Search results for: tree rings

Authors: Mohammad Syed Ali Molla, Mohammed Azim, Mohammad Esharuzzaman

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Centrifugal-casting machine is used in manufacturing special machine components like multi-layer journal bearing used in all internal combustion engine, steam, gas turbine and air craft turboengine where isotropic properties and high precisions are desired. Moreover, this machine can be used in manufacturing thin wall hightech machine components like cylinder liners and piston rings of IC engine and other machine parts like sleeves, and bushes. Heavy-duty machine component like railway wheel can also be prepared by centrifugal casting. A lot of technological developments are required in casting process for production of good casted machine body and machine parts. Usually defects like blowholes, surface roughness, chilled surface etc. are found in sand casted machine parts. But these can be removed by centrifugal casting machine using rotating metallic die. Moreover, die rotation, its temperature control, and good pouring practice can contribute to the quality of casting because of the fact that the soundness of a casting in large part depends upon how the metal enters into the mold or dies and solidifies. Poor pouring practice leads to variety of casting defects such as temperature loss, low quality casting, excessive turbulence, over pouring etc. Besides these, handling of molten metal is very unsecured and dangerous for the workers. In order to get rid of all these problems, the need of an automatic pouring device arises. In this research work, a robot assisted pouring device and a centrifugal casting machine are designed, developed constructed and tested experimentally which are found to work satisfactorily. The robot assisted pouring device is further modified and developed for using it in actual metal casting process. Lot of settings and tests are required to control the system and ultimately it can be used in automation of centrifugal casting machine to produce high-tech machine parts with desired precision.

Keywords: bearing, centrifugal casting, cylinder liners, robot

Procedia PDF Downloads 391

Authors: Ratun Rahman, Rafid Islam, Akin Ahmed, Kamrul Hasan, Hasan Mahmud

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Malware is becoming more prevalent, and several threat categories have risen dramatically in recent years. This paper provides a bird's-eye view of the world of malware analysis. The efficiency of five different machine learning methods (Naive Bayes, K-Nearest Neighbor, Decision Tree, Random Forest, and TensorFlow Decision Forest) combined with features picked from the retrieval of Android permissions to categorize applications as harmful or benign is investigated in this study. The test set consists of 1,168 samples (among these android applications, 602 are malware and 566 are benign applications), each consisting of 948 features (permissions). Using the permission-based dataset, the machine learning algorithms then produce accuracy rates above 80%, except the Naive Bayes Algorithm with 65% accuracy. Of the considered algorithms TensorFlow Decision Forest performed the best with an accuracy of 90%.

Keywords: android malware detection, machine learning, malware, malware analysis

Procedia PDF Downloads 133

Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

Procedia PDF Downloads 520

Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

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Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

Procedia PDF Downloads 394

Authors: Ploenpit Boochathum, Pitchayanad Kaolim, Phimjutha Srisangkaew

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In general, rubber foam is produced based on the sulfur curing system. However, the remaining sulfur in the rubber product waste is burned to sulfur dioxide gas causing the environment pollution. To avoid using sulfur as curing agent in the rubber foam products, this research work proposes non-sulfur curing system by using corn starch as a curing agent. The ether crosslinks were proposed to be produced via the functional bonding between hydroxyl groups of the starch molecules and chloroacetate groups added on the natural rubber molecules. The chloroacetated natural rubber (CNR) latex was prepared via the epoxidation reaction of the concentrated natural rubber latex, subsequently, epoxy rings were attacked by chloroacetic acid to produce hydroxyl groups and chloroacetate groups on the rubber molecules. Foaming agent namely NaHCO3 was selected to add in the CNR latex due to the low decomposition temperature at about 50°C. The appropriate curing temperature was assigned to be 90°C that is above gelatinization temperature; 60-70°C, of starch. The effect of weight ratio of starch, i.e., 0 phr, 3 phr and 5 phr, on the physical properties of CNR rubber foam was investigated. It was found that density reduced from 0.81 g/cm3 for 0 phr to 0.75 g/cm3 for 3 phr and 0.79 g/cm3 for 5 phr. The ability to return to its original thickness after prolonged compressive stresses of CNR rubber foam cured with starch loading of 5 phr was found to be considerably better than that of CNR rubber foam cured with starch 3 phr and CNR rubber foam without addition of starch according to the compression set that was determined to decrease from 66.67% to 40% and 26.67% with the increase loading of starch. The mechanical properties including tensile strength and modulus of CNR rubber foams cured using starch were determined to increase except that the elongation at break was found to decrease. In addition, all mechanical properties of CNR rubber foams cured with the starch 3 phr and 5 phr were found to be slightly different and drastically higher than those of CNR rubber foam without the addition of starch. This research work indicates that starch can be applicable as a curing agent for CNR rubber. This is confirmed by the increase of the elastic modulus (G') of CNR rubber foams that was cured with the starch over the CNR rubber foam without curing agent. This type of rubber foam is believed to be one of the biodegradable and environment-friendly product that can be cured at low temperature of 90°C.

Keywords: chloroacetated natural rubber, corn starch, non-sulfur curing system, rubber foam

Procedia PDF Downloads 286

Authors: Zainab Dahrouch, Beatrix Petrovičová, Claudia Triolo, Fabiola Pantò, Angela Malara, Salvatore Patanè, Maria Allegrini, Saveria Santangelo

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Synthetic dyes dispersed in water cause environmental damage and have harmful effects on human health. Methylene blue (MB) is broadly used as a dye in the textile, pharmaceutical, printing, cosmetics, leather, and food industries. The complete removal of MB is difficult due to the presence of aromatic rings in its structure. The present study is focused on electrospun nanofibers (NFs) with engineered architecture and surface to be used as catalysts for the photodegradation of MB. Ti and/or Zn oxide NFs are produced by electrospinning precursor solutions with different Ti: Zn molar ratios (from 0:1 to 1:0). Subsequent calcination and cooling steps are operated at fast rates to generate porous NFs with capture centers to reduce the recombination rate of the photogenerated charges. The comparative evaluation of the NFs as photocatalysts for the removal of MB from an aqueous solution with a dye concentration of 15 µM under UV irradiation shows that the binary (wurtzite ZnO and anatase TiO₂) oxides exhibit higher catalytic activity compared to ternary (ZnTiO₃ and Zn₂TiO₄) oxides. The higher band gap and lower crystallinity of the ternary oxides are responsible for their lower photocatalytic activity. It has been found that the optimal load for the wurtzite ZnO is 0.66 mg mL⁻¹, obtaining a degradation rate of 7.94.10⁻² min⁻¹. The optimal load for anatase TiO₂ is lower (0.33 mg mL⁻¹) and the corresponding rate constant (1.12×10⁻¹ min⁻¹) is higher. This finding (higher activity with lower load) is of crucial importance for the scaling up of the process on an industrial scale. Indeed, the anatase NFs outperform even the commonly used P25-TiO₂ benchmark. Besides, they can be reused twice without any regeneration treatment, with 5.2% and 18.7% activity decrease after second and third use, respectively. Thanks to the scalability of the electrospinning technique, this laboratory-scale study provides a perspective towards the sustainable large-scale manufacture of photocatalysts for the treatment of industry effluents.

Keywords: anatase, capture centers, methylene blue dye, nanofibers, photodegradation, zinc oxide

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Authors: J. Sahari, S. M. Sapuan

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Eco-friendly composites have been successfully prepared by using sugar palm tree as a sources. The effect of fibre content on mechanical properties of (SPF/SPS) biocomposites have been done and the experimentally tensile properties (tensile strength and modulus) of biocomposites have been compared with the existing theories of reinforcement. The biocomposites were prepared with different amounts of fibres (i.e. 10%, 20% and 30% by weight percent). The mechanical properties of plasticized SPS improved with the incorporation of fibres. Both approaches (experimental and theoretical) show that the young’s modulus of the biocomposites is consistently increased when the sugar palm fibre (SPF) are placed into the sugar palm starch matrix (SPS). Surface morphological study through scanning electron microscopy showed homogeneous distribution of fibres and matrix with good adhesion which play an important role in improving the mechanical properties of biocomposites. The observed deviations between the experimental and theoretical values are explained by the simplifying model assumptions applied for the configuration of the composites, in particular the sugar palm starch composites.

Keywords: eco-friendly, biocomposite, mechanical, experimental, theoretical

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Authors: Jessica M. Black

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

Keywords: methodology, natural science, social science, transdisciplinary

Procedia PDF Downloads 88

Authors: B. Chandra

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In Association Rule Mining, much attention has been paid for developing algorithms for large (frequent/closed/maximal) itemsets but very little attention has been paid to improve the performance of rule generation algorithms. Rule generation is an important part of Association Rule Mining. In this paper, a novel approach named NARG (Association Rule using Antecedent Support) has been proposed for rule generation that uses memory resident data structure named FCET (Frequent Closed Enumeration Tree) to find frequent/closed itemsets. In addition, the computational speed of NARG is enhanced by giving importance to the rules that have lower antecedent support. Comparative performance evaluation of NARG with fast association rule mining algorithm for rule generation has been done on synthetic datasets and real life datasets (taken from UCI Machine Learning Repository). Performance analysis shows that NARG is computationally faster in comparison to the existing algorithms for rule generation.

Keywords: knowledge discovery, association rule mining, antecedent support, rule generation

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Authors: Norah Mohammed Alshahrani, Abdulaziz Almaleh

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Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD and they are Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then Feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by the Support Vector Machine (SVM), achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: autism spectrum disorder, machine learning, feature selection, support vector machine

Procedia PDF Downloads 126

Authors: J. K. Kavitha, D. Manjula, U. Kanimozhi

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Mining frequent and high utility item sets have gained much significance in the recent years. When the data arrives sporadically, incremental and interactive rule mining and utility mining approaches can be adopted to handle user’s dynamic environmental needs and avoid redundancies, using previous data structures, and mining results. The dependence on recommendation systems has exponentially risen since the advent of search engines. This paper proposes a model for building a recommendation system that suggests frequent and high utility item sets over dynamic datasets for a cluster based location prediction strategy to predict user’s trajectories using the Efficient Incremental Rule Mining (EIRM) algorithm and the Fast Update Utility Pattern Tree (FUUP) algorithm. Through comprehensive evaluations by experiments, this scheme has shown to deliver excellent performance.

Keywords: data sets, recommendation system, utility item sets, frequent item sets mining

Procedia PDF Downloads 276

Authors: Aadityaa Singla

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The big five personality traits are as follows: openness, conscientiousness, extraversion, agreeableness, and neuroticism. In order to get an insight into their personality, many flocks to these categories, which each have different meanings/characteristics. This information is important not only to individuals but also to career professionals and psychologists who can use this information for candidate assessment or job recruitment. The links between AI and psychology have been well studied in cognitive science, but it is still a rather novel development. It is possible for various AI classification models to accurately predict a personality question via ten input questions. This would contrast with the hundred questions that normal humans have to answer to gain a complete picture of their five personality traits. In order to approach this problem, various AI classification models were used on a dataset to predict what a user may answer. From there, the model's prediction was compared to its actual response. Normally, there are five answer choices (a 20% chance of correct guess), and the models exceed that value to different degrees, proving their significance. By utilizing an MLP classifier, decision tree, linear model, and K-nearest neighbors, they were able to obtain a test accuracy of 86.643, 54.625, 47.875, and 52.125, respectively. These approaches display that there is potential in the future for more nuanced predictions to be made regarding personality.

Keywords: machine learning, personally, big five personality traits, cognitive science

Procedia PDF Downloads 123

Authors: Yaxiao Zhang, Yangzhou Chen

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This study investigate the formation problem for high-order continuous-time multi-robot with bounded symmetric time-varying delay protocol under switched directed communication topology. By using a linear transformation, the formation problem is transformed to stability analysis of a switched delay system. Under the assumption that each communication topology has a directed spanning tree, sufficient conditions are presented in terms of linear matrix inequalities (LMIs) that the multi-robot system can achieve a desired formation by the trade-off among the pre-exist topologies with the help of the scheme of average dwell time. A numeral example is presented to illustrate the effectiveness of the obtained results.

Keywords: multi-robot systems, formation, switched directed topology, symmetric time-varying delay, average dwell time, linear matrix inequalities (lmis)

Procedia PDF Downloads 512

Authors: Farrukh Shahzad

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The rich Islamic resources related to religious text, Islamic sciences, and history are widely available in print and in electronic format online. However, most of these works are only available in Arabic language. In this research, an attempt is made to utilize these resources to create interactive web applications in Arabic, English and other languages. The system utilizes the Pattern Recognition, Knowledge Management, Data Mining, Information Retrieval and Management, Indexing, storage and data-analysis techniques to parse, store, convert and manage the information from authentic Arabic resources. These interactive web Apps provide smart multi-lingual search, tree based search, on-demand information matching and linking. In this paper, we provide details of application architecture, design, implementation and technologies employed. We also presented the summary of web applications already developed. We have also included some screen shots from the corresponding web sites. These web applications provide an Innovative On-line Learning Systems (eLearning and computer based education).

Keywords: Islamic resources, Muslim scholars, hadith, narrators, history, fiqh

Procedia PDF Downloads 262

Authors: Sudeepti Dayal, Neelesh Gupta

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Image abbreviation is utilized for reducing the size of a file without demeaning the quality of the image to an objectionable level. The depletion in file size permits more images to be deposited in a given number of spaces. It also minimizes the time necessary for images to be transferred. Storage of medical images is a most researched area in the current scenario. To store a medical image, there are two parameters on which the image is divided, regions of interest and non-regions of interest. The best way to store an image is to compress it in such a way that no important information is lost. Compression can be done in two ways, namely lossy, and lossless compression. Under that, several compression algorithms are applied. In the paper, two algorithms are used which are, discrete cosine transform, applied to non-region of interest (lossy), and discrete wavelet transform, applied to regions of interest (lossless). The paper introduces SPIHT (set partitioning hierarchical tree) algorithm which is applied onto the wavelet transform to obtain good compression ratio from which an image can be stored efficiently.

Keywords: Compression ratio, DWT, SPIHT, DCT

Procedia PDF Downloads 329

Authors: L. S. Chathurika

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Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

Procedia PDF Downloads 104

Authors: Fengmei Li, Li Xu, Guoliang Xia

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Whey acidic proteins (WAP) domain-containing proteins in crustacean are involved in innate immune response against microbial invasion. In the present study, a novel double WAP domain (DWD)-containing protein gene 3 was identified from Chinese mitten crab Eriocheir sinensis (designated EsDWD3) by expressed sequence tag (EST) analysis and PCR techniques. The full-length cDNA of EsDWD3 was of 1223 bp, consisting of a 5′-terminal untranslated region (UTR) of 74 bp, a 3′ UTR of 727 bp with a polyadenylation signal sequence AATAAA and a polyA tail, and an open reading frame (ORF) of 423 bp. The ORF encoded a polypeptide of 140 amino acids with a signal peptide of 22 amino acids. The deduced protein sequence EsDWD3 showed 96.4 % amino acid similar to other reported EsDWD1 from E. sinensis, and phylogenetic tree analysis revealed that EsDWD3 had closer relationships with the reported two double WAP domain-containing proteins of E. sinensis species.

Keywords: Chinese mitten crab, Eriocheir sinensis, cloning, double WAP domain-containing protein

Procedia PDF Downloads 335

Authors: V. K. Srivastava

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Graphene has recently attracted an increasing attention in nanocomposites applications because it has 200 times greater strength than steel, making it the strongest material ever tested. Graphene, as the fundamental two-dimensional (2D) carbon structure with exceptionally high crystal and electronic quality, has emerged as a rapidly rising star in the field of material science. Graphene, as defined, as a 2D crystal, is composed of monolayers of carbon atoms arranged in a honeycombed network with six-membered rings, which is the interest of both theoretical and experimental researchers worldwide. The name comes from graphite and alkene. Graphite itself consists of many graphite-sheets stacked together by weak van der Waals forces. This is attributed to the monolayer of carbon atoms densely packed into honeycomb structure. Due to superior inherent properties of graphene nanoplatelets (GnP) over other nanofillers, GnP particles were added in epoxy resin with the variation of weight percentage. It is indicated that the DMA results of storage modulus, loss modulus and tan δ, defined as the ratio of elastic modulus and imaginary (loss) modulus versus temperature were affected with addition of GnP in the epoxy resin. In epoxy resin, damping (tan δ) is usually caused by movement of the molecular chain. The tan δ of the graphene nanoplatelets/epoxy resin composite is much lower than that of epoxy resin alone. This finding suggests that addition of graphene nanoplatelets effectively impedes movement of the molecular chain. The decrease in storage modulus can be interpreted by an increasing susceptibility to agglomeration, leading to less energy dissipation in the system under viscoelastic deformation. The results indicates the tan δ increased with the increase of temperature, which confirms that tan δ is associated with magnetic field strength. Also, the results show that the nanohardness increases with increase of elastic modulus marginally. GnP filled epoxy resin gives higher value than the epoxy resin, because GnP improves the mechanical properties of epoxy resin. Debonding of GnP is clearly observed in the micrograph having agglomeration of fillers and inhomogeneous distribution. Therefore, DMA and nanohardness studies indiacte that the elastic modulus of epoxy resin is increased with the addition of GnP fillers.

Keywords: agglomeration, elastic modulus, epoxy resin, graphene nanoplatelet, loss modulus, nanohardness, storage modulus

Procedia PDF Downloads 249

Authors: Siamak Dawazdah Emami, Amin Khodaei, Harith Bin Ahmad, Hairul A. Adbul-Rashid

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The Fano resonance effect on plasmonic nanoparticle materials results in such materials possessing a number of unique optical properties, and the potential applicability for sensing, nonlinear devices and slow-light devices. A Fano resonance is a consequence of coherent interference between superradiant and subradiant hybridized plasmon modes. Incident light on subradiant modes will initiate excitation that results in superradiant modes, and these superradient modes possess zero or finite dipole moments alongside a comparable negligible coupling with light. This research work details the derivation of an electrodynamics coupling model for the interaction of dipolar transitions and radiation via plasmonic nanoclusters such as quadrimers, pentamers and heptamers. The directivity calculation is analyzed in order to qualify the redirection of emission. The geometry of a configured array of nanostructures strongly influenced the transmission and reflection properties, which subsequently resulted in the directivity of each antenna being related to the nanosphere size and gap distances between the nanospheres in each model’s structure. A well-separated configuration of nanospheres resulted in the structure behaving similarly to monomers, with spectra peaks of a broad superradiant mode being centered within the vicinity of 560 nm wavelength. Reducing the distance between ring nanospheres in pentamers and heptamers to 20~60 nm caused the coupling factor and charge distributions to increase and invoke a subradiant mode centered within the vicinity of 690 nm. Increasing the outside ring’s nanosphere distance from the centered nanospheres caused the coupling factor to decrease, with the coupling factor being inversely proportional to cubic of the distance between nanospheres. This phenomenon led to a dramatic decrease of the superradiant mode at a 200 nm distance between the central nanosphere and outer rings. Effects from a superradiant mode vanished beyond a 240 nm distance between central and outer ring nanospheres.

Keywords: fano resonance, optical antenna, plasmonic, nano-clusters

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Authors: Mais Khader, Xingjie Wei

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This research sheds light on female entrepreneurs by providing new insights on the survival predictions of companies led by females in the UK. This study aims to build a predictive machine learning model of the survival of female-led & co-led small & medium enterprises (SMEs) in the UK over the period 2000-2020. The predictive model built utilised a combination of financial and non-financial features related to both companies and their directors to predict SMEs' survival. These features were studied in terms of their contribution to the resultant predictive model. Five machine learning models are used in the modelling: Decision tree, AdaBoost, Naïve Bayes, Logistic regression and SVM. The AdaBoost model had the highest performance of the five models, with an accuracy of 73% and an AUC of 80%. The results show high feature importance in predicting companies' survival for company size, management experience, financial performance, industry, region, and females' percentage in management.

Keywords: company survival, entrepreneurship, females, machine learning, SMEs

Procedia PDF Downloads 72

Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, ECG, ResNet, sliding window

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Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, S. Meenakshi Sundaram

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Alzheimer’s disease is one of the prevalent kind of ailment, expected for impudent reconciliation or an effectual therapy is to be accredited hitherto. Probable detonation of patients in the upcoming years, and consequently an enormous deal of apprehension in early discovery of the disorder, this will conceivably chaperon to enhanced healing outcomes. Complex impetuosity of the brain is an observant symbolic of the disease and a unique recognition of genetic sign of the disease. Machine learning alongside deep learning and decision tree reinforces the aptitude to absorb characteristics from multi-dimensional data’s and thus simplifies automatic classification of Alzheimer’s disease. Susceptible testing was prophesied and realized in training the prospect of Alzheimer’s disease classification built on machine learning advances. It was shrewd that the decision trees trained with deep neural network fashioned the excellent results parallel to related pattern classification.

Keywords: Alzheimer's diagnosis, decision trees, deep neural network, machine learning, pattern classification

Procedia PDF Downloads 279

Authors: Itai Kohavi, Wojciech Nowiak

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This article explores the stagnation of the Israeli -Palestinian peace negotiation process, and the reasons behind the failure of more than 12 international initiatives to resolve the conflict. Twenty-seven top members of the Israeli national security elite (INSE) were interviewed, including heads of the negotiation teams, the National Security Council, the Mossad, and other intelligence and planning arms. The interviewees provided their insights on the Israeli challenges in reaching a sustainable and stable peace agreement and in dealing with the international pressure on Israel to negotiate a peace agreement while preventing anti-Israeli UN decisions and sanctions. The findings revealed a decision tree, with red herring deception strategies implemented to postpone the negotiation process and to delay major decisions during the negotiation process. Beyond the possible applications for the Israeli – Palestinian conflict, the findings shed more light on the phenomenon of rational deception of allies in a negotiation process, a subject less frequently researched as compared with deception of rivals.

Keywords: deception, Israeli-Palestinian conflict, negotiation, red herring, terrorist state, treadmill negotiation

Procedia PDF Downloads 281

Authors: Sameer Ansari, Suhas Nitsure

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Biomimicry has made a big move into the built environment by adapting nature's solutions to human designs and inventions. We can examine numerous aspects of the built environment right from generating energy, fed by rainwater and powered by sun to over all land use impacts. This paper discusses the potential of a man made building which will work for the welfare of humans and reduce the impact of the harmful environment on us which we ourselves created for us. Building services inspired by nature such as building walls from homeostasis in organisms, natural ventilation from termites, artificial aggregates from natural aggregates, solar panels from photosynthesis and building structure itself compared to tree as a cantilever. Environmental services such as using CO2 as a feedstock for construction related activities, using Ornilux glasses and  saving birds from collision with buildings, using prefabricated steel for fast building members- save time and also negligible waste as no formwork is used. Maglev inspired car elevators in building which is unique and giving all together new direction to technology.

Keywords: biomimicry, green technology, maglev car elevator, civil engineering

Procedia PDF Downloads 558

Authors: Ibrahim Ilker Ozyigit, Ertugrul Filiz, Seda Birbilener, Semsettin Kulac, Zeki Severoglu

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In this study, we have performed genetic diversity analysis of Tilia tomentosa genotypes by using randomly amplified polymorphic DNA (RAPD) primers. A total of 28 genotypes, including 25 members from the urban ecosystem and 3 genotypes from forest ecosystem as outgroup were used. 8 RAPD primers produced a total of 53 bands, of which 48 (90.6 %) were polymorphic. Percentage of polymorphic loci (P), observed number of alleles (Na), effective number of alleles (Ne), Nei's (1973) gene diversity (h), and Shannon's information index (I) were found as 94.29 %, 1.94, 1.60, 0.34, and 0.50, respectively. The unweighted pair-group method with arithmetic average (UPGMA) cluster analysis revealed that two major groups were observed. The genotypes of urban and forest ecosystems showed a high genetic similarity between 28% and 92% and these genotypes did not separate from each other in UPGMA tree. Also, urban and forest genotypes clustered together in principal component analysis (PCA).

Keywords: Tilia tomentosa, genetic diversity, urban ecosystem, RAPD, UPGMA

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Authors: M. Mahdavi, M. Mohseni, R. Rafiei, H. Yari

Abstract:

Environmental factors can deteriorate the automotive coatings significantly. Such as UV radiations, humidity, hot-cold shock and destructive chemical compounds. Furthermore, some natural materials such as bird droppings and tree gums have the potential to degrade the coatings as well. The present work aims to study the mechanism of degradation for two automotive refinish coating (PU based) systems exposed to two types of biological materials, i.e. Arabic gum and the simulated bird dropping, pancreatin. To reach this goal, effects of these biological materials on surface properties and appearance were studied using different techniques including digital camera, FT-IR spectroscopy, optical microscopy, and gloss measurements. In addition, the thermo-mechanical behavior of coatings was examined by DMTA. It was found that cross-linking had a crucial role on the biological resistance of clear coat. The higher cross-linking enhanced biological resistance.

Keywords: refinish clear coat, pancreatin, Arabic gum, cross-linking, biological degradation

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Authors: Lakhdari Wassima, Ouffroukh Ammar, Dahliz Abderrahmène, Soud Adila, Hammi Hamida, M’lik Randa

Abstract:

Prospected palm groves of Oued Righ regions (Ouargla, Algeria) allowed us to observe sudden death of palm trees aged between 05 and 70 years. Field examinations revealed abnormal clinical signs with sometimes a quick death of affected trees. Entomologic investigations have confirmed the absence of phytophagous insects on dead trees. Further investigations by questioning farmers on the global management of palm groves visited (Irrigation, water quality used, soil type, etc.) did not establish any relationship between these aspects and the death of palm trees, which naturally pushed us to focus our investigations for research on fungal pathogens. Thus, laboratory studies were conducted to know the real causes of this phenomenon, 13 fungi were found on different parts of the dead palm trees. The flowing fungal types were identified: 1-Diplodia phoenicum, 2-Theilaviopsis paradoxa, 3-Phytophthora sp, 4-Helminthosporium sp, 5-Stemphylium botryosum, 6-Alternaria sp, 7-Aspergillus niger, 8-Aspergillus sp.

Keywords: palm tree, death, fungal pathogens, Oued Righ

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Authors: Tak-Chung Fu, Ying-Kit Hung, Fu-Lai Chung

Abstract:

In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is first introduced in 2001. This process originally works for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful cases, it is worth to further explore the opportunity to apply PIP in time series ‘Big Data’. However, the performance of PIP identification is always considered as the limitation when dealing with ‘Big’ time series data. In this paper, two distributed versions of PIP identification based on the Specialized Binary (SB) Tree are proposed. The proposed approaches solve the bottleneck when running the PIP identification process in a standalone computer. Improvement in term of speed is obtained by the distributed versions.

Keywords: distributed computing, performance analysis, Perceptually Important Point identification, time series data mining

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Authors: Yasser M. Saad, Amr A. El Hanafy, Saleh A. Alkarim, Hussein A. Almehdar, Elrashdy M. Redwan

Abstract:

Camels are substantial providers of transport, milk, sport, meat, shelter, security and capital in many countries, particularly in Saudi Arabia. Inter simple sequence repeat technique was used to detect the genetic variations among some camel breeds (Majaheim, Safra, Wadah, and Hamara). Actual number of alleles, effective number of alleles, gene diversity, Shannon’s information index and polymorphic bands were calculated for each evaluated camel breed. Neighbor-joining tree that re-constructed for evaluated these camel breeds showed that, Hamara breed is distantly related from the other evaluated camels. In addition, the polymorphic sites, haplotypes and nucleotide diversity were identified for some camelidae cox1 gene sequences (obtained from NCBI). The distance value between C. bactrianus and C. dromedarius (0.072) was relatively low. Analysis of genetic diversity is an important way for conserving Camelus dromedarius genetic resources.

Keywords: camel, genetics, ISSR, neighbor-joining

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Authors: Hayal Akyildirim Beğen, Özgür Emi̇nağaoğlu

Abstract:

DNA barcoding is the method of description of species based on gene diversity. In current studies, registration, genetic identification and protection of especially endemic plants pecies are carried out by DNA barcoding techniques. Molecular studies are based on the amplification and sequencing of the barcode gene region by the PCR method. Endemic Campanula choruhensis Kit Tan & Sorger, Campanula troegera Damboldt and Campanula betulifolia K.Koch is widespread in Artvin, Erzurum and around Çoruh valley passing through it. Intense road and dam constructions are carried out in and around the distribution area of this species. This situation harms the habitat of the species and puts its extinction. In this study, the plastid matK barcode gene regions (650 bp) of three Campanula species were created. To make the identification of this species quickly and accurately, gene sequence compared with sequences of other Campanula L. species. As a result of phylogenetic analysis, C. choruhensis is close relative to C. betulifolia. Morphologically, these species were determined to be more similar to each other with flower and leaf characters. C. troegera formed a separate branch.

Keywords: campanula, DNA barcoding, endemic, türkiye, artvin

Procedia PDF Downloads 52