Search results for: word processing

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: Alison L. Miles

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Almost all major financial institutions in the world suffered losses due to the financial crisis of 2007, but the extent varied widely. The causes of the crash of 2007 are well documented and predominately focus on the role and complexity of the financial markets. The dominant theme of the literature suggests the causes of the crash were a combination of globalization, financial sector innovation, moribund regulation and short termism. While these arguments are undoubtedly true, they do not tell the whole story. A key weakness in the current analysis is the lack of consideration of those leading the banks pre and during times of crisis. This purpose of this study is to examine the possible link between the leadership styles and characteristics of the CEO, CFO and chairman and the financial institutions that failed or needed recapitalization. As such, it contributes to the literature and debate on international financial crises and systemic risk and also to the debate on risk management and regulatory reform in the banking sector. In order to first test the proposition (p1) that there are prevalent leadership characteristics or traits in financial institutions, an initial study was conducted using a sample of the top 65 largest global banks and financial institutions according to the Banker Top 1000 banks 2014. Secondary data from publically available and official documents, annual reports, treasury and parliamentary reports together with a selection of press articles and analyst meeting transcripts was collected longitudinally from the period 1998 to 2013. A computer aided key word search was used in order to identify the leadership styles and characteristics of the chairman, CEO and CFO. The results were then compared with the leadership models to form a picture of leadership in the sector during the research period. As this resulted in separate results that needed combining, SPSS data editor was used to aggregate the results across the studies using the variables ‘leadership style’ and ‘company financial performance’ together with the size of the company. In order to test the proposition (p2) that there was a prevalent leadership style in the banks that failed and the proposition (P3) that this was different to those that did not, further quantitative analysis was carried out on the leadership styles of the chair, CEO and CFO of banks that needed recapitalization, were taken over, or required government bail-out assistance during 2007-8. These included: Lehman Bros, Merrill Lynch, Royal Bank of Scotland, HBOS, Barclays, Northern Rock, Fortis and Allied Irish. The findings show that although regulatory reform has been a key mechanism of control of behavior in the banking sector, consideration of the leadership characteristics of those running the board are a key factor. They add weight to the argument that if each crisis is met with the same pattern of popular fury with the financier, increased regulation, followed by back to business as usual, the cycle of failure will always be repeated and show that through a different lens, new paradigms can be formed and future clashes avoided.

Keywords: banking, financial crisis, leadership, risk

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Authors: Quentin Lobbe, David Chavalarias, Alexandre Delanoe

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The ICT revolution has given birth to an unprecedented world of digital traces and has impacted a wide number of knowledge-driven domains such as science, education or policy making. Nowadays, we are daily fueled by unlimited flows of articles, blogs, messages, tweets, etc. The internet itself can thus be considered as an unsteady hyper-textual environment where websites emerge and expand every day. But there are structures inside knowledge. A given text can always be studied in relation to others or in light of a specific socio-cultural context. By way of their textual traces, human beings are calling each other out: hypertext citations, retweets, vocabulary similarity, etc. We are in fact the architects of a giant web of elements of knowledge whose structures and shapes convey their own information. The global shapes of these digital traces represent a source of collective knowledge and the question of their visualization remains an opened challenge. How can we explore, browse and interact with such shapes? In order to navigate across these growing constellations of words and texts, interdisciplinary innovations are emerging at the crossroad between fields of social and computational sciences. In particular, complex systems approaches make it now possible to reconstruct the hidden structures of textual knowledge by means of multi-scale objects of research such as semantic maps and phylomemies. The phylomemy reconstruction is a generic method related to the co-word analysis framework. Phylomemies aim to reveal the temporal dynamics of large corpora of textual contents by performing inter-temporal matching on extracted knowledge domains in order to identify their conceptual lineages. This study aims to address the question of visualizing the global shapes of online political discussions related to the French presidential and legislative elections of 2017. We aim to build phylomemies on top of a dedicated collection of thousands of French political tweets enriched with archived contemporary news web articles. Our goal is to reconstruct the temporal evolution of online debates fueled by each political community during the elections. To that end, we want to introduce an iterative data exploration methodology implemented and tested within the free software Gargantext. There we combine synchronic and diachronic axis of visualization to reveal the dynamics of our corpora of tweets and web pages as well as their inner syntagmatic and paradigmatic relationships. In doing so, we aim to provide researchers with innovative methodological means to explore online semantic landscapes in a collaborative and reflective way.

Keywords: online political debate, French election, hyper-text, phylomemy

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Authors: K. Radha Krishnan, Mirajul Alom

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Dehydrated powder prepared from fresh radish (Raphanus sativus) leaves were investigated for the color stability by different drying methods (tray, sun and solar). The effect of blanching conditions, drying methods as well as drying temperatures (50 – 90°C) were considered for studying the color degradation kinetics of chlorophyll in the dehydrated powder. The hunter color parameters (L*, a*, b*) and total color difference (TCD) were determined in order to investigate the color degradation kinetics of chlorophyll. Blanching conditions, drying method and drying temperature influenced the changes in L*, a*, b* and TCD values. The changes in color values during processing were described by a first order kinetic model. The temperature dependence of chlorophyll degradation was adequately modeled by Arrhenius equation. To predict the losses in green color, a mathematical model was developed from the steady state kinetic parameters. The results from this study indicated the protective effect of blanching conditions on the color stability of dehydrated radish powder.

Keywords: chlorophyll, color stability, degradation kinetics, drying

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Authors: Ksenia Meshkova

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With increasing amount of multimedia data, it is very important to automate and speed up the process of obtaining meta. This process means not just recognition of some object or its movement, but recognition of the entire scene versus separate frames and having timeline segmentation as a final result. Labeling datasets is time consuming, besides, attributing characteristics to particular scenes is clearly difficult due to their nature. In this article, we will consider autoencoders application to unsupervised scene recognition and clusterization based on interpretable features. Further, we will focus on particular types of auto encoders that relevant to our study. We will take a look at the specificity of deep learning related to information theory and rate-distortion theory and describe the solutions empowering poor interpretability of deep learning in media content processing. As a conclusion, we will present the results of the work of custom framework, based on autoencoders, capable of scene recognition as was deeply studied above, with highlights generation resulted out of this recognition. We will not describe in detail the mathematical description of neural networks work but will clarify the necessary concepts and pay attention to important nuances.

Keywords: neural networks, computer vision, representation learning, autoencoders

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Authors: Joelle Thng, Li Fang

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Objective: As programming becomes a highly valued and sought-after skill in the economy, many universities have started offering Python courses to help students keep up with the demands of employers. This study focuses on designing a university module that effectively educates undergraduate students on financial analysis using Python programming. Methodology: To better satisfy the specific demands for each sector, this study adopted a qualitative research modus operandi to craft a module that would complement students’ existing financial skills. The lessons were structured using research-backed educational learning tools, and important Python concepts were prudently screened before being included in the syllabus. The course contents were streamlined based on criteria such as ease of learning and versatility. In particular, the skills taught were modelled in a way to ensure they were beneficial for financial data processing and analysis. Results: Through this study, a 6-week course containing the chosen topics and programming applications was carefully constructed for finance students. Conclusion: The findings in this paper will provide valuable insights as to how teaching programming could be customised for students hailing from various academic backgrounds.

Keywords: curriculum development, designing effective instruction, higher education strategy, python for finance students

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Authors: Athina Puype, Lorenzo Malerba, Nico De Wispelaere, Roumen Petrov, Jilt Sietsma

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Reduced activation ferritic/martensitic (RAFM) steels like EUROFER97 are primary candidate structural materials for first wall application in the future demonstration (DEMO) fusion reactor. Existing steels of this type obtain their functional properties by a two-stage heat treatment, which consists of an annealing stage at 980°C for thirty minutes followed by quenching and an additional tempering stage at 750°C for two hours. This thermal quench and temper (Q&T) treatment creates a microstructure of tempered martensite with, as main precipitates, M23C6 carbides, with M = Fe, Cr and carbonitrides of MX type, e.g. TaC and VN. The resulting microstructure determines the mechanical properties of the steel. The ductility is largely determined by the tempered martensite matrix, while the resistance to mechanical degradation, determined by the spatial and size distribution of precipitates and the martensite crystals, plays a key role in the high temperature properties of the steel. Unfortunately, the high temperature response of EUROFER97 is currently insufficient for long term use in fusion reactors, due to instability of the matrix phase and coarsening of the precipitates at prolonged high temperature exposure. The objective of this study is to induce grain refinement by appropriate modifications of the processing route in order to increase the high temperature strength of a lab-cast EUROFER RAFM steel grade. The goal of the work is to obtain improved mechanical behavior at elevated temperatures with respect to conventionally heat treated EUROFER97. A dilatometric study was conducted to study the effect of the annealing temperature on the mechanical properties after a Q&T treatment. The microstructural features were investigated with scanning electron microscopy (SEM), electron back-scattered diffraction (EBSD) and transmission electron microscopy (TEM). Additionally, hardness measurements, tensile tests at elevated temperatures and Charpy V-notch impact testing of KLST-type MCVN specimens were performed to study the mechanical properties of the furnace-heated lab-cast EUROFER RAFM steel grade. A significant prior austenite grain (PAG) refinement was obtained by lowering the annealing temperature of the conventionally used Q&T treatment for EUROFER97. The reduction of the PAG results in finer martensitic constituents upon quenching, which offers more nucleation sites for carbide and carbonitride formation upon tempering. The ductile-to-brittle transition temperature (DBTT) was found to decrease with decreasing martensitic block size. Additionally, an increased resistance against high temperature degradation was accomplished in the fine grained martensitic materials with smallest precipitates obtained by tailoring the annealing temperature of the Q&T treatment. It is concluded that the microstructural refinement has a pronounced effect on the DBTT without significant loss of strength and ductility. Further investigation into the optimization of the processing route is recommended to improve the mechanical behavior of RAFM steels at elevated temperatures.

Keywords: ductile-to-brittle transition temperature (DBTT), EUROFER, reduced activation ferritic/martensitic (RAFM) steels, thermal treatments

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Authors: Rujijan Vichivanives, Kittiya Poonsilp, Canasanan Wanavijit

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This research aims to create mobile tools to analyze rice disease quickly and easily. The principle of object-oriented software engineering and objective-C language were used for software development methodology and the principle of decision tree technique was used for analysis method. Application users can select the features of rice disease or the color appears on the rice leaves for recognition analysis results on iOS mobile screen. After completing the software development, unit testing and integrating testing method were used to check for program validity. In addition, three plant experts and forty farmers have been assessed for usability and benefit of this system. The overall of users’ satisfaction was found in a good level, 57%. The plant experts give a comment on the addition of various disease symptoms in the database for more precise results of the analysis. For further research, it is suggested that image processing system should be developed as a tool that allows users search and analyze for rice diseases more convenient with great accuracy.

Keywords: rice disease, data analysis system, mobile application, iOS operating system

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Authors: B. Khorram, M. Taslikh, R. Sattarzadeh, M. Ghazanfari

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Bitterness is one of the most important problems in cheese processing industry all over the world. There are several reasons that bitterness may develop in cheese. With a few exceptions bitterness is generally associated with proteolysis. In this investigation, anti-stick as a neutral substance in proteolysis were considered and studied for reducing the problem. This vast survey was conducted in a big cheese production factory (in Neyshabur) and in the same procedure anti-stick as interested factor in cheeses packaging compared to standard cheeses production, one line productions (65200 packs with anti-stick were tested by 2953 persons for bitterness and another line was included the same procedure with standard cheese. In this investigate: 83% of standard packaging cheeses, compared with only28% of consumers cheese with anti-stick which confirmed bitterness. Although bitterness is generally associated with proteolysis and Microbial factors, Somatic cell, Starters play important role in generating bitterness in ultra filtrated cheeses, but based on the results the other factors such as anti-stick in packaging can be effective methods for reducing and removing unfavorable bitterness in cheese production.

Keywords: bitterness, uf cheese, anti-stick, single packaged

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Authors: Ahmed Noor Al-Qayyim

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During the past decades, the bridge structures are considered very important portions of transportation networks, due to the fast urban sprawling. With the failure of bridges that under operating conditions lead to focus on updating the default bridge inspection methodology. The structures health monitoring (SHM) using the vibration response appeared as a promising method to evaluate the condition of structures. The rapid development in the sensors technology and the condition assessment techniques based on the vibration-based damage detection made the SHM an efficient and economical ways to assess the bridges. SHM is set to assess state and expects probable failures of designated bridges. In this paper, a presentation for Frequency Response function method that uses the captured vibration test information of structures to evaluate the structure condition. Furthermore, the main steps of the assessment of bridge using the vibration information are presented. The Frequency Response function method is applied to the experimental data of a full-scale bridge.

Keywords: bridge assessment, health monitoring, damage detection, frequency response function (FRF), signal processing, structure identification

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Authors: Rania Ajmi, Faiza Allouche Khebour, Aude Nuscia Taibi, Sirine Essasi

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Urban green spaces (UGS) as an important contributor can be a significant part of sustainable development. A spatial method was employed to assess and map the spatial distribution of UGS in five districts in Sousse, Tunisia. Ecological management of UGS is an essential factor for the sustainable development of the city; hence the municipality of Sousse has decided to support the districts according to different green spaces characters. And to implement this policy, (1) a new GIS web application was developed, (2) then the implementation of the various green spaces was carried out, (3) a spatial mapping of UGS using Quantum GIS was realized, and (4) finally a data processing and statistical analysis with RStudio programming language was executed. The intersection of the results of the spatial and statistical analyzes highlighted the presence of an imbalance in terms of the spatial UGS distribution in the study area. The discontinuity between the coast and the city's green spaces was not designed in a spirit of network and connection, hence the lack of a greenway that connects these spaces to the city. Finally, this GIS support will be used to assess and monitor green spaces in the city of Sousse by decision-makers and will contribute to improve the well-being of the local population.

Keywords: distributions, GIS, green space, imbalance, spatial analysis

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Authors: Royan Dawud Aldian, Endah Purwanti, Soegianto Soelistiono

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In this research we have been developed an automatic investigation to classify normal children voice or autistic by using modern computation technology that is computation based on artificial neural network. The superiority of this computation technology is its capability on processing and saving data. In this research, digital voice features are gotten from the coefficient of linear-predictive coding with auto-correlation method and have been transformed in frequency domain using fast fourier transform, which used as input of artificial neural network in back-propagation method so that will make the difference between normal children and autistic automatically. The result of back-propagation method shows that successful classification capability for normal children voice experiment data is 100% whereas, for autistic children voice experiment data is 100%. The success rate using back-propagation classification system for the entire test data is 100%.

Keywords: autism, artificial neural network, backpropagation, linier predictive coding, fast fourier transform

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Authors: Omaima G. Allam, O. A. Hakeim, K. Haggag, N. S. Elshemy

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In the midst of the two decades the use of microwave dielectric warming in the field of science has transformed into a powerful methodology to redesign compound procedures. The potential benefit of the application of these modern methods of treatment emphasize so as to reach to optimum treatment conditions and the best results, especially hydrophobicity, moisture content and increase dyeing processing while maintaining the physical and chemical properties of each textile. Moreover, polyester fibres are sometimes spun together with natural fibres to produce a cloth with blended properties. So that at the present task, the polyester/viscose mix fabrics (60 /40) were pretreated with 4 g/l of KOH for 2 min in microwave irradiation with a liquor ratio 1:25. Subsequently fabrics were inundated with different concentrations of sericin (10, 30, 50 g/l). Treated fabrics were coloured with the commercial dyes samples: Reactive Red 84(Dye 1). C. I. Acid Blue 203(Dye 2) and C.I. Reactive violet 5 (Dye 3). Colour value was specified as well as fastness properties. Likewise, the physical properties of untreated and treated fabrics such as moisture content %, tensile strength, elongation % and were evaluated. The untreated and treated fabrics are described by infrared spectroscopy (FTIR) and scanning electron microscopy.

Keywords: polyester viscose blends fabric, sericin, dyes, colour value

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Authors: Samanta Kietytė

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The object of this study is the verbal derivatives stemming from the Lithuanian prefix per-. The prefix under examination can be classified as prepositional, having descended from the preposition per, thereby sharing the same prototypical meaning – denoting movement OVER. These frequently co-occur within sentences (1). The aim of this paper is to conduct a semantic analysis of the prefix per- and to propose a possible radial network of its meanings. In essence, the aim is to identify the interrelationships existing between its meanings. 1) Jis peršoko per tvorą/ 3SG.NOM.M jump.PST.3 over fence.ACC.SG. /ʻHe jumped over the fenceʼ. The foundation of this work lies in the methodological and theoretical framework of cognitive linguistics. The prototypical meaning of prefixes consistently embodies spatial dimensions that can be described through image schemas. This entails the identification of the trajectory, the landmark, and the relation between them in the situation described by the prefixed verb. The meanings of linguistic units are not perceived as arbitrary, but rather, they are interconnected through semantic motivation. According to this perspective, a singular meaning within linguistic units is considered as prototypical, while additional meanings are descended (not necessarily directly) from it. For example, one of the per- meanings TRANSFER (2) is derived from the prototypical meaning OVER. 2) Prašau persiųsti vadovo laišką man./ Ask.PRS.1 forward.INF manager.GEN.SG email.ACC.SG 1.SG.DAT/ ʻPlease forward the manager‘s email to meʼ. Certain semantic relations are explained by the conceptual metaphor and metonymy theory. For instances, when prefixed verb has a meaning WIN (3) it is related to the prototypical meaning. In this case, the prefixed verb describes situations of winning in various ways. In the prototypical meaning, the trajector moves higher than the landmark, and winning is metaphorically perceived as being higher. 3) Sūnus peraugo tėvą./ Son.NOM.SG outgrow.PST.3 father.ACC.SG/ ʻThe son has outgrown the fatherʼ. The data utilized for this study was collected from the 2014 grammatically annotated text "Lithuanian Web (LithuanianWaC v2)", consisting of 63,645,700 words. Given that the corpus is grammatically lemmatized, the list of the 793 items was obtained using the wordlist function and specifying that verbs starting with per were searched. The list included not only prefixed verbs but also other verbs whose roots have the same letter sequences as prefixes. Also, words with misspellings, without diacritical marks, and words listed for lemmatization errors were rejected, and a total of 475 derivatives were left for further analysis. The semantic analysis revealed that there are 12 distinct meanings of the prefix per-. The spatial meanings were extracted by determining what a trajector is, what a landmark is, and what the relation between them is. The connection between non-spatial meanings and spatial ones occurs through semantic motivation established by identifying elements that correspond to the trajector and landmark. The analysis reveals that there are no strict boundaries among these meanings, instead showing a continuum that encompasses a central core and a peripheral association with their internal structure, i.e., some derivatives are more prototypical of a particular meaning than others.

Keywords: word-formation, cognitive semantics, metaphor, radial networks, prototype theory, prefix

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Authors: Akila Murali, Prithika Manivel

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Disasters are defined as a serious disruption of the functioning of a community or a society, which involves widespread human, material, economic or environmental impacts. The number of people suffering food crisis as a result of natural disasters has tripled in the last thirty years. The economic losses due to natural disasters have shown an increase with a factor of eight over the past four decades, caused by the increased vulnerability of the global society, and also due to an increase in the number of weather-related disasters. Efficient disaster detection and alerting systems could reduce the loss of life and properties. In the event of a disaster, another important issue is a good search and rescue system with high levels of precision, timeliness and safety for both the victims and the rescuers. Wireless Sensor Networks technology has the capability of quick capturing, processing, and transmission of critical data in real-time with high resolution. This paper studies the capacity of sensors and a Wireless Sensor Network to collect, collate and analyze valuable and worthwhile data, in an ordered manner to help with disaster management.

Keywords: alerting systems, disaster detection, Ad Hoc network, WSN technology

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Authors: Uduak Umoh, Imo Eyoh, Emmauel Nyoho

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This paper compares fuzzy-machine learning algorithms such as Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) for the predicting cases of fire outbreak. The paper uses the fire outbreak dataset with three features (Temperature, Smoke, and Flame). The data is pre-processed using Interval Type-2 Fuzzy Logic (IT2FL) algorithm. Min-Max Normalization and Principal Component Analysis (PCA) are used to predict feature labels in the dataset, normalize the dataset, and select relevant features respectively. The output of the pre-processing is a dataset with two principal components (PC1 and PC2). The pre-processed dataset is then used in the training of the aforementioned machine learning models. K-fold (with K=10) cross-validation method is used to evaluate the performance of the models using the matrices – ROC (Receiver Operating Curve), Specificity, and Sensitivity. The model is also tested with 20% of the dataset. The validation result shows KNN is the better model for fire outbreak detection with an ROC value of 0.99878, followed by SVM with an ROC value of 0.99753.

Keywords: Machine Learning Algorithms , Interval Type-2 Fuzzy Logic, Fire Outbreak, Support Vector Machine, K-Nearest Neighbour, Principal Component Analysis

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Authors: Cau Tran, Tu Nguyen, Tien Pham

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Model-based design (MBD) is a mathematical and visual technique for addressing design issues in the fields of communications, signal processing, and complicated control systems. It is utilized in several automotive, aerospace, industrial, and motion control applications. Virtual models are at the center of the software development process with model based design. A method used in the creation of embedded software is model-based design. In this study, the LAT motor is modeled in a simulation environment, and the LAT motor control is designed with a cascade structure, a speed and current control loop, and a controller that is used in the next part. A PID structure serves as this controller. Based on techniques and motor parameters that match the design goals, the PID controller is created for the model using traditional design principles. The MBD approach will be used to build embedded software for motor control. The paper will be divided into three distinct sections. The first section will introduce the design process and the benefits and drawbacks of the MBD technique. The design of control software for LAT motors will be the main topic of the next section. The experiment's results are the subject of the last section.

Keywords: model based design, limited angle torque, intellectual property core, hardware description language, controller area network, user datagram protocol

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Authors: Omar A. A. Shaebi, Matouk M. Elamari, Salaheddin Allid

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The Information Computing System of Monitoring (ICSM) for the Research Reactor of Tajoura Nuclear Research Centre (TNRC) stopped working since early 1991. According to the regulations, the computer is necessary to operate the reactor up to its maximum power (10 MW). The fund is secured via IAEA to develop a modern computer based data acquisition system to replace the old computer. This paper presents the development of the Labview based data acquisition system to allow automated measurements using National Instruments Hardware and its labview software. The developed system consists of SCXI 1001 chassis, the chassis house four SCXI 1100 modules each can maintain 32 variables. The chassis is interfaced with the PC using NI PCI-6023 DAQ Card. Labview, developed by National Instruments, is used to run and operate the DAQ System. Labview is graphical programming environment suited for high level design. It allows integrating different signal processing components or subsystems within a graphical framework. The results showed system capabilities in monitoring variables, acquiring and saving data. Plus the capability of the labview to control the DAQ.

Keywords: data acquisition, labview, signal conditioning, national instruments

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Authors: Ameur Abdelkader, Abed Bouarfa Hafida

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Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data.

Keywords: predictive analysis, big data, predictive analysis algorithms, CART algorithm

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Authors: António J. R. Neves, Rui Garcia, Paulo Dias, Alina Trifan

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With the aging of the world population and the continuous growth in technology, service robots are more and more explored nowadays as alternatives to healthcare givers or personal assistants for the elderly or disabled people. Any service robot should be capable of interacting with the human companion, receive commands, navigate through the environment, either known or unknown, and recognize objects. This paper proposes an approach for object recognition based on the use of depth information and color images for a service robot. We present a study on two of the most used methods for object detection, where 3D data is used to detect the position of objects to classify that are found on horizontal surfaces. Since most of the objects of interest accessible for service robots are on these surfaces, the proposed 3D segmentation reduces the processing time and simplifies the scene for object recognition. The first approach for object recognition is based on color histograms, while the second is based on the use of the SIFT and SURF feature descriptors. We present comparative experimental results obtained with a real service robot.

Keywords: object detection, feature, descriptors, SIFT, SURF, depth images, service robots

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Authors: Xuemei Jiang, Kunfu Zhu, Shouyang Wang

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In this paper, we selected China as a case and employ a time-series of unique input-output tables distinguishing firm ownership and processing exports, to evaluate the role of foreign-invested enterprises (FIEs) in China’s rapid carbon dioxide emission growth. The results suggested that FIEs contributed to 11.55% of the economic outputs’ growth in China between 1992-2010, but accounted for only 9.65% of the growth of carbon dioxide emissions. In relative term, until 2010 FIEs still emitted much less than Chinese-owned enterprises (COEs) when producing the same amount of outputs, although COEs experienced much faster technology upgrades. In an ideal scenario where we assume the final demands remain unchanged and COEs completely mirror the advanced technologies of FIEs, more than 2000 Mt of carbon dioxide emissions would be reduced for China in 2010. From a policy perspective, the widespread FIEs are very effective and efficient channel to encourage technology transfer from developed to developing countries.

Keywords: carbon dioxide emissions, foreign-invested enterprises, technology transfer, input–output analysis, China

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Authors: Mehaiguene Madjid, Touhari Fadhila, Meddi Mohamed

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The Cheliff basin offers a good hydrological example for the possibility of studying the problem which elucidated in the future, because of the unclearity in several aspects and hydraulic installation. Thus, our study of the Cheliff basin is divided into two principal parts: The spatial evaluation of the precipitation: also, the understanding of the modes of the reconstitution of the resource in water supposes a good knowledge of the structuring of the precipitation fields in the studied space. In the goal of a good knowledge of revitalizes them in water and their management integrated one judged necessary to establish a precipitation card of the Cheliff basin for a good understanding of the evolution of the resource in water in the basin and that goes will serve as basis for all study of hydraulic planning in the Cheliff basin. Then, the establishment of the precipitation card of the Cheliff basin answered a direct need of setting to the disposition of the researchers for the region and a document of reference that will be completed therefore and actualized. The hydrological study, based on the statistical hydrometric data processing will lead us to specify the hydrological terms of the assessment hydrological and to clarify the fundamental aspects of the annual flow, seasonal, extreme and thus of their variability and resources surface water.

Keywords: hydrological assessment, surface water resources, Cheliff, Algeria

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Authors: Mohammad Beyki, Justus Pawlak, Robert Patzke, Franz Renz

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In the context of planetary exploration, the MIMOS II (miniaturized Mössbauer spectrometer) serves as a proven and reliable measuring instrument. The transmission behaviour of the electronics in the Mössbauer spectroscopy is newly developed and optimized. For this purpose, the overall electronics is split into three parts. This elaboration deals exclusively with the first part of the signal chain for the evaluation of photons in experiments with gamma radiation. Parallel to the analysis of the electronics, a new method for the stability consideration of linear and non-linear systems is presented: The extended method of Lyapunov’s stability criteria. The design helps to weigh advantages and disadvantages against other simulated circuits in order to optimize the MIMOS II for the terestric and extraterestric measurment. Finally, after stability analysis, the controller design according to Ackermann is performed, achieving the best possible optimization of the output variable through a skillful pole assignment.

Keywords: Mössbauer spectroscopy, electronic signal amplifier, light processing technology, photocurrent, trans-impedance amplifier, extended Lyapunov method

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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The most aggressive form of brain tumor is called glioma. Glioma is kind of tumor that arises from glial tissue of the brain and occurs quite often. A fully automatic 2D-CNN model for brain tumor segmentation is presented in this paper. We performed pre-processing steps to remove noise and intensity variances using N4ITK and standard intensity correction, respectively. We used Keras open-source library with Theano as backend for fast implementation of CNN model. In addition, we used BRATS 2015 MRI dataset to evaluate our proposed model. Furthermore, we have used SimpleITK open-source library in our proposed model to analyze images. Moreover, we have extracted random 2D patches for proposed 2D-CNN model for efficient brain segmentation. Extracting 2D patched instead of 3D due to less dimensional information present in 2D which helps us in reducing computational time. Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.77 for complete, 0.76 for core, 0.77 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, LGG

Procedia PDF Downloads 182

Authors: Duygu Dere, Mert Ergeneci, Kaan Gokcesu

Abstract:

Google Trends data has gained increasing popularity in the applications of behavioral finance, decision science and risk management. Because of Google’s wide range of use, the Trends statistics provide significant information about the investor sentiment and intention, which can be used as decisive factors for corporate and risk management fields. However, an anomaly, a significant increase or decrease, in a certain query cannot be detected by the state of the art applications of computation due to the random baseline noise of the Trends data, which is modelled as an Additive white Gaussian noise (AWGN). Since through time, the baseline noise power shows a gradual change an adaptive thresholding method is required to track and learn the baseline noise for a correct classification. To this end, we introduce an online method to classify meaningful deviations in Google Trends data. Through extensive experiments, we demonstrate that our method can successfully classify various anomalies for plenty of different data.

Keywords: adaptive data processing, behavioral finance , convex optimization, online learning, soft minimum thresholding

Procedia PDF Downloads 167

Authors: Emel Kızılkaya Aydogan, Mihrimah Ozmen, Yılmaz Delice

Abstract:

In recent days, there is a change and the ongoing development of the telecommunications sector in the global market. In this sector, churn analysis techniques are commonly used for analysing why some customers terminate their service subscriptions prematurely. In addition, customer churn is utmost significant in this sector since it causes to important business loss. Many companies make various researches in order to prevent losses while increasing customer loyalty. Although a large quantity of accumulated data is available in this sector, their usefulness is limited by data quality and relevance. In this paper, a cost-sensitive feature selection framework is developed aiming to obtain the feature reducts to predict customer churn. The framework is a cost based optional pre-processing stage to remove redundant features for churn management. In addition, this cost-based feature selection algorithm is applied in a telecommunication company in Turkey and the results obtained with this algorithm.

Keywords: churn prediction, data mining, decision-theoretic rough set, feature selection

Procedia PDF Downloads 446

Authors: Tarek M. Ahmed, Ahmed S. El Mesalamy, Amro M. Youssef, Tawfik T. El Midany

Abstract:

Abrasive waterjet (AWJ) machining is considered as one of the most powerful cutting processes. It can be used for cutting heat sensitive, hard and reflective materials. Aluminum 2024 is a high-strength alloy which is widely used in aerospace and aviation industries. This paper aims to improve aluminum alloy and to investigate the effect of AWJ control parameters on surface geometry quality. Design of experiments (DoE) is used for establishing an experimental matrix. Statistical modeling is used to present a relation between the cutting parameters (pressure, speed, and distance between the nozzle and cut surface) and responses (taper angle and surface roughness). The results revealed a tangible improvement in productivity by using AWJ processing. The taper kerf angle can be improved by decreasing standoff distance and speed and increasing water pressure. While decreasing (cutting speed, pressure and distance between the nozzle and cut surface) improve the surface roughness in the operating window of cutting parameters.

Keywords: abrasive waterjet machining, machining of aluminum alloy, non-traditional cutting, statistical modeling

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Authors: Sari Elkahina, Zergoug Mourad, Benachenhou Kamel

Abstract:

The main purpose of this paper is to perform a computations comparison of stress intensity factor 'SIF' evaluation in case of cracked thin plate with Aluminum alloy 7075-T6 and 2024-T3 used in aeronautics structure under uniaxial loading. This evaluation is based on finite element method with a virtual power principle through two techniques: the extrapolation and G−θ. The first one consists to extrapolate the nodal displacements near the cracked tip using a refined triangular mesh with T3 and T6 special elements, while the second, consists of determining the energy release rate G through G−θ method by potential energy derivation which corresponds numerically to the elastic solution post-processing of a cracked solid by a contour integration computation via Gauss points. The SIF obtained results from extrapolation and G−θ methods will be compared to an analytical solution in a particular case. To illustrate the influence of the meshing kind and the size of integration contour position simulations are presented and analyzed.

Keywords: crack tip, SIF, finite element method, concentration technique, displacement extrapolation, aluminum alloy 7075-T6 and 2024-T3, energy release rate G, G-θ method, Gauss point numerical integration

Procedia PDF Downloads 337

Authors: Ibrahim Gomaa, Hoda M. O. Mokhtar

Abstract:

Although most of the existing skyline queries algorithms focused basically on querying static points through static databases; with the expanding number of sensors, wireless communications and mobile applications, the demand for continuous skyline queries has increased. Unlike traditional skyline queries which only consider static attributes, continuous skyline queries include dynamic attributes, as well as the static ones. However, as skyline queries computation is based on checking the domination of skyline points over all dimensions, considering both the static and dynamic attributes without separation is required. In this paper, we present an efficient algorithm for computing continuous skyline queries without discriminating between static and dynamic attributes. Our algorithm in brief proceeds as follows: First, it excludes the points which will not be in the initial skyline result; this pruning phase reduces the required number of comparisons. Second, the association between the spatial positions of data points is examined; this phase gives an idea of where changes in the result might occur and consequently enables us to efficiently update the skyline result (continuous update) rather than computing the skyline from scratch. Finally, experimental evaluation is provided which demonstrates the accuracy, performance and efficiency of our algorithm over other existing approaches.

Keywords: continuous query processing, dynamic database, moving object, skyline queries

Procedia PDF Downloads 210

Authors: Kaoutar Ben Azzou, Hanaa Talei

Abstract:

Human resources is an important department in each organization as it manages the life cycle of employees from recruitment training to retirement or termination of contracts. The recruitment process starts with a job opening, followed by a selection of the best-fit candidates from all applicants. Matching the best profile for a job position requires a manual way of looking at many CVs, which requires hours of work that can sometimes lead to choosing not the best profile. The work presented in this paper aims at reducing the workload of HR personnel by automating the preliminary stages of the candidate screening process, thereby fostering a more streamlined recruitment workflow. This tool introduces an automated system designed to help with the recruitment process by scanning candidates' CVs, extracting pertinent features, and employing machine learning algorithms to decide the most fitting job profile for each candidate. Our work employs natural language processing (NLP) techniques to identify and extract key features from unstructured text extracted from a CV, such as education, work experience, and skills. Subsequently, the system utilizes these features to match candidates with job profiles, leveraging the power of classification algorithms.

Keywords: automated recruitment, candidate screening, machine learning, human resources management

Procedia PDF Downloads 56