillonite as fillers in butyl rubber matrix. The need of composite materials arose from the fact that stand-alone polymers are ineffective in providing all the superior properties required by different applications. These drawbacks can be diminished or even eliminated by incorporating a new range of additives into the organic matrix, fillers that have important roles in modifying properties of various polymers. A composite material can provide superior and unique mechanical and physical properties because it combines the most desirable properties of its constituents while suppressing their least desirable properties. The commercial importance of polymers and the continuous increase of their use results in the continuous demand for improvement in their properties to meet the necessary conditions. To study the performance of the elastomeric nanocomposites were mechanically tested, it will be tested the qualities of tensile at low temperatures and RT and the behavior at the compression at cryogenic to room temperatures and under different environments. The morphology of specimens will be investigated by optical and scanning electronic microscopy.

Keywords: elastomeric nanocomposites, O-rings, space applications, mechanical properties

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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

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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

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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

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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

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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

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Authors: Michele Aleandri, Matteo Colangeli, Davide Gabrielli

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We study a generalization to a continuous setting of the classical Markov chain tree theorem. In particular, we consider an irreducible diffusion process on a metric graph. The unique invariant measure has an atomic component on the vertices and an absolutely continuous part on the edges. We show that the corresponding density at x can be represented by a normalized superposition of the weights associated to metric arborescences oriented toward the point x. A metric arborescence is a metric tree oriented towards its root. The weight of each oriented metric arborescence is obtained by the product of the exponential of integrals of the form ∫a/b², where b is the drift and σ² is the diffusion coefficient, along the oriented edges, for a weight for each node determined by the local orientation of the arborescence around the node and for the inverse of the diffusion coefficient at x. The metric arborescences are obtained by cutting the original metric graph along some edges.

Keywords: diffusion processes, metric graphs, invariant measure, reversibility

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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

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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

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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

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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

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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

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Authors: Lutful Karim, Mohammed S. Al-kahtani

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Sensors are being used in various applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control and hence, play the vital role in the growth of big data. However, sensors collect redundant data. Thus, aggregating and filtering sensors data are significantly important to design an efficient big data framework. Current researches do not focus on aggregating and filtering data at multiple layers of sensor-based big data framework. Thus, this paper introduces (i) three layers data aggregation and framework for big data and (ii) a priority-based, dynamic data aggregation scheme (PDDA) for the lowest layer at sensors. Simulation results show that the PDDA outperforms existing tree and cluster-based data aggregation scheme in terms of overall network energy consumptions and end-to-end data transmission delay.

Keywords: big data, clustering, tree topology, data aggregation, sensor networks

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Chien-Kuo Chang, You-Syuan Wu, Min-Chiu Wu, Chun-Wei Wang

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Epoxy resin is widely used in the insulation of high-voltage equipment such as transformers and insulating bushings due to its good electrical insulation properties. However, manufacturing defects will cause unpredictable accidents. Therefore, it is an important issue to determine the insulation state of equipment by measuring partial discharges. In this study, the needle-plane electrode structure was used to test the epoxy resin electrical treeing insulation deterioration phenomenon. During the test, we measured the partial discharge signal and then used the signal as the input data of the insulation status assessment system, which was developed in the past research. The experimental samples were made of transparent epoxy resin to facilitate the observation of changes, and were made in the distance of 1 cm and 1.5 cm of 5 sets. During the experiment, a magnifying glass with a total magnification of 2 times is set up to enlarge the picture and a time-lapse camera is used to record the changes of the experimental samples. In the experiment, we found that the electrical treeing phenomenon of the epoxy resin insulation deterioration process can be divided into several stages: initial dark tree, filamentary tree, reverse tree, and insulation breakdown, and simply observed each stage of electrical treeing. After substituting the partial discharge signal into the insulation status assessment system, it can be found that most experimental samples were assessed into the attention period in the middle of the test and into the risky period in the middle and late of the test. Compared to the attention period signal to the recorded film, there was no obvious correlation currently, but compared to the risky period signal, we can see that the experimental sample deformed due to the temperature rise caused by the larger and more frequent discharge. Besides, we also try to collect data about different types of PD by mixing high dielectric constant materials and changing the interior constitution of the sample. Recording data like PDIV、PDEV、RPDIV, the data that recorded can improve performance of various algorithm models.

Keywords: partial discharge, insulation deterioration, epoxy resin, electrical treeing

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Authors: Saurav Kumar Suman, P. Karthigayani

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In this paper the height of the tree is estimated and forest types is classified from the multi temporal RISAT-1 Horizontal-Horizontal (HH) and Horizontal-Vertical (HV) Polarised Satellite Aperture Radar (SAR) data. The novelty of the proposed project is combined use of the Back-scattering Coefficients (Sigma Naught) and the Coherence. It uses Water Cloud Model (WCM). The approaches use two main steps. (a) Extraction of the different forest parameter data from the Product.xml, BAND-META file and from Grid-xxx.txt file come with the HH & HV polarized data from the ISRO (Indian Space Research Centre). These file contains the required parameter during height estimation. (b) Calculation of the Vegetation and Ground Backscattering, Coherence and other Forest Parameters. (c) Classification of Forest Types using the ENVI 5.0 Tool and ROI (Region of Interest) calculation.

Keywords: RISAT-1, classification, forest, SAR data

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Authors: Humphrey Agevi, Harrison Tsingalia, Richard Onwonga, Shem Kuyah

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Most of the world ecosystems have been affected by the effects of climate change. Efforts have been made to mitigate against climate change effects. While most studies have been done in forest ecosystems and pure plant plantations, trees on farms including agroforestry have only received attention recently. Agroforestry systems and tree cover on agricultural lands make an important contribution to climate change mitigation but are not systematically accounted for in the global carbon budgets. This study sought to: (i) determine tree diversity in different agroforestry practices; (ii) determine tree biomass in different agroforestry practices. Study area was determined according to the Land degradation surveillance framework (LSDF). Two study sites were established. At each of the site, a 5km x 10km block was established on a map using Google maps and satellite images. Way points were then uploaded in a GPS helped locate the blocks on the ground. In each of the blocks, Nine (8) sentinel clusters measuring 1km x 1km were randomized. Randomization was done in a common spreadsheet program and later be downloaded to a Global Positioning System (GPS) so that during surveys the researchers were able to navigate to the sampling points. In each of the sentinel cluster, two farm boundaries were randomly identified for convenience and to avoid bias. This led to 16 farms in Kakamega South and 16 farms in Kakamega North totalling to 32 farms in Kakamega Site. Species diversity was determined using Shannon wiener index. Tree biomass was determined using allometric equation. Two agroforestry practices were found; homegarden and hedgerow. Species diversity ranged from 0.25-2.7 with a mean of 1.8 ± 0.10. Species diversity in homegarden ranged from 1-2.7 with a mean of 1.98± 0.14. Hedgerow species diversity ranged from 0.25-2.52 with a mean of 1.74± 0.11. Total Aboveground Biomass (AGB) determined was 13.96±0.37 Mgha-1. Homegarden with the highest abundance of trees had higher above ground biomass (AGB) compared to hedgerow agroforestry. This study is timely as carbon budgets in the agroforestry can be incorporated in the global carbon budgets and improve the accuracy of national reporting of greenhouse gases.

Keywords: agroforestry, allometric equations, biomass, climate change

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Authors: Hatice Esra Akgül, Celaleddin Öztürk

Abstract:

Lichen biodiversity in forests is controlled by environmental conditions. Epiphytic lichens have some degree of substrate specificity. Diversity and distribution of epiphytic lichens are affected by humidity, light, altitude, temperature, bark pH of the trees.This study describes the epiphytic lichen communities with comparing their preferred tree species. 34 epiphytic lichen taxa are reported on Pinus sp. L., Quercus sp. L., Fagus sp. L., Carpinus sp. L., Abies sp. Mill., Fraxinus sp. Tourn. ex L. from different altitudes around the Şeker Canyon (Karabük, Turkey). 11 of these taxa are growing on Quercus sp., 10 of them are growing on Fagus sp., 7 of them are growing on Pinus sp., 4 of them are on Carpinus sp., 2 of them are on Abies sp. and one of them is on Fraxinus sp. Evernia prunastri (L.) Ach. is growing on both of Fagus sp. and Quercus sp. Lecanora pulicaris (Pers.) Ach. is growing on both of Abies sp. and Quercus sp.

Keywords: biodiversity, epiphytic lichen, forest, Turkey

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Authors: Dilesh Indorkar

Abstract:

The aromatic, six membered ring containing three nitrogen atoms are known as triazines. Three triazines are theoretically possible, 1,3,5-triazine, 1,2,4-triazine and 1,2,3-triazine[1]. The 1,3,5-triazines are amongst the oldest known organic compounds. Originally they were called the symmetric triazines. Usuelly abbreviated to s- or sys triazines. The numbering follows the usual convention of beginning at the hetero atom as shown for the parent compound 1,3,5-triazine (I). The triazine rings, each contain 6 pi electrons which fill three bonding molecular orbital there are also three pairs of non bonding electrons in each molecule which are responsible for basic properties of the compounds.

Keywords: s-triazine, thiazoline, isoxazoline, benzoxazine heterocyclic

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Authors: Meera Tandan, Mohan Timilsina, Martin Cormican, Akke Vellinga

Abstract:

Background: In general practice, many infections are treated empirically without microbiological confirmation. Understanding susceptibility of antimicrobials during empirical prescribing can be helpful to reduce inappropriate prescribing. This study aims to apply a prediction model using a decision tree approach to predict the antimicrobial resistance (AMR) of urinary tract infections (UTI) based on non-clinical features of patients over 65 years. Decision tree models are a novel idea to predict the outcome of AMR at an initial stage. Method: Data was extracted from the database of the microbiological laboratory of the University Hospitals Galway on all antimicrobial susceptibility testing (AST) of urine specimens from patients over the age of 65 from January 2011 to December 2014. The primary endpoint was resistance to common antimicrobials (Nitrofurantoin, trimethoprim, ciprofloxacin, co-amoxiclav and amoxicillin) used to treat UTI. A classification and regression tree (CART) model was generated with the outcome ‘resistant infection’. The importance of each predictor (the number of previous samples, age, gender, location (nursing home, hospital, community) and causative agent) on antimicrobial resistance was estimated. Sensitivity, specificity, negative predictive (NPV) and positive predictive (PPV) values were used to evaluate the performance of the model. Seventy-five percent (75%) of the data were used as a training set and validation of the model was performed with the remaining 25% of the dataset. Results: A total of 9805 UTI patients over 65 years had their urine sample submitted for AST at least once over the four years. E.coli, Klebsiella, Proteus species were the most commonly identified pathogens among the UTI patients without catheter whereas Sertia, Staphylococcus aureus; Enterobacter was common with the catheter. The validated CART model shows slight differences in the sensitivity, specificity, PPV and NPV in between the models with and without the causative organisms. The sensitivity, specificity, PPV and NPV for the model with non-clinical predictors was between 74% and 88% depending on the antimicrobial. Conclusion: The CART models developed using non-clinical predictors have good performance when predicting antimicrobial resistance. These models predict which antimicrobial may be the most appropriate based on non-clinical factors. Other CART models, prospective data collection and validation and an increasing number of non-clinical factors will improve model performance. The presented model provides an alternative approach to decision making on antimicrobial prescribing for UTIs in older patients.

Keywords: antimicrobial resistance, urinary tract infection, prediction, decision tree

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Authors: Richard Renou, Laurent Soulard

Abstract:

Lasers are widely used in glass material processing, from waveguide fabrication to channel drilling. The gradual damage of glass optics under UV lasers is also an important issue to be addressed. Glass materials (including metallic glasses) can undergo a permanent densification under laser-induced shock loading. Despite increased interest on interactions between laser and glass materials, little is known about the structural mechanisms involved under shock loading. For example, the densification process in silica glasses occurs between 8 GPa and 30 GPa. Above 30 GPa, the glass material returns to the original density after relaxation. Investigating these unusual mechanisms in silica glass will provide an overall better understanding in glass behaviour. Non-Equilibrium Molecular Dynamics simulations (NEMD) were carried out in order to gain insight on the silica glass microscopic structure under shock loading. The shock was generated by the use of a piston impacting the glass material at high velocity (from 100m/s up to 2km/s). Periodic boundary conditions were used in the directions perpendicular to the shock propagation to model an infinite system. One-dimensional shock propagations were therefore studied. Simulations were performed with the STAMP code developed by the CEA. A very specific structure is observed in a silica glass. Oxygen atoms around Silicon atoms are organized in tetrahedrons. Those tetrahedrons are linked and tend to form rings inside the structure. A significant amount of empty cavities is also observed in glass materials. In order to understand how a shock loading is impacting the overall structure, the tetrahedrons, the rings and the cavities were thoroughly analysed. An elastic behaviour was observed when the shock pressure is below 8 GPa. This is consistent with the Hugoniot Elastic Limit (HEL) of 8.8 GPa estimated experimentally for silica glasses. Behind the shock front, the ring structure and the cavity distribution are impacted. The ring volume is smaller, and most cavities disappear with increasing shock pressure. However, the tetrahedral structure is not affected. The elasticity of the glass structure is therefore related to a ring shrinking and a cavity closing. Above the HEL, the shock pressure is high enough to impact the tetrahedral structure. An increasing number of hexahedrons and octahedrons are formed with the pressure. The large rings break to form smaller ones. The cavities are however not impacted as most cavities are already closed under an elastic shock. After the material relaxation, a significant amount of hexahedrons and octahedrons is still observed, and most of the cavities remain closed. The overall ring distribution after relaxation is similar to the equilibrium distribution. The densification process is therefore related to two structural mechanisms: a change in the coordination of silicon atoms and a cavity closing. To sum up, non-equilibrium molecular dynamics were carried out to investigate silica behaviour under shock loading. Analysing the structure lead to interesting conclusions upon the elastic and the densification mechanisms in glass materials. This work will be completed with a detailed study of the mechanism occurring above 30 GPa, where no sign of densification is observed after the material relaxation.

Keywords: densification, molecular dynamics simulations, shock loading, silica glass

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Authors: Darren Zou

Abstract:

Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.

Keywords: BART, Bayesian, predict, stock

Procedia PDF Downloads 98