Search results for: finite state machine

Authors: Yeonjune Kang

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A triangulation of a planar region is a partition of that region into triangles. In the finite element method, triangulations are often used as the grid underlying a computation. In order to be suitable as a finite element mesh, a triangulation must have well-shaped triangles, according to criteria that depend on the details of the particular problem. For instance, most methods require that all triangles be small and as close to the equilateral shape as possible. Stated differently, one wants to avoid having either thin or flat triangles in the triangulation. There are many triangulation procedures, a particular one being the one known as the longest edge bisection algorithm described below. Starting with a given triangle, locate the midpoint of the longest edge and join it to the opposite vertex of the triangle. Two smaller triangles are formed; apply the same bisection procedure to each of these triangles. Continuing in this manner after n steps one obtains a triangulation of the initial triangle into 2n smaller triangles. The longest edge algorithm was first considered in the late 70’s. It was shown by various authors that this triangulation has the desirable properties for the finite element method: independently of the number of iterations the angles of these triangles cannot get too small; moreover, the size of the triangles decays exponentially. In the present paper we consider a related triangulation algorithm we refer to as the largest angle bisection procedure. As the name suggests, rather than bisecting the longest edge, at each step we bisect the largest angle. We study the properties of the resulting triangulation and prove that, while the general behavior resembles the one in the longest edge bisection algorithm, there are several notable differences as well.

Keywords: angle bisectors, geometry, triangulation, applied mathematics

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Authors: Sh. Mirhosseini, Y. Haghighatfar, M. Sedighi

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Forced vibration problem of a delaminated beam made of fiber metal laminates is studied in this paper. Firstly, a delamination is considered to divide the beam into four sections. The classic beam theory is assumed to dominate each section. The layers on two sides of the delamination are constrained to have the same deflection. This hypothesis approves the conditions of compatibility as well. Consequently, dynamic response of the beam is obtained by the means of differential transform method (DTM). In order to verify the correctness of the results, a model is constructed using commercial software ABAQUS 6.14. A linear spring with constant stiffness takes the effect of contact between delaminated layers into account. The attained semi-analytical outcomes are in great agreement with finite element analysis.

Keywords: delamination, forced vibration, finite element modelling, natural frequency

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Authors: Abed Ahmed, Mehrdad Asadi, Jennifer Martay

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Stainless steel pipelines are crucial components to transportation and storage in the oil and gas industry. However, the rise of random attacks and vandalism on these pipes for their valuable transport has led to more security and protection for incoming surface impacts. These surface impacts can lead to large global deformations of the pipe and place the pipe under strain, causing the eventual failure of the pipeline. Therefore, understanding how these surface impact loads affect the pipes is vital to improving the pipes’ security and protection. In this study, experimental test and finite element analysis (FEA) have been carried out on EN3B stainless steel specimens to study the impact behaviour. Low velocity impact tests at 9 m/s with 16 kg dome impactor was used to simulate for high momentum impact for localised failure. FEA models of clamped and deformable boundaries were modelled to study the effect of the boundaries on the pipes impact behaviour on its impact resistance, using experimental and FEA approach. Comparison of experimental and FE simulation shows good correlation to the deformable boundaries in order to validate the robustness of the FE model to be implemented in pipe models with complex anisotropic structure.

Keywords: dynamic impact, deformable boundary conditions, finite element modelling, LS-DYNA, stainless steel pipe

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Authors: Kemal Polat

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In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

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Authors: Adegbite Bashiru Adeniyi, Olaniyi Akeem Olawale, Ayobamidele Sinatu Juliet

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The study was carried out in the 6 South Western states of Nigeria to analyze socio-economic characteristic of garri processors and their anthropometric qualities with respect to modern technologies used in garri processing. About 20 respondents were randomly selected from each of the 6 workstations purposively considered for the study due to their daily processing activities already attracted high patronage of customers. These include Oguntolu village (Ogun State), Igoba-Akure (Ondo State), Imo-Ilesa (Osun State), Odo Oba-Ileri (Oyo State), Irasa village (Ekiti State) and Epe in Lagos state. Interview schedule was conducted for 120 respondents to elicit information. Data were analyzed using descriptive statistical tools. It was observed from the findings that respondents were in their most productive age range (36-45 years) except Ogun state where majority (45%) were relatively older than 45 years. A fewer processors were much younger than 26 years old. It furthers revealed that not less than 55% have body weight greater than 50.0 kilogram, also not less than 70% were taller than 1.5 meter. So also, the hand length and hand thickness of the majority were long and bulky which are considered suitable for operating some modern and improved technologies in garri-frying process. This information could be used by various technological developers to enhance production of modern equipment and tools for a greater efficiency.

Keywords: agro-business, anthromorphic, modernization, proficiency

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Authors: Abubakar Rashid, Muhammad Saad, Faheem Ahmed

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This paper provides a comprehensive study pertaining to diagnosis of intermittent high vibrations on an industrial gas turbine using detailed vibrations analysis, followed by its rectification. Engro Polymer & Chemicals Limited, a Chlor-Vinyl complex located in Pakistan has a captive combined cycle power plant having two 28 MW gas turbines (make Hitachi) & one 15 MW steam turbine. In 2018, the organization faced an issue of high vibrations on one of the gas turbines. These high vibration peaks appeared intermittently on both compressor’s drive end (DE) & turbine’s non-drive end (NDE) bearing. The amplitude of high vibration peaks was between 150-170% on the DE bearing & 200-300% on the NDE bearing from baseline values. In one of these episodes, the gas turbine got tripped on “High Vibrations Trip” logic actuated at 155µm. Limited instrumentation is available on the machine, which is monitored with GE Bently Nevada 3300 system having two proximity probes installed at Turbine NDE, Compressor DE &at Generator DE & NDE bearings. Machine’s transient ramp-up & steady state data was collected using ADRE SXP & DSPI 408. Since only 01 key phasor is installed at Turbine high speed shaft, a derived drive key phasor was configured in ADRE to obtain low speed shaft rpm required for data analysis. By analyzing the Bode plots, Shaft center line plot, Polar plot & orbit plots; rubbing was evident on Turbine’s NDE along with increased bearing clearance of Turbine’s NDE radial bearing. The subject bearing was then inspected & heavy deposition of carbonized coke was found on the labyrinth seals of bearing housing with clear rubbing marks on shaft & housing covering at 20-25 degrees on the inner radius of labyrinth seals. The collected coke sample was tested in laboratory & found to be the residue of lube oil in the bearing housing. After detailed inspection & cleaning of shaft journal area & bearing housing, new radial bearing was installed. Before assembling the bearing housing, cleaning of bearing cooling & sealing air lines was also carried out as inadequate flow of cooling & sealing air can accelerate coke formation in bearing housing. The machine was then taken back online & data was collected again using ADRE SXP & DSPI 408 for health analysis. The vibrations were found in acceptable zone as per ISO standard 7919-3 while all other parameters were also within vendor defined range. As a learning from subject case, revised operating & maintenance regime has also been proposed to enhance machine’s reliability.

Keywords: ADRE, bearing, gas turbine, GE Bently Nevada, Hitachi, vibration

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Authors: Jeron Maheswaran, Sudath C. Siriwardane

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In fatigue analysis, the structural detail of tubular joint has taken great attention among engineers. The DNV-RP-C203 is covering this topic quite well for simple and clear joint cases. For complex joint and geometry, where joint classification isn’t available and limitation on validity range of non-dimensional geometric parameters, the challenges become a fact among engineers. The classification of joint is important to carry out through the fatigue analysis. These joint configurations are identified by the connectivity and the load distribution of tubular joints. To overcome these problems to some extent, this paper compare the fatigue life of tubular joints in offshore jacket according to the stress concentration factors (SCF) in DNV-RP-C203 and finite element method employed Abaqus/CAE. The paper presents the geometric details, material properties and considered load history of the jacket structure. Describe the global structural analysis and identification of critical tubular joints for fatigue life estimation. Hence fatigue life is determined based on the guidelines provided by design codes. Fatigue analysis of tubular joints is conducted using finite element employed Abaqus/CAE [4] as next major step. Finally, obtained SCFs and fatigue lives are compared and their significances are discussed.

Keywords: fatigue life, stress-concentration factor, finite element analysis, offshore jacket structure

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: biomedical data, learning, classifier, algorithms decision tree, knowledge extraction

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Authors: M. Khaled Abduesslam, Mohammed Ali, Basher H. Alsdai, Muhammad Nizam Inayati

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This paper presents the voltage problem location classification using performance of Least Squares Support Vector Machine (LS-SVM) and Learning Vector Quantization (LVQ) in electrical power system for proper voltage problem location implemented by IEEE 39 bus New-England. The data was collected from the time domain simulation by using Power System Analysis Toolbox (PSAT). Outputs from simulation data such as voltage, phase angle, real power and reactive power were taken as input to estimate voltage stability at particular buses based on Power Transfer Stability Index (PTSI).The simulation data was carried out on the IEEE 39 bus test system by considering load bus increased on the system. To verify of the proposed LS-SVM its performance was compared to Learning Vector Quantization (LVQ). The results showed that LS-SVM is faster and better as compared to LVQ. The results also demonstrated that the LS-SVM was estimated by 0% misclassification whereas LVQ had 7.69% misclassification.

Keywords: IEEE 39 bus, least squares support vector machine, learning vector quantization, voltage collapse

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Authors: Gozel Judakova, Markus Bause

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We present and analyze reliable numerical techniques for simulating complex flow and transport phenomena related to natural gas transportation in pipelines. Such kind of problems are of high interest in the field of petroleum and environmental engineering. Modeling and understanding natural gas flow and transformation processes during transportation is important for the sake of physical realism and the design and operation of pipeline systems. In our approach a two fluid flow model based on a system of coupled hyperbolic conservation laws is considered for describing natural gas flow undergoing hydratization. The accurate numerical approximation of two-phase gas flow remains subject of strong interest in the scientific community. Such hyperbolic problems are characterized by solutions with steep gradients or discontinuities, and their approximation by standard finite element techniques typically gives rise to spurious oscillations and numerical artefacts. Recently, stabilized and discontinuous Galerkin finite element techniques have attracted researchers’ interest. They are highly adapted to the hyperbolic nature of our two-phase flow model. In the presentation a streamline upwind Petrov-Galerkin approach and a discontinuous Galerkin finite element method for the numerical approximation of our flow model of two coupled systems of Euler equations are presented. Then the efficiency and reliability of stabilized continuous and discontinous finite element methods for the approximation is carefully analyzed and the potential of the either classes of numerical schemes is investigated. In particular, standard benchmark problems of two-phase flow like the shock tube problem are used for the comparative numerical study.

Keywords: discontinuous Galerkin method, Euler system, inviscid two-fluid model, streamline upwind Petrov-Galerkin method, twophase flow

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Authors: Tamas Mankovits

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Lightweight cellular-type structures like metal foams have excellent mechanical properties, therefore the interest in these materials is widely spreading as load-bearing structural elements, e.g. as implants. Numerous technologies are available to produce metal foams. In this paper the material response of closed cell foam structures produced by direct foaming and additive technology is compared. The production technology circumstances are also investigated. Geometrical variations are developed for foam structures produced by additive manufacturing and simulated by finite element method to be able to predict the mechanical behavior.

Keywords: additive manufacturing, direct foaming, finite element method, metal foam

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Authors: Olubusayo Asikhia

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The purpose of the study was to compare the incidence of indiscipline and guidance and counseling programmes among federal and state-owned tertiary institutions in Lagos State, Southwest, Nigeria. This study was borne out of persistent students’ revolt and unrest in Nigerian tertiary institutions with its concomitant breakdown of law and order, disruption of academic activities, closure of institutions and the disruption of lives and property among others. A sample of 300 students, 270 staff (including academic and non-academic, heads of disciplinary committees, deans of student affairs and counselors) from 6 federal and state-owned tertiary institutions were selected through stratified random sampling. Three structured questionnaires with reliability coefficients that range from 0.78 to 0.91 were used to collect data for the study. The data were tested at 0.05 level of significance and analyzed using descriptive, paired samples statistics and regression analysis. Findings from the four hypotheses generated for the study revealed that student indiscipline occurs more in state colleges of education and polytechnics than in their federal counterparts. It was also higher in federal universities than the state. The study also revealed a significant difference in the causes of indiscipline and assessment of guidance and counseling programmes in state and federal universities and no significant difference in state and federal colleges and polytechnics. It was recommended that the guidance and counseling department in tertiary institutions should create a well-structured program that will enable it to address issues of indiscipline among students and that counselors should use small-group counseling approach and student-focused interventions.

Keywords: colleges of education, guidance and counseling, indiscipline, polytechnics, students and universities

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Authors: Jean Marie Tshimula, Atsushi Togashi

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We develop a data-science approach for providing an interactive visualization and predictive models to find insights into the projects' historical data in order for stakeholders understand some unseen opportunities in the African market that might escape them behind the online project portfolio of the African Development Bank. This machine learning-based web application identifies the market trend of the fastest growing economies across the continent as well skyrocketing sectors which have a significant impact on the future of business in Africa. Owing to this, the approach is tailored to predict where the investment needs are the most required. Moreover, we create a corpus that includes the descriptions of over more than 1,200 projects that approximately cover 14 sectors designed for some of 53 African countries. Then, we sift out this large amount of semi-structured data for extracting tiny details susceptible to contain some directions to follow. In the light of the foregoing, we have applied the combination of Latent Dirichlet Allocation and Random Forests at the level of the analysis module of our methodology to highlight the most relevant topics that investors may focus on for investing in Africa.

Keywords: machine learning, topic modeling, natural language processing, big data

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Authors: D. B. Gohil

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Closed die forging is a very complex process, and measurement of actual forces for real material is difficult and time consuming. Hence, the modelling technique has taken the advantage of carrying out the experimentation with the proper model material which needs lesser forces and relatively low temperature. The results of experiments on the model material then may be correlated with the actual material by using the theory of similarity. There are several methods available to resolve the complexity involved in the closed die forging process. Finite Element Method (FEM) and Finite Difference Method (FDM) are relatively difficult as compared to the slab method. The slab method is very popular and very widely used by the people working on shop floor because it is relatively easy to apply and reasonably accurate for most of the common forging load requirement computations.

Keywords: experimentation, forging, process modeling, strain distribution

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Authors: Emna Bahri Hammami, Zammouri Mounira, Tarhouni Jamila

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The Nadhour Saouaf Sisseb El Alem (NSSA) system comprises some of the most intensively exploited aquifers in central Tunisia. Since the 1970s, the growth in economic productivity linked to intensive agriculture in this semiarid region has been sustained by increasing pumping rates of the system’s groundwater. Exploitation of these aquifers has increased rapidly, ultimately causing their depletion. With the aim to better understand the behavior of the aquifer system and to predict its evolution, the paper presents a finite difference model of the groundwater flow and solute transport. The model is based on the Groundwater Modeling System (GMS) and was calibrated using data from 1970 to 2010. Groundwater levels observed in 1970 were used for the steady-state calibration. Groundwater levels observed from 1971 to 2010 served to calibrate the transient state. The impact of pumping discharge on the evolution of groundwater levels was studied through three hypothetical pumping scenarios. The first two scenarios replicated the approximate drawdown in the aquifer heads (about 17 m in scenario 1 and 23 m in scenario 2 in the center of NSSA) following an increase in pumping rates by 30% and 50% from their current values, respectively. In addition, pumping was stopped in the third scenario, which could increase groundwater reserves by about 7 Mm3/year. NSSA groundwater reserves could be improved considerably if the pumping rules were taken seriously.

Keywords: pumping, depletion, groundwater modeling system GMS, Nadhour Saouaf

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Authors: Xu Jiaqiao

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Text sentiment analysis is an important branch of natural language processing. This technology is widely used in public opinion analysis and web surfing recommendations. At present, the mainstream sentiment analysis methods include three parts: sentiment analysis based on a sentiment dictionary, based on traditional machine learning, and based on deep learning. This paper mainly analyzes and compares the advantages and disadvantages of the SVM method of traditional machine learning and the Long Short-term Memory (LSTM) method of deep learning in the field of Chinese sentiment analysis, using Chinese comments on Sina Microblog as the data set. Firstly, this paper classifies and adds labels to the original comment dataset obtained by the web crawler, and then uses Jieba word segmentation to classify the original dataset and remove stop words. After that, this paper extracts text feature vectors and builds document word vectors to facilitate the training of the model. Finally, SVM and LSTM models are trained respectively. After accuracy calculation, it can be obtained that the accuracy of the LSTM model is 85.80%, while the accuracy of SVM is 91.07%. But at the same time, LSTM operation only needs 2.57 seconds, SVM model needs 6.06 seconds. Therefore, this paper concludes that: compared with the SVM model, the LSTM model is worse in accuracy but faster in processing speed.

Keywords: sentiment analysis, support vector machine, long short-term memory, Chinese microblog comments

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Authors: B. Ramesh Naik, T. Venugopal

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This paper presents a novel approach which improves the high-level semantics of images based on machine learning approach. The contemporary approaches for image retrieval and object recognition includes Fourier transforms, Wavelets, SIFT and HoG. Though these descriptors helpful in a wide range of applications, they exploit zero order statistics, and this lacks high descriptiveness of image features. These descriptors usually take benefit of primitive visual features such as shape, color, texture and spatial locations to describe images. These features do not adequate to describe high-level semantics of the images. This leads to a gap in semantic content caused to unacceptable performance in image retrieval system. A novel method has been proposed referred as discriminative learning which is derived from machine learning approach that efficiently discriminates image features. The analysis and results of proposed approach were validated thoroughly on WANG and Caltech-101 Databases. The results proved that this approach is very competitive in content-based image retrieval.

Keywords: CBIR, discriminative learning, region weight learning, scale invariant feature transforms

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Authors: Aziza Tahar Djebbar

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Throughout American history, many American presidents have tried to create new reforms to enhance the living conditions of the American citizens and promote their welfare. Among these reforms were those dealing with health care, education, as well as social security, which would facilitate accordingly the evolution of the American welfare state. After the New Deal, from 1945 to 1969, American presidents sought to carry out and enlarge the scope of the welfare state that emerged during the Roosevelt Administration by introducing new social reforms. Yet, there were some American presidents who succeeded, and there were some presidents who failed.The task of this research work is to depict the postwar period from 1945 to 1969. Some light will be shed on the main causes that led to the delay of many programs from 1945 to 1960. Further, the focus will be on the main factors that contributed to the reappearance of many social reforms and the dramatic expansion of the welfare state all along the 1960s.

Keywords: new deal, great society, medicaid, medicare, war on poverty, social reforms, welfare reforms

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Authors: Akbar Jamali

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According to Hegel’s philosophy, constitution of any given nation is the best expression of its national Self-Consciousness. Since constitution is the place in which freedom and Universal Rights is expressed, and since the essence of Self-consciousness is freedom, the development of self-consciousness and consequently freedom, is the direct cause of the development of constitution. Self-consciousness develops in the human history according to its own internal and external dialectic; therefore, it is essentially a dynamic phenomenon. However, constitution is supposed to be a stable foundation for the legal system of state and society. Therefore, the dilemma is: how the dynamic and contradictory nature of Self-Consciousness is the foundation of constitution that supposed to be the stable base of legal system of state and society. According to Hegel’s philosophy, the contradiction between the dynamic self- consciousness and the static constitution and state has an essential role in the formation of social movements within any given state. Self-consciousness is the phenomenology of Spirit in the human history. Subjective Spirit expresses itself in the different shapes of Self-consciousness in human spirit. These different shapes of self-consciousness must be identical with its contradiction; Objective Spirit. State is the highest form of the objective Spirit. Therefore, state and its foundation namely ‘constitution’ must be identical with Self-consciousness. "Spirit cannot remain forever alienated from its expression." Hegel states. Self-consciousness is the Subjective Spirit, it freely develops according to its internal and external contradictions, but since it must be always identical with its expression namely constitution, its development results to alienation. They way by which self-consciousness became again identical with the constitution determines the nature of legal and political development of any given society and state. In the democratic states, self-consciousness shows itself partially in the public opinion. In the process of election, this public opinion changes the ruling parties that construct the government. In democracies, self-consciousness or subjective spirit is in a dialectical relationship with state or the Objective Spirit. Therefore, it cannot remain alienated with its expression that is political system and its constitution. But, in the autocracies Self-consciousness cannot easily express itself in the government and its constitution. More Self-consciousness develops more it becomes alienated with its expression that is the state and its constitution. Rebel and revolution are the symptom of alienation of Spirit (self-consciousness) with its expression (state and its constitution).

Keywords: alienation, constitution, self-consciousness, spirit

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Authors: Temitope Francis Abiodun

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The practice of intelligence is not limited to the machinery of a nation state alone, yet not much research or analysis has been directed towards the spy-crafts and tradecrafts engaged in by violent non-state actors (VNSAs) in the international community. The rise of 'private sector intelligence' in more recent years has only just begun to be interrogated by practitioners and academics. However, the use of intelligence by insurgents and other groups assembled to achieve varied forms of politico-military outcomes has often been overlooked. This paper examined the factors and conditions that gave rise to an increase in violent non-state actors (VNSAs), strategies aiding their deployment of insurgent intelligence, and as well the implications of their activities on global security. The failed state theory was adopted, while a descriptive research design served as the framework for the study. Data were collected from primary and secondary sources. The paper, however, revealed there were massive deployments of insurgent intelligence by violent non-state actors in contrast to a faulty pre-conception that insurgents were not as highly trained in deployment of intelligence as state actors, having assumed that the VNSAs lacked the sophistication to produce intelligence. However, the strategic objectives of insurgents (VNSAs) were revealed to depend on well-organized information gathering operations that feed into the tactical executions of their insurgency. The paper recommends, therefore, there is a need for adequate training on the part of security personnel in the states to be alive to their responsibilities; and there is also a need to ensure adequate border control and management to checkmate the influx of the various violent or deadly movements across global frontiers.

Keywords: terrorism, non-violent state actors, private sector intelligence, security

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Authors: Ayomide Oyedele

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In the dynamic landscape of cybersecurity, "Strategic Cyber Sentinel" emerges as a revolutionary framework, transcending traditional approaches. This paper pioneers a holistic strategy, weaving together threat intelligence, machine learning, and adaptive defenses. Through meticulous real-world simulations, we demonstrate the unprecedented resilience of our framework against evolving cyber threats. "Strategic Cyber Sentinel" redefines proactive threat mitigation, offering a robust defense architecture poised for the challenges of tomorrow.

Keywords: cybersecurity, resilience, threat intelligence, machine learning, adaptive defenses

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Authors: Dong Wook Lee, Adrian Mistreanu

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The authors have studied a method for analyzing containment and penetration using an explicit nonlinear Finite Element Analysis. This method may be used in the stage of concept design for the protection of external configurations or components of aircraft engines and nuclear power plant structures. This paper consists of the modeling method, the results obtained from the method and the comparison of the results with those calculated from simple analytical method. It shows that the containment capability obtained by proposed method matches well with analytically calculated containment capability.

Keywords: computer aided engineering, containment analysis, finite element analysis, impact analysis, penetration analysis

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Authors: Avinash Malladhi

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Forensic accounting is a specialized field that involves the application of accounting principles, investigative skills, and legal knowledge to detect and prevent fraud. With the rise of big data and technological advancements, artificial intelligence (AI) and machine learning (ML) algorithms have emerged as powerful tools for forensic accountants to enhance their fraud detection capabilities. In this paper, we review and analyze various AI/ML algorithms that are commonly used in forensic accounting, including supervised and unsupervised learning, deep learning, natural language processing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Decision Trees, and Random Forests. We discuss their underlying principles, strengths, and limitations and provide empirical evidence from existing research studies demonstrating their effectiveness in detecting financial fraud. We also highlight potential ethical considerations and challenges associated with using AI/ML in forensic accounting. Furthermore, we highlight the benefits of these technologies in improving fraud detection and prevention in forensic accounting.

Keywords: AI, machine learning, forensic accounting & fraud detection, anti money laundering, Benford's law, fraud triangle theory

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Authors: Tina Beyene

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In this paper examines the state of grassroots feminist activism in the backdrop of state feminism in Ethiopia. Specifically, I examine the impact of the Charities and Societies Proclamation (aka CSO law), a 2009 law that banned so-called foreign NGOs—i.e., those receiving more than 10% of its operating budget from non-local sources— from working in the areas of human rights, democracy, governance, and gender equality. Viewed as government retribution for the NGO opposition to the government in the 2005 elections, the law aimed to halt the work groups such as the Ethiopian Women Lawyers Association (EWLA), who were defined as a “foreign” NGO. Based on interviews with prominent Ethiopian women’s rights leaders in Addis Ababa, Ethiopia, I assess how grassroots feminist organizing adapts to state suppression on the one hand, and the aggressive entry of the state into women’s rights work on the other hand. While the 2009 law has slowed down the work of women’s rights activism, displaced feminists view feminist advocacy as cyclical and the state as neither fully adversarial nor an ally but rather as an instable entity that at times provides political openings to push ambitious feminist agendas. Grassroots activists are regrouping and developing new political responses strategies such as coding rights issues to fit state mandate; dissembling rights work in permissible social provision language; rechanneling political work into informal spaces and unregistered social clubs; innovating new funding partnerships, and reassembling as privately held research and advocacy companies. my study reveals how grassroots feminist politics operates in the shadow of a hostile state and within the confines of local politics.

Keywords: grassroots feminism, ethiopian feminism, civil society and gender, state feminism

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Authors: Amreth Chandrasehar

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Machine Learning (ML) Models are developed and run in production to solve various use cases that help organizations to be more efficient and help drive the business. But this comes at a massive development cost and lost business opportunities. According to the Gartner report, 85% of data science projects fail, and one of the factors impacting this is not paying attention to Model Observability. Model Observability helps the developers and operators to pinpoint the model performance issues data drift and help identify root cause of issues. This paper focuses on providing insights into incorporating model observability in model development and operationalizing it in production.

Keywords: model observability, monitoring, drift detection, ML observability platform

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Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

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Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

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Authors: S. Houcine Habib, B. Elkhalil Hachi, Mohamed Guesmi, Mohamed Haboussi

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In this paper, dynamic fracture behaviors of cracked orthotropic structure are modeled using extended finite element method (X-FEM). In this approach, the finite element method model is first created and then enriched by special orthotropic crack tip enrichments and Heaviside functions in the framework of partition of unity. The mixed mode stress intensity factor (SIF) is computed using the interaction integral technique based on J-integral in order to predict cracking behavior of the structure. The developments of these procedures are programmed and introduced in a self-software platform code. To assess the accuracy of the developed code, results obtained by the proposed method are compared with those of literature.

Keywords: X-FEM, composites, stress intensity factor, crack, dynamic orthotropic behavior

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Authors: R. Simutis, V. Galvanauskas, D. Levisauskas, J. Repsyte, V. Grincas

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This paper deals with advanced state estimation algorithms for estimation of biomass concentration and specific growth rate in a typical fed-batch biotechnological process. This biotechnological process was represented by a nonlinear mass-balance based process model. Extended Kalman Filter (EKF) and Particle Filter (PF) was used to estimate the unmeasured state variables from oxygen uptake rate (OUR) and base consumption (BC) measurements. To obtain more general results, a simplified process model was involved in EKF and PF estimation algorithms. This model doesn’t require any special growth kinetic equations and could be applied for state estimation in various bioprocesses. The focus of this investigation was concentrated on the comparison of the estimation quality of the EKF and PF estimators by applying different measurement noises. The simulation results show that Particle Filter algorithm requires significantly more computation time for state estimation but gives lower estimation errors both for biomass concentration and specific growth rate. Also the tuning procedure for Particle Filter is simpler than for EKF. Consequently, Particle Filter should be preferred in real applications, especially for monitoring of industrial bioprocesses where the simplified implementation procedures are always desirable.

Keywords: biomass concentration, extended Kalman filter, particle filter, state estimation, specific growth rate

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Authors: Jaya Mathew

Abstract:

Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R

Procedia PDF Downloads 358

Authors: Amin Moradi, Maryam Moeini

Abstract:

It is a widely held view that Ghiyath al-Din Jamshid-e-Kashani, also known as al-Kashi (1380-1429 CE), was the first who played a significant role in the interaction between mathematicians and architects by introducing theoretical knowledge in Islamic architecture. In academic discourses, geometric rules extracted from his splendid volume titled as Key of Arithmetic has uncritically believed by historians of architecture to contemplate the whole process of arch design all throughout the Islamic buildings. His theories tried to solve the fundamental problem of structural design and to understand what makes an Islamic structure safe or unsafe. As a result, al-Kashi arrived at the conclusion that a safe state of equilibrium is achieved through a specific geometry as a rule. This paper reassesses the stability of al-Kashi's systematized principal forms to evaluate the logic of his hypothesis with a special focus on large spans. Besides the empirical experiences of the author in masonry constructions, the finite element approach was proposed considering the current standards in order to get a better understanding of the validity of geometric rules proposed by al-Kashi for the equilibrium conditions of Islamic masonry arches and vaults. The state of damage of his reference arches under loading condition confirms beyond any doubt that his conclusion of the geometrical configuration measured through his treaties present some serious operational limits and do not go further than some individualized mathematical hypothesis. Therefore, the nature of his mathematical studies regarding Islamic arches is in complete contradiction with the practical knowledge of construction methodology.

Keywords: Jamshid al-Kashani, Islamic architecture, Islamic geometry, construction equilibrium, collapse mechanism

Procedia PDF Downloads 111