Search results for: tree ring

Authors: Getamesay Kassaye Dimru

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The cultivation of fruit tree in degraded areas has two-fold importance. Firstly, it improves food availability and income, and secondly, it promotes the conservation of soil and water improving, in turn, the productivity of the land. The main objectives of this study are to identify the determinant of farmer's fruit trees plantation decision and to major fruit production challenges and opportunities of the study area. The analysis was made using primary data collected from 60 sample household selected randomly from the study area in 2016. The primary data was supplemented by data collected from a key informant. In addition to the descriptive statistics and statistical tests (Chi-square test and t-test), a logit model was employed to identify the determinant of fruit tree plantation decision. Drought, pest incidence, land degradation, lack of input, lack of capital and irrigation schemes maintenance, lack of misuse of irrigation water and limited agricultural personnel are the major production constraints identified. The opportunities that need to further exploited are better access to irrigation, main road access, endowment of preferred guava variety, experience of farmers, and proximity of the study area to research center. The result of logit model shows that from different factors hypothesized to determine fruit tree plantation decision, age of the household head accesses to market and perception of farmers about fruits' disease and pest resistance are found to be significant. The result has revealed important implications for the promotion of fruit production for both land degradation control and rehabilitation and increasing the livelihood of farming households.

Keywords: degradation, fruit, irrigation, pest

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

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

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

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Authors: Jephte Sompud, Emily A. Gilbert, Andy Russel Mojiol, Cynthia B. Sompud, Alim Biun

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Naturally regenerated acacia forest and secondary indigenous forest forms some of the urban forests in Sabah. Naturally regenerated acacia trees are usually seen along the road that exists as forest islands. Acacia tree is not an indigenous tree species in Sabah that was introduced in the 1960’s as fire breakers that eventually became one of the preferred trees for forest plantation for paper and pulp production. Due to its adaptability to survive even in impoverished soils and poor-irrigated land, this species has rapidly spread throughout Sabah through natural regeneration. Currently, there is a lack of study to investigate the bird population in the naturally regenerated acacia forest. This study is important because it shed some light on the role of naturally regenerated acacia forest on bird’s population, as bird is known to be a good bioindicator forest health. The aim of this study was to document the bird’s population in naturally regenerated acacia forest with that adjacent secondary indigenous forest. The study site for this study was at Universiti Malaysia Sabah (UMS) Campus. Two forest types in the campus were chosen as a study site, of which were naturally regenerated Acacia Forest and adjacent secondary indigenous forest, located at the UMS Hill. A total of 21 sampling days were conducted in each of the forest types. The method used during this study was solely mist nets with three pockets. Whenever a bird is caught, it is extracted from the net to be identified and measurements were recorded in a standard data sheet. Mist netting was conducted from 6 morning until 5 evening. This study was conducted between February to August 2014. Birds that were caught were ring banded to initiate a long-term study on the understory bird’s population in the Campus The data was analyzed using descriptive analysis, diversity indices, and t-test. The bird population diversity at naturally regenerated Acacia forest with those at the secondary indigenous forest was calculated using two common indices, of which were Shannon-Wiener and Simpson diversity index. There were 18 families with 33 species that were recorded from both sites. The number of species recorded at the naturally regenerated acacia forest was 26 species while at the secondary indigenous forest were 19 species. The Shannon diversity index for Naturally Regenerated Acacia Forest and secondary indigenous forests were 2.87 and 2.46. The results show that there was very significantly higher species diversity at the Naturally Regenerated Acacia Forest as opposed to the secondary indigenous forest (p<0.001). This suggests that Naturally Regenerated Acacia forest plays an important role in urban bird conservation. It is recommended that Naturally Regenerated Acacia Forests should be considered as an established urban forest conservation area as they do play a role in biodiversity conservation. More future studies in Naturally Regenerated Acacia Forest should be encouraged to determine the status and value of biodiversity conservation of this ecosystem.

Keywords: naturally regenerated acacia forest, bird population diversity, Universiti Malaysia Sabah, biodiversity conservation

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Authors: Khater Nadia, Garah Kenza

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

Keywords: moringa stenopetala, PEG, water stress, rate

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

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

Keywords: lignocellulosic biomass, bioethanol, pretreatment, sawdust

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

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

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

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Authors: Mohammed Berka, Khaled Merit

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Recent works on microwave filters show that the constituent materials such filters are very important in the design and realization. Several solutions have been proposed to improve the qualities of filtering. In this paper, we propose a new dual band-pass filter based on the coupling of a composite (CRLH) coplanar waveguide with complementary split ring resonators (CSRRs). The (CRLH) CPW is composed of two resonators, each one has an interdigital capacitor (CID) and two short-circuited stubs parallel to top ground plane. On the lower ground plane, we use defected ground structure technology (DGS) to engrave two (CSRRs) offered with different shapes and dimensions. Between the top ground plane and the substrate, we place a ferrite layer to control the electromagnetic coupling between (CRLH) CPW and (CSRRs). The global filter that has coplanar access will have a dual band-pass behavior around the magnetic resonances of (CSRRs). Since there’s no scientific or experimental result in the literature for this kind of complicated structure, it was necessary to perform simulation using HFSS Ansoft designer.

Keywords: complementary split ring resonators, coplanar waveguide, ferrite, filter, stub.

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Authors: Nesrine Gaaliche

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This study provides a finite element dynamic model for analyzing rolling bearing system vibration response. The vibration responses of polypropylene bearings with and without defects are studied using FE analysis and compared to experimental data. The viscoelastic behavior of thermoplastic is investigated in this work to evaluate the influence of material flexibility and damping viscosity. The vibrations are detected using 3D dynamic analysis. Peak vibrations are more noticeable in an inner ring defect than in an outer ring defect, according to test data. The performance of thermoplastic bearings is compared to that of metal parts using vibration signals. Both the test and numerical results show that Polypropylene bearings exhibit less vibration than steel counterparts. Unlike bearings made from metal, polypropylene bearings absorb vibrations and handle shaft misalignments. Following validation of the overall vibration spectrum data, Von Mises stresses inside the rings are assessed under high loads. Stress is significantly high under the balls, according to the simulation findings. For the test cases, the computational findings correspond closely to the experimental results.

Keywords: viscoelastic, FE analysis, polypropylene, bearings

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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

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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

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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

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Authors: Tamara Jimah, Priscilla Kehoe, Pamela Pimentel, Amir Rahmani, Nikil Dutt, Yuqing Guo

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Ensuring equitable access to maternal health care is critical for public health. Particularly for underserved women, community health workers (CHWs) have been invaluable in providing support through health education and strategies for improved maternal self-care management. Our research aimed to assess the acceptance of technology by CHWs and perinatal women to promote healthy pregnancy and postpartum wellness. This pilot study was conducted at a local community organization in Orange County, California, where CHWs play an important role in supporting low-income women through home visitations. Questionnaires were administered to 14 CHWs and 114 pregnant and postpartum women, literate in English and/or Spanish. CHWs tested two wearable devices (Galaxy watch and Oura ring) and shared their user experience, including potential reception by the perinatal women they served. In addition, perinatal women provided information on access to a smart phone and the internet, as well as their interest in using wearable devices to self-monitor personal health with guidance from a CHW. Over 85% of CHWs agreed that it was useful to track pregnancy with the smart watch and ring. The majority of perinatal women owned a smartphone (97.4%), had access to the internet (80%) and unlimited data plans (78%), expressed interest in using the smart wearable devices to self-monitor health, and were open to receiving guidance from a CHW (87%). Community health workers and perinatal women embraced the use of wearable technology to monitor maternal health. These preliminary findings have formed the basis of an ongoing research study that integrates CHW guidance and technology (i.e., smart watch, smart ring, and a mobile phone app) to promote self-efficacy and self-management among underserved perinatal women.

Keywords: community health workers, health promotion and education, health equity, maternal and child health, technology

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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

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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

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Authors: Binh T. T. Nguyen, Zhenyu Li, Eric Yap, Yi Zhang, Ai-Qun Liu

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Silicon-photonic-sensor system is an emerging class of analytical technologies that use evanescent field wave to sensitively measure the slight difference in the surrounding environment. The wavelength shift induced by local refractive index change is used as an indicator in the system. These devices can be served as sensors for a wide variety of chemical or biomolecular detection in clinical and environmental fields. In our study, a system including a silicon-based micro-ring resonator, microfluidic channel, and optical processing is designed, fabricated for biomolecule detection. The system is demonstrated to detect Clostridium botulinum type A neurotoxin (BoNT) in different water sources. BoNT is one of the most toxic substances known and relatively easily obtained from a cultured bacteria source. The toxin is extremely lethal with LD50 of about 0.1µg/70kg intravenously, 1µg/ 70 kg by inhalation, and 70µg/kg orally. These factors make botulinum neurotoxins primary candidates as bioterrorism or biothreat agents. It is required to have a sensing system which can detect BoNT in a short time, high sensitive and automatic. For BoNT detection, silicon-based micro-ring resonator is modified with a linker for the immobilization of the anti-botulinum capture antibody. The enzymatic reaction is employed to increase the signal hence gains sensitivity. As a result, a detection limit to 30 pg/mL is achieved by our silicon-photonic sensor within a short period of 80 min. The sensor also shows high specificity versus the other type of botulinum. In the future, by designing the multifunctional waveguide array with fully automatic control system, it is simple to simultaneously detect multi-biomaterials at a low concentration within a short period. The system has a great potential to apply for online, real-time and high sensitivity for the label-free bimolecular rapid detection.

Keywords: biotoxin, photonic, ring resonator, sensor

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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

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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: 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: 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: Nazar Hussain, Debaraj Mukherjee

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Carbohydrate-derived chiral intermediates which contain arrays of defined stereocenters have found enormous applications in organic synthesis due to their inherent functional group, stereochemical and structural diversities as well as their ready availability. Stereodiversity of these classes of molecules has motivated synthetic organic chemistry over the years. One major challenge is control of relative configuration during construction of acyclic fragments. Here, we show that The Diels Alder addition of arynes to appropriately substituted vinyl/aryl glycals followed by π-extension via pyran ring opening smoothly furnished meta-disubstituted fused aromatic cores containing a stereo-defined orthogonally protected chiral side chain. The method is broad in terms of aryl homologation affording benzene, naphthalene, and phenanthrene derivatives. Base-induced deprotonation followed by cleavage of the allylic C-O bond appears to be the crucial steps leading to the development of aromaticity, which is the driving force behind the annulative π-extension process. The present protocol can be used for the synthesis of meta-disubstituted naphthalene aldehydes and substrates for aldolases.

Keywords: vinyl/C-2 aryl glycal, arynes, cyclization, ring opening

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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

Procedia PDF Downloads 343

Authors: Yomna Ibrahim El-Gazzar, Hussien Ibrahim El-Subbagh, Hanan Hanaa Georgey, Ghada S. Hassan Hassan

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Dihydrofolate reductase (DHFR) is an enzyme that has pivotal importance in biochemistry and medicinal chemistry. It catalyzes the reduction of dihydrofolate to tetrahydrofolate and intimately couples with thymidylate synthase. Thymidylate synthase is a crucial enzyme that catalyzes the reductive methylation of (dUMP) to (dTMP) utilizing N5, N10-methylenetetrahydrofolate as a cofactor. A new series of 2-substituted thio-quinazolin-4-one analogs was designed that possessed electron withdrawing or donating functional groups (Cl or OCH3) at position 6- or 7-, 4-methoxyphenyl function at position 3-.The thiol function is used to connect to either 1,2,4-triazole, or 1,3,4-thiadiazole via a methylene bridge. Most of the functional groups designed to be accommodated on the quinazoline ring such as thioether, alkyl to increase lipid solubility of polar compounds, a character very much needed in the nonclassical DHFR inhibitors. The target compounds were verified with spectral data and elemental analysis. DHFR inhibitions, as well as antitumor activity, were applied on three cell lines (MCF-7, CACO-2, HEPG-2).

Keywords: nonclassical antifolates, DHFR Inhibitors, antitumor activity, quinazoline ring

Procedia PDF Downloads 394

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

Procedia PDF Downloads 151

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

Procedia PDF Downloads 173