Search results for: forensic accounting & fraud detection

Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Éva Pintér, Péter Bagó, Nikolett Deutsch, Miklós Hetényi

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The process of digital transformation has a profound influence on all sectors of the worldwide economy and the business environment. The influence of blockchain technology can be observed in the digital economy and e-government, rendering it an essential element of a nation's growth strategy. The banking industry is experiencing significant expansion and development of financial technology firms. Utilizing developing technologies such as artificial intelligence (AI), machine learning (ML), and big data (BD), these entrants are offering more streamlined financial solutions, promptly addressing client demands, and presenting a challenge to incumbent institutions. The advantages of digital transformation are evident in the corporate realm, and firms that resist its adoption put their survival at risk. The advent of digital technologies has revolutionized the business environment, streamlining processes and creating opportunities for enhanced communication and collaboration. Thanks to the aid of digital technologies, businesses can now swiftly and effortlessly retrieve vast quantities of information, all the while accelerating the process of creating new and improved products and services. Big data analytics is generally recognized as a transformative force in business, considered the fourth paradigm of science, and seen as the next frontier for innovation, competition, and productivity. Big data, an emerging technology that is shaping the future of the banking sector, offers numerous advantages to banks. It enables them to effectively track consumer behavior and make informed decisions, thereby enhancing their operational efficiency. Banks may embrace big data technologies to promptly and efficiently identify fraud, as well as gain insights into client preferences, which can then be leveraged to create better-tailored products and services. Moreover, the utilization of big data technology empowers banks to develop more intelligent and streamlined models for accurately recognizing and focusing on the suitable clientele with pertinent offers. There is a scarcity of research on big data analytics in the banking industry, with the majority of existing studies only examining the advantages and prospects associated with big data. Although big data technologies are crucial, there is a dearth of empirical evidence about the role of big data analytics (BDA) capabilities in bank performance. This research addresses a gap in the existing literature by introducing a model that combines the resource-based view (RBV), the technical organization environment framework (TOE), and dynamic capability theory (DC). This study investigates the influence of Big Data Analytics (BDA) utilization on the performance of market and risk management. This is supported by a comparative examination of Hungarian mobile banking services.

Keywords: big data, digital transformation, dynamic capabilities, mobile banking

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Authors: Dina Ahmed Alkhodary

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Human Resources are the individual skills, knowledge, attitude, capabilities and experience collected to produce wealth to the company. Human Resource disclosure is the process of involving, reporting, and sharing the Investments made in the Human Resources of an Organization that such as organizations short goals and objectives, employees creation value, training and development plan are presently not accounted for in the conventional accounting practices which is importance nowadays to reduce the employee`s turnover. For the purpose of the study 3 telecommunications companies in Jordan have been selected. Telecommunication industry has been chosen for this study since it is a successful sector in Jordan and Human resource disclosure practices were adopted in all the selected companies and companies was aware to the HR practices. The objective of the study is to find out the HR disclosures practices of the telecommunication Companies in Jordan and to find the relationship between the HR Disclosures practices and employees’ turnover which has been measured by leaver proficiencies, remaining member proficiencies and the new comers proficiencies. The researcher has used the questioner to collect data for the research purpose. Results reveal that There are human resource disclosure practices in telecommunication companies in Jordan but in some areas only and has found There that there is a significant relationship between the human resource disclosure practices of the telecommunication companies in Jordan and Employees turnover. It is important to the companies to disclose more information and it’s important to the researchers to study the HR disclosure in the other industries in Jordan to increase the awareness about it.

Keywords: HR, disclosure, employee, turnover

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Authors: Kashima Arora, Monika Tomar, Vinay Gupta

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Biosensors have found potential applications ranging from environmental testing and biowarfare agent detection to clinical testing, health care, and cell analysis. This is driven in part by the desire to decrease the cost of health care and to obtain precise information more quickly about the health status of patient by the development of various biosensors, which has become increasingly prevalent in clinical testing and point of care testing for a wide range of biological elements. Uric acid is an important byproduct in human body and a number of pathological disorders are related to its high concentration in human body. In past few years, rapid growth in the development of new materials and improvements in sensing techniques have led to the evolution of advanced biosensors. In this context, metal oxide thin film based matrices due to their bio compatible nature, strong adsorption ability, high isoelectric point (IEP) and abundance in nature have become the materials of choice for recent technological advances in biotechnology. In the past few years, wide band-gap metal oxide semiconductors including ZnO, SnO₂ and CeO₂ have gained much attention as a matrix for immobilization of various biomolecules. Tin oxide (SnO₂), wide band gap semiconductor (Eg =3.87 eV), despite having multifunctional properties for broad range of applications including transparent electronics, gas sensors, acoustic devices, UV photodetectors, etc., it has not been explored much for biosensing purpose. To realize a high performance miniaturized biomolecular electronic device, rf sputtering technique is considered to be the most promising for the reproducible growth of good quality thin films, controlled surface morphology and desired film crystallization with improved electron transfer property. Recently, iron oxide and its composites have been widely used as matrix for biosensing application which exploits the electron communication feature of Fe, for the detection of various analytes using urea, hemoglobin, glucose, phenol, L-lactate, H₂O₂, etc. However, to the authors’ knowledge, no work is being reported on modifying the electronic properties of SnO₂ by implanting with suitable metal (Fe) to induce the redox couple in it and utilizing it for reagentless detection of uric acid. In present study, Fe implanted SnO₂ based matrix has been utilized for reagentless uric acid biosensor. Implantation of Fe into SnO₂ matrix is confirmed by energy-dispersive X-Ray spectroscopy (EDX) analysis. Electrochemical techniques have been used to study the response characteristics of Fe modified SnO₂ matrix before and after uricase immobilization. The developed uric acid biosensor exhibits a high sensitivity to about 0.21 mA/mM and a linear variation in current response over concentration range from 0.05 to 1.0 mM of uric acid besides high shelf life (~20 weeks). The Michaelis-Menten kinetic parameter (Km) is found to be relatively very low (0.23 mM), which indicates high affinity of the fabricated bioelectrode towards uric acid (analyte). Also, the presence of other interferents present in human serum has negligible effect on the performance of biosensor. Hence, obtained results highlight the importance of implanted Fe:SnO₂ thin film as an attractive matrix for realization of reagentless biosensors towards uric acid.

Keywords: Fe implanted tin oxide, reagentless uric acid biosensor, rf sputtering, thin film

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Authors: Shringika Soni, Utkarsh Jain, Nidhi Chauhan

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Aptamers are recently discovered ~80-100 bp long artificial oligonucleotides that not only demonstrated their applications in therapeutics; it is tremendously used in diagnostic and sensing application to detect different biomarkers and drugs. Synthesizing aptamers for proteins or genomic template is comparatively feasible in laboratory, but drugs or other chemical target based aptamers require major specification and proper optimization and validation. One has to optimize all selection, amplification, and characterization steps of the end product, which is extremely time-consuming. Therefore, we performed asymmetric PCR (polymerase chain reaction) for random oligonucleotides pool synthesis, and further use them in Systematic evolution of ligands by exponential enrichment (SELEX) for anti-psychotic drugs based aptamers synthesis. Anti-psychotic drugs are major tranquilizers to control psychosis for proper cognitive functions. Though their low medical use, their misuse may lead to severe medical condition as addiction and can promote crime in social and economical impact. In this work, we have approached the in-vitro SELEX method for ssDNA synthesis for anti-psychotic drugs (in this case ‘target’) based aptamer synthesis. The study was performed in three stages, where first stage included synthesis of random oligonucleotides pool via asymmetric PCR where end product was analyzed with electrophoresis and purified for further stages. The purified oligonucleotide pool was incubated in SELEX buffer, and further partition was performed in the next stage to obtain target specific aptamers. The isolated oligonucleotides are characterized and quantified after each round of partition, and significant results were obtained. After the repetitive partition and amplification steps of target-specific oligonucleotides, final stage included sequencing of end product. We can confirm the specific sequence for anti-psychoactive drugs, which will be further used in diagnostic application in clinical and forensic set-up.

Keywords: anti-psychotic drugs, aptamer, biosensor, ssDNA, SELEX

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Authors: Attila Kiss, Erzsébet Némedi, Zoltán Naár

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As natural prebiotic (non-digestible) carbohydrates stimulate the growth of colon microflora and contribute to maintain the health of the host, analytical studies aiming at revealing the chemical behavior of these beneficial food components came to the forefront of interest. Food processing (especially baking) may lead to a significant conversion of the parent compounds, hence it is of utmost importance to characterize the transformation patterns and the plausible decomposition products formed by thermal degradation. The relevance of this work is confirmed by the wide-spread use of these carbohydrates (fructo-oligosaccharides, cyclodextrins, raffinose and resistant starch) in the food industry. More and more functional foodstuffs are being developed based on prebiotics as bioactive components. 12 different types of oligosaccharides have been investigated in order to reveal their thermal degradation characteristics. Different carbohydrate derivatives (D-fructose and D-glucose oligomers and polymers) have been exposed to elevated temperatures (150 °C 170 °C, 190 °C, 210 °C, and 220 °C) for 10 min. An advanced HPLC method was developed and used to identify the decomposition products of carbohydrates formed as a consequence of thermal treatment. Gradient elution was applied with binary solvent elution (acetonitrile, water) through amine based carbohydrate column. Evaporative light scattering (ELS) proved to be suitable for the reliable detection of the UV/VIS inactive carbohydrate degradation products. These experimental conditions and applied advanced techniques made it possible to survey all the formed intermediers. Change in oligomer distribution was established in cases of all studied prebiotics throughout the thermal treatments. The obtained results indicate increased extent of chain degradation of the carbohydrate moiety at elevated temperatures. Prevalence of oligomers with shorter chain length and even the formation of monomer sugars (D-glucose and D-fructose) might be observed at higher temperatures. Unique oligomer distributions, which have not been described previously are revealed in the case of each studied, specific carbohydrate, which might result in various prebiotic activities. Resistant starches exhibited high stability when being thermal treated. The degradation process has been modeled by a plausible reaction mechanism, in which proton catalyzed degradation and chain cleavage take place.

Keywords: prebiotics, thermal degradation, fructo-oligosaccharide, HPLC, ELS detection

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Authors: Victor Ananth Paramananth, Rashid Muniginin, Mahaya Abd Rahman, Siti Afifah Ismail

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Safety issues, scene protection, and appropriate evidence collection must be handled in any bio crime scene. There will be a scene or multi-scene to be cordoned for investigation in any bio-incident or bio crime event. Evidence collection is critical in determining the type of microbial or toxin, its lethality, and its source. As a consequence, from the start of the investigation, a proper sampling method is required. The most significant challenges for the crime scene officer would be deciding where to obtain samples, the best sampling method, and the sample sizes needed. Since there could be evidence in liquid, viscous, or powder shape at a crime scene, crime scene officers have difficulty determining which tools to use for sampling. To maximize sample collection, the appropriate tools for sampling methods are necessary. This study aims to assist the crime scene officer in collecting liquid, viscous, and powder biological samples in sufficient quantity while preserving sample quality. Observational tests on sample collection using liquid, viscous, and powder samples for adequate quantity and sample quality were performed using UV light in this research. The density of the light emission varies upon the method of collection and sample types. The best tools for collecting sufficient amounts of liquid, viscous, and powdered samples can be identified by observing UV light. Instead of active microorganisms, the invisible powder is used to assess sufficient sample collection during a crime scene investigation using various collection tools. The liquid, powdered and viscous samples collected using different tools were analyzed using Fourier transform infrared - attenuate total reflection (FTIR-ATR). FTIR spectroscopy is commonly used for rapid discrimination, classification, and identification of intact microbial cells. The liquid, viscous and powdered samples collected using various tools have been successfully observed using UV light. Furthermore, FTIR-ATR analysis showed that collected samples are sufficient in quantity while preserving their quality.

Keywords: biological sample, crime scene, collection tool, UV light, forensic

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Authors: Muktar Haruna

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This research work examines the International Financial Reporting Standard (IFRS) adoption and value relevance of earnings of listed consumer goods companies in the Nigerian. The population of the study comprises 22 listed consumer goods companies, out of which 15 were selected as sample size of the study. The scope of the study is a 12-year period covering from 2006 to 2018. Secondary data from the annual report of sampled companies were used, which consists of earnings per share (EPS), the book value of equity per share (BVE) as independent variables; firm size (FSZ) as a control variable, and market share price of sampled companies from Nigerian stock exchange as dependent variable. Multiple regressions were used to analyze the data. The results of the study showed that IFRS did not improve the value relevance of earnings after the adoption, which translates to a decrease in value relevance of accounting numbers in the post-adoption period. The major recommendation is that the Nigerian Reporting Council should ensure full compliance to all provisions of IFRS and provide uniformity in the presentation of non-current assets in the statement of financial position, where some present only net current assets leaving individual figures for current assets and liabilities invisible.

Keywords: IFRS, adoption, value relevance, earning per share, book value of equity per share

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Authors: Shun-Qi Zhang, Shu-Yang Zhang, Min Chen, , Jing Bai

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Piezoelectric laminated smart structures can be subjected to the strong driving electric field, which may result in large displacements and rotations. In one hand, piezoelectric materials usually behave very significant material nonlinear effects under strong electric fields. On the other hand, thin-walled structures undergoing large displacements and rotations exist nonnegligible geometric nonlinearity. In order to give a precise prediction of piezo laminated smart structures under the large electric field, this paper develops a finite element (FE) model accounting for material nonlinearity (piezoelectric part) and geometric nonlinearity based on the first order shear deformation (FSOD) hypothesis. The proposed FE model is first validated by both experimental and numerical examples from the literature. Afterwards, it is applied to simulate for plate and shell structures with multiple piezoelectric patches under the strong applied electric field. From the simulation results, it shows that large discrepancies occur between linear and nonlinear predictions for piezoelectric laminated structures driving at the strong electric field. Therefore, both material and geometric nonlinearities should be taken into account for piezoelectric structures under strong electric.

Keywords: piezoelectric smart structures, finite element analysis, geometric nonlinearity, electroelastic material nonlinearities

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Authors: Alicja Starczewska, Aleksander Nawrat, Krzysztof Daniec, Jarosław Homa, Kacper Hołda

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Border Gateway Protocol is the main routing protocol that enables routing establishment between all autonomous systems, which are the basic administrative units of the internet. Due to the poor protection of BGP, it is important to use additional BGP security systems. Many solutions to this problem have been proposed over the years, but none of them have been implemented on a global scale. This article describes a system capable of building images of real-time BGP network topology in order to detect BGP anomalies. Our proposal performs a detailed analysis of BGP messages that come into local network cards supplemented by information collected by remote collectors in different localizations.

Keywords: BGP, BGP hijacking, cybersecurity, detection

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Authors: Erick C. Valverde, Wansu Lim

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Facial emotion recognition (FER) system has been recently developed for more advanced computer vision applications. The ability to identify human emotions would enable smart healthcare facility to diagnose mental health illnesses (e.g., depression and stress) as well as better human social interactions with smart technologies. The FER system involves two steps: 1) face detection task and 2) facial emotion recognition task. It classifies the human expression in various categories such as angry, disgust, fear, happy, sad, surprise, and neutral. This system requires intensive research to address issues with human diversity, various unique human expressions, and variety of human facial features due to age differences. These issues generally affect the ability of the FER system to detect human emotions with high accuracy. Early stage of FER systems used simple supervised classification task algorithms like K-nearest neighbors (KNN) and artificial neural networks (ANN). These conventional FER systems have issues with low accuracy due to its inefficiency to extract significant features of several human emotions. To increase the accuracy of FER systems, deep learning (DL)-based methods, like convolutional neural networks (CNN), are proposed. These methods can find more complex features in the human face by means of the deeper connections within its architectures. However, the inference speed and computational costs of a DL-based FER system is often disregarded in exchange for higher accuracy results. To cope with this drawback, an optimized DL-based FER system is proposed in this study.An extreme version of Inception V3, known as Xception model, is leveraged by applying different network optimization methods. Specifically, network pruning and quantization are used to enable lower computational costs and reduce memory usage, respectively. To support low resource requirements, a 68-landmark face detector from Dlib is used in the early step of the FER system.Furthermore, a DL compiler is utilized to incorporate advanced optimization techniques to the Xception model to improve the inference speed of the FER system. In comparison to VGG-Net and ResNet50, the proposed optimized DL-based FER system experimentally demonstrates the objectives of the network optimization methods used. As a result, the proposed approach can be used to create an efficient and real-time FER system.

Keywords: deep learning, face detection, facial emotion recognition, network optimization methods

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Authors: Amal Saafan, Kareem Al Sofy, Sameh AbdelGhani, Magdy Amin

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Background: Acinetobacter spp. resistance towards β-lactam antibiotics is mediated mainly by different classes of β-lactamases production; detection of some genes responsible for production of β-lactamases is the objective of the study. Methods: One hundred fifty bacterial isolates were recovered from blood, sputum, and urine specimens from different hospitals in Egypt. Sixty-nine isolate were identified as Acinetobacter baumannii using traditional biochemical tests, CHROM agar, MicroScan and PCR amplification of blaoxa-51like gene. Acinetobacterbaumannii isolates were grouped into carbapenem resistant group (GP1), cefotaxime, ceftazidime and cefoxitin resistant group (GP2) and carbapenem and cephalosporin non-resistant group (GP3). Carbapenemase activity was screened using modified Hodge test (MHT) for GP1.Metallo-β-lactamases screening was performed for MHT positive isolates using double disk synergy test (DDST) and combined disk test (CDT). Amp C activity was screened using Amp C disk test with Tris-EDTA, DDST, and CDT for GP2. Finally, PCR amplification of blaoxa-51like, blaoxa-23like, blaIMP-like, blaVIM-like, and blaADC-like genes was performed for isolates that showed, at least, two positive results of three for both AmpC and carbapenemases phenotypic screening tests (obvious activity), in addition to GP3 (for comparison). Detection of blaoxa-51like and blaADC-like genes preceded by ISAba1 was also performed. Results: Antibiogram of 69 pure Acinetobacter baumannii isolates resulted in 57, 64, and 2 isolates enrolled into GP1, GP2, and GP3, respectively. Carbapenemase activity was shown by 49(85.9%) isolate using MHT. Metallo-β-lactamases screening revealed 32(65.3%) and 35(71.4%) using DDST and CDT, respectively.AmpC activity was shown by 43(67.2%) and 50 (78.1%) isolates using AmpC disk test with Tris-EDTA, and both DDST and CDT, respectively. Twenty-seven isolates showed obvious activity, all of them (100%) were harboring blaoxa-51like and blaADC-like genes, while blaoxa-23like, blaIMP-like andblaVIM-like genes were harbored by 23(85.2%), 9 (33.%) and no isolate respectively. Only 12 (44.4%) isolates harbored blaoxa-51like and blaADC-like genes preceded by ISAba1. GP3 isolates showed only positive blaoxa-51like and blaADC-like genes. Conclusion: It is not possible to correlate resistance with presence of blaoxa-51like and blaADC-like genes and presence of ISAba1 was immediate as transcriptional promoter. A blaoxa-23like gene played an important role in carbapenem resistance when compared with blaIMP-like and blaVIM-like gene.

Keywords: acinetobacter, beta-lactams, resistance, antimicrobial agents

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Authors: Heungsu Lee, Youngseok Kim, Jonghwa Yi, Chul Park

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A landing pier is subjected to safety assessment by visual inspection and design data, but it is difficult to check the damage in real-time. In this study, real - time damage detection and safety evaluation methods were studied. As a result of structural analysis of the arbitrary landing pier structure, the inflection point of deformation and moment occurred at 10%, 50%, and 90% of pile length. The critical value of Fiber Bragg Grating (FBG) sensor was set according to the safety factor, and the FBG sensor application method for real - time safety evaluation was derived.

Keywords: FBG sensor, harbor structure, maintenance, safety evaluation system

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Authors: Jakub Bartus

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In general, composite steel and concrete structures present an effective structural solution utilizing full potential of both materials. As they have a numerous advantages on the construction side, they can reduce greatly the overall cost of construction, which is the main objective of the last decade, highlighted by the current economic and social crisis. The study represents not only an analysis of composite beams’ behaviour having web openings but emphasizes the influence of these openings on the total strain distribution at the level of steel bottom flange as well. The major investigation was focused on a change of structural performance with respect to various layouts of openings. Examining this structural modification, an improvement of load carrying capacity of composite beams was a prime object. The study is devided into analytical and numerical part. The analytical part served as an initial step into the design process of composite beam samples, in which optimal dimensions and specific levels of utilization in individual stress states were taken into account. The numerical part covered description of imposed structural issue in a form of a finite element model (FEM) using strut and shell elements accounting for material non-linearities. As an outcome, a number of conclusions were drawn describing and explaining an effect of web opening presence on the structural performance of composite beams.

Keywords: composite beam, web opening, steel flange, totalstrain, finite element analysis

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Authors: Ticijana Prijon, Branko Ermenc

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Background: Blunt aortic trauma (BAT) includes various morphological changes that occur during deceleration, acceleration and/or body compression in traffic accidents. The various forms of BAT, from limited laceration of the intima to complete transection of the aorta, depends on the force acting on the vessel wall and the tolerance of the aorta to injury. The force depends on the change in velocity, the dynamics of the accident and of the seating position in the car. Tolerance to aortic injury depends on the anatomy, histological structure and pathomorphological alterations due to aging or disease of the aortic wall.An overview of the literature and medical documentation reveals that different terms are used to describe certain forms of BAT, which can lead to misinterpretation of findings or diagnoses. We therefore, propose a classification that would enable uniform systematic screening of all forms of BAT. We have classified BAT into three morphologycal types: TYPE I (intramural), TYPE II (transmural) and TYPE III (multiple) aortic ruptures with appropriate subtypes. Methods: All car accident casualties examined at the Institute of Forensic Medicine from 2001 to 2009 were included in this retrospective study. Autopsy reports were used to determine the occurrence of each morphological type of BAT in deceased drivers, front seat passengers and other passengers in cars and to define the morphology of BAT in relation to the accident dynamics and the age of the fatalities. Results: A total of 391 fatalities in car accidents were included in the study. TYPE I, TYPE II and TYPE III BAT were observed in 10,9%, 55,6% and 33,5%, respectively. The incidence of BAT in drivers, front seat and other passengers was 36,7%, 43,1% and 28,6%, respectively. In frontal collisions, the incidence of BAT was 32,7%, in lateral collisions 54,2%, and in other traffic accidents 29,3%. The average age of fatalities with BAT was 42,8 years and of those without BAT 39,1 years. Conclusion: Identification and early recognition of the risk factors of BAT following a traffic accident is crucial for successful treatment of patients with BAT. Front seat passengers over 50 years of age who have been injured in a lateral collision are the most at risk of BAT.

Keywords: aorta, blunt trauma, car accidents, morphology, risk factors

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Authors: Laura Boschis, Elena D. Ozzello, Enzo Mastromatteo

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Point of care diagnostic finds growing interest in medicine and agri-food because of faster intervention and prevention. EliChip is a microfluidic platform to perform Point of Care immunoenzymatic assay based on ready-to-use kits and a portable instrument to manage fluidics and read reliable quantitative results. Thanks to miniaturization, analyses are faster and more sensible than conventional ELISA. EliChip is one of the crucial assets of the Europen-founded Flamingo project for in-line measuring inflammatory markers.

Keywords: lab on chip, point of care, immunoenzymatic analysis, synovial arthritis

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Authors: Giovanni Gonzalez Araujo, Michael Kyrilov, Angelo Kyrilov

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STEAMplug is a web-based innovative educational platform which makes teaching easier and learning more effective. It requires no setup, eliminating the barriers to entry, allowing students to focus on their learning throughreal-world development environments. The student-centric tools enable easy collaboration between peers and teachers. Analyzing user interactions with the system enables us to predict student performance and identify at-risk students, allowing early instructor intervention.

Keywords: plagiarism detection, identifying at-Risk Students, education technology, e-learning system, collaborative development, learning and teaching with technology

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Authors: Ekaterina Tikhonova

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This research is aimed to evaluate the effectiveness of T&D (Training and Development) on the example of two T&D programs for the Executive TOP Management run in 2012, 2015-2016 in Komos Group. This study is commissioned to research the effectiveness of two similar corporate T&D programs (within one company) in two periods of time (2012, 2015-2016) through evaluating the programs’ effectiveness using the four-level Kirkpatrick’s model of evaluating T&D programs and calculating ROI as an instrument for T&D program measuring by Phillips’ formula. The research investigates the correlation of two figures: the ROI calculated and the rating percentage scale per the ROI implementation (Wagle’s scale). The study includes an assessment of feedback 360 (Kirkpatrick's model) and Phillips’ ROI Methodology that provides a step-by-step process for collecting data, summarizing and processing the collected information. The data is collected from the company accounting data, the HR budgets, MCFO and the company annual reports for the research periods. All analyzed data and reports are organized and presented in forms of tables, charts, and graphs. The paper also gives a brief description of some constrains of the research considered. After ROI calculation, the study reveals that ROI ranges between the average implementation (65% to 75%) by Wagle’s scale that can be considered as a positive outcome. The paper also gives some recommendations how to use ROI in practice and describes main benefits of ROI implementation.

Keywords: ROI, organizational performance, efficacy of T&D program, employee performance

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Authors: Yoftahe Nigussie Worku

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This study focused on designing a Fire tube boiler to generate saturated steam with a 2000kg/h capacity at a 12bar design pressure. The primary project goal is to achieve efficient steam production while minimizing costs. This involves selecting suitable materials for component parts, employing cost-effective construction methods, and optimizing various parameters. The analysis phase employs iterative processes and relevant formulas to determine key design parameters. This includes optimizing the diameter of tubes for overall heat transfer coefficient, considering a two-pass configuration due to tube and shell size, and using heavy oil fuel no.6 with specific heating values. The designed boiler consumes 140.37kg/hr of fuel, producing 1610kw of heat at an efficiency of 85.25%. The fluid flow is configured as cross flow, leveraging its inherent advantages. The tube arrangement involves welding the tubes inside the shell, which is connected to the tube sheet using a combination of gaskets and welding. The design of the shell adheres to the European Standard code for pressure vessels, accounting for weight and supplementary accessories and providing detailed drawings for components like lifting lugs, openings, ends, manholes, and supports.

Keywords: efficiency, coefficient, saturated steam, fire tube

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Authors: Marcio Freire Cruz, Naoaki Ono, Shigehiko Kanaya, Carlos Arthur Mattos Teixeira Cavalcante

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The systems that record patient care information, known as Electronic Medical Record (EMR) and those that monitor vital signs of patients, such as heart rate, body temperature, and blood pressure have been extremely valuable for the effectiveness of the patient’s treatment. Several kinds of research have been using data from EMRs and vital signs of patients to predict illnesses. Among them, we highlight those that intend to predict, classify, or, at least identify patterns, of sepsis illness in patients under vital signs monitoring. Sepsis is an organic dysfunction caused by a dysregulated patient's response to an infection that affects millions of people worldwide. Early detection of sepsis is expected to provide a significant improvement in its treatment. Preceding works usually combined medical, statistical, mathematical and computational models to develop detection methods for early prediction, getting higher accuracies, and using the smallest number of variables. Among other techniques, we could find researches using survival analysis, specialist systems, machine learning and deep learning that reached great results. In our research, patients are modeled as points moving each hour in an n-dimensional space where n is the number of vital signs (variables). These points can reach a sepsis target point after some time. For now, the sepsis target point was calculated using the median of all patients’ variables on the sepsis onset. From these points, we calculate for each hour the position vector, the first derivative (velocity vector) and the second derivative (acceleration vector) of the variables to evaluate their behavior. And we construct a prediction model based on a Long Short-Term Memory (LSTM) Network, including these derivatives as explanatory variables. The accuracy of the prediction 6 hours before the time of sepsis, considering only the vital signs reached 83.24% and by including the vectors position, speed, and acceleration, we obtained 94.96%. The data are being collected from Medical Information Mart for Intensive Care (MIMIC) Database, a public database that contains vital signs, laboratory test results, observations, notes, and so on, from more than 60.000 patients.

Keywords: dynamic analysis, long short-term memory, prediction, sepsis

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Authors: Sofia Meacham

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In this abstract paper, the Domain-Specific Languages (DSL) evaluation will be presented based on existing literature and years of experience developing DSLs for several domains. The domains we worked on ranged from AI, business applications, and finances/accounting to health. In general, DSLs have been utilised in many domains to provide tailored and efficient solutions to address specific problems. Although they are a reputable method among highly technical circles and have also been used by non-technical experts with success, according to our knowledge, there isn’t a commonly accepted method for evaluating them. There are some methods that define criteria that are adaptations from the general software engineering quality criteria. Other literature focuses on the DSL usability aspect of evaluation and applies methods such as Human-Computer Interaction (HCI) and goal modeling. All these approaches are either hard to introduce, such as the goal modeling, or seem to ignore the domain-specific focus of the DSLs. From our experience, the DSLs have domain-specificity in their core, and consequently, the methods to evaluate them should also include domain-specific criteria in their core. The domain-specific criteria would require synergy between the domain experts and the DSL developers in the same way that DSLs cannot be developed without domain-experts involvement. Methods from agile and other software engineering practices, such as co-creation workshops, should be further emphasised and explored to facilitate this direction. Concluding, our latest experience and plans for DSLs evaluation will be presented and open for discussion.

Keywords: domain-specific languages, DSL evaluation, DSL usability, DSL quality metrics

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Authors: Roscoe McLean, Jared Padayachee, Glen Bright

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There is currently a gap in the technology covering the rapid establishment of control after a reconfiguration in a Reconfigurable Manufacturing System. This gap involves the detection of the factory floor state and the communication link between the factory floor and the high-level software. In this paper, a thin, hardware-supported Middleware Management System (MMS) is proposed and its design and implementation are discussed. The research found that a cost-effective localization technique can be combined with intelligent software to speed up the ramp-up of a reconfigured system. The MMS makes the process more intelligent, more efficient and less time-consuming, thus supporting the industrial implementation of the RMS paradigm.

Keywords: intelligent systems, middleware, reconfigurable manufacturing, management system

Procedia PDF Downloads 676

Authors: Benjamin O. Ehigie, Aderemi Alarape, Nyitor Shenge, Sylvester A. Okhakhume, Timileyin Fashola, Fiyinfunjah Dosumu

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Organisational citizenship behaviour (OCB) is a construct in industrial and organisational behaviour that explains a person's voluntary commitment within an organisation, which is outside the scope of his or her contractual tasks. To attain organisational effectiveness the human factor of production is inevitable, hence the importance of employee behaviour. While the concept of organisational citizenship behavior is mostly discussed in the context of the workplace, it is reasoned that the idea could be reflective in relation to national commitment. Many developing countries in Africa, including Nigeria, suffer economic hardship today not necessarily due to poor resources but bad management of the resources. The mangers of their economies are not committed to the tenets of economic growth but engrossed in fraud, corruption, bribery, and other economic vices. It is this backdrop that necessitated the development and validation of the Ibadan-Nigeria Citizenship Behaviour (I-NCB) Scale. The study adopted a cross-sectional survey (online) research design, using 2404 postgraduate students in the Premier University of the country, with 99.2% being Nigerians and 0.8% non-Nigerians. Gender composition was 1,439 (60%) males and 965 (40%) females, 1201 (50%) were employed while 1203 50% unemployed, 74.2% of the employed were in public paid employment, 19.5% in private sector, and 6.3% were self-employed. Through literature review, 78 items were generated. Using 10 lecturers and 21 students, content and face validity were established respectively. Data collected were subjected to reliability and factor analytic statistics at p < .05 level of significance. Results of the content and face validity at 80% level of item acceptance resulted to 60 items; this was further reduced to 50 after item-total correlation using r=.30 criterion. Divergent validity of r= -.28 and convergent validity of r= .44 were obtained by correlating the I-NCB scale with standardized Counterproductive work behaviour (CWB) scale and Organisational Citizenship Behaviour (OCB) scale among the workers. The reliability coefficients obtained were; Cronbach alpha of internal consistency (α = 0.941) and split-half reliability of r = 0.728. Factor analyses of the I-NCB scale with principal component and varimax rotation yielded five factors when Eigenvalue above 1 were extracted. The factors which accounted for larger proportions of the total variance were given factor names as; Altruistic, Attachment, Affective, Civic responsibility and Allegiance. As much as there are vast journals on citizenship behaviour in organisations, there exists no standardized tool to measure citizenship behaviour of a country. The Ibadan-Nigeria Citizenship Behaviour (I-NCB) scale was consequently developed. The scale could be used to select personnel into political positions and senior administrative positions among career workers in Nigeria, with the aim of determining national commitment to service.

Keywords: counterproductive work behaviour, CWB, Nigeria Citizenship Behaviour, organisational citizenship behaviour, OCB, Ibadan

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Authors: Ian Kingsbury

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The rapid expansion of charter schools (public schools that receive government but do not face the same regulations as traditional public schools) over the preceding two decades has raised concerns over the potential for graft and fraud. These concerns are largely justified: Incidents of financial crime and mismanagement are not unheard of, and the charter sector has become a darling of hedge fund managers. In response, several states have strengthened their charter school regulatory regimes. Imposing regulations and attempting to increase accountability seem like sensible measures, and perhaps they are necessary. However, increased regulation may come at the cost of imposing barriers to entry. Specifically, increased regulation often entails evidence for a high likelihood of fiscal solvency. That should theoretically entail access to capital in the short-term, which may systematically preclude Black or Hispanic applicants from opening charter schools. Moreover, increased regulation necessarily entails more red tape. The institutional wherewithal and the number of hours required to complete an application to open a charter school might favor those who have partnered with an education service provider, specifically a charter management organization (CMO) or education management organization (EMO). These potential barriers to entry pose a significant policy concern. Just as policymakers hope to increase the share of minority teachers and principals, they should sensibly care whether individuals who open charter schools look like the students in that school. Moreover, they might be concerned if successful applications in states with stringent regulations are overwhelmingly affiliated with education service providers. One of the original missions of charter schools was to serve as a laboratory of innovation. Approving only those applications affiliated with education service providers (and in effect establishing a parallel network of schools rather than a diverse marketplace of schools) undermines that mission. Data and methods: The analysis examines more than 2,000 charter school applications from 15 states. It compares the outcomes of applications from states with a strong regulatory environment (those with high scores) from NACSA-the National Association of Charter School Authorizers- to applications from states with a weak regulatory environment (those with a low NACSA score). If the hypothesis is correct, applicants not affiliated with an ESP are more likely to be rejected in high-regulation states compared to those affiliated with an ESP, and minority candidates not affiliated with an education service provider (ESP) are particularly likely to be rejected. Initial returns indicate that the hypothesis holds. More applications in low NASCA-scoring Arizona come from individuals not associated with an ESP, and those individuals are as likely to be accepted as those affiliated with an ESP. On the other hand, applicants in high-NACSA scoring Indiana and Ohio are more than 20 percentage points more likely to be accepted if they are affiliated with an ESP, and the effect is particularly pronounced for minority candidates. These findings should spur policymakers to consider the drawbacks of charter school accountability and consider accountability regimes that do not impose barriers to entry.

Keywords: accountability, barriers to entry, charter schools, choice

Procedia PDF Downloads 159

Authors: Tugce Arslan

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For a communicative act to be carried out successfully, the speaker and the listener must consider certain principles in line with the intention–centered “Cooperative Principle” expressed by H. P. Grice. Violation of a communication principle causes the listener to make new inferences called “implicatures”. In this study, focusing on the linguistic use of H. P. Grice’s principles, we aim to find out which principles of conversation are generally followed in case files from different fields and which principles are frequently violated. Three case files were examined, and the violating and the abiding cases of the maxims were classified in terms of four categories (Quality, Quantity, Relevance and Manner). The results of this investigation is reported below (V: Violating, A: Abiding): Quality Quantity Relevance Manner V A V A V A V A Case 1 10 8 5 9 3 15 16 6 Case 2 4 5 11 6 2 11 7 14 Case 3 21 13 7 12 9 14 15 9 Total 35 26 23 27 14 40 38 29 The excerpts were selected from files covering three different areas: the Assize Court, the Family Court and the Commercial Court of First Instance. In this way, the relations between the types of violations and the types of courts are examined. Our main finding is that in the 1st and the 3rd file, as the cases of violation in “Quality” and “Manner” increase, the cases of violation in “Quantity” and “Relevance” decrease. In the second file, on the other hand, as the cases of violation in “Quantity” increase, the cases of violation in “Quality”, “Relevance” and “Manner” decrease. In the talk, we shall compare these results with the results obtained in the study of Tajabadi, Dowlatabadi, and Mehric (2014), which examined various case files in Iran. Our main finding is that in the study conducted in Iran, violations were found only on the principles of “Quantity” and “Relevance”, while violations were found on the principles of “Quality”, “Quantity” and “Manner” in this study. In this case, it shows us that there is a connection between at least two maxims. In both cases, it has been noticed that the “Quantity” maxim is a common denominator. Studies in this field can be enlightening for many areas such as discourse analysis, legal studies, etc. Accordingly, comments will be made about the nature of the violations mentioned in H. P. Grice’s “Cooperation Principle”. We shall also discuss various conversational practices that cannot be analysed with these maxims.

Keywords: comparative analysis, cooperation principle, forensic linguistics, pragmatic.

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Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

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This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

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Authors: Justo Rodriguez, Daming Chen, Amador M. Guzman

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The detection, treatment, and control of rapidly propagating, deadly viruses such as COVID-19, require the development of inexpensive, fast, and accurate devices to address the urgent needs of the population. Microfluidics-based sensors are amongst the different methods and techniques for detection that are easy to use. A micro analyzer is defined as a microfluidics-based sensor, composed of a network of microchannels with varying functions. Given their size, portability, and accuracy, they are proving to be more effective and convenient than other solutions. A micro analyzer based on the concept of “Lab on a Chip” presents advantages concerning other non-micro devices due to its smaller size, and it is having a better ratio between useful area and volume. The integration of multiple processes in a single microdevice reduces both the number of necessary samples and the analysis time, leading the next generation of analyzers for the health-sciences. In some applications, the flow of solution within the microchannels is originated by a pressure gradient, which can produce adverse effects on biological samples. A more efficient and less dangerous way of controlling the flow in a microchannel-based analyzer is applying an electric field to induce the fluid motion and either enhance or suppress the mixing process. Electrokinetic flows are characterized by no less than two non-dimensional parameters: the electric Rayleigh number and its geometrical aspect ratio. In this research, stable and unstable flows have been studied numerically (and when possible, will be experimental) in a T-shaped microchannel. Additionally, unstable electrokinetic flows for Rayleigh numbers higher than critical have been characterized. The flow mixing enhancement was quantified in relation to the stretching and folding that fluid particles undergo when they are subjected to supercritical electrokinetic flows. Computational simulations were carried out using a finite element-based program while working with the flow mixing concepts developed by Gollub and collaborators. Hundreds of seeded massless particles were tracked along the microchannel from the entrance to exit for both stable and unstable flows. After post-processing, their trajectories, the folding and stretching values for the different flows were found. Numerical results show that for supercritical electrokinetic flows, the enhancement effects of the folding and stretching processes become more apparent. Consequently, there is an improvement in the mixing process, ultimately leading to a more homogenous mixture.

Keywords: microchannel, stretching and folding, electro kinetic flow mixing, micro-analyzer

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Authors: Yunyong Guo, Man Wang, Bryan Guo, Nathan Guo

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This paper introduces an advanced security and privacy model tailored for Telehealth systems, emphasizing end-to-end protection across IoT, Fog, and Cloud components. The proposed model integrates encryption, key management, intrusion detection, and privacy-preserving measures to safeguard patient data. A comprehensive simulation study evaluates the model's effectiveness in scenarios such as unauthorized access, physical breaches, and insider threats. Results indicate notable success in detecting and mitigating threats yet underscore areas for refinement. The study contributes insights into the intricate balance between security and usability in Telehealth environments, setting the stage for continued advancements.

Keywords: cloud, enhancing security, fog, IoT, telehealth

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Authors: Chao-Ming Su, Pei-Sheng Wu, Yu-Chi Kuo, Yin-Chou Huang, Tan-Yueh Chen, Jefunnie Matahum, Tzong-Rong Ger

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Application of magnetic particles (MPs) has been applied in biomedical field for many years. There are lots of advantages through this mediator including high biocompatibility and multi-diversified bio-applications. However, current techniques for evaluating the quantity of the magnetic-labeled sample assays are rare. In this paper, a Wheatstone bridge giant magnetoresistance (GMR) sensor integrated with a homemade detecting system was fabricated and used to quantify the concentration of MPs. The homemade detecting system has shown high detecting sensitivity of 10 μg/μl of MPs with optimized parameter vertical magnetic field 100 G, horizontal magnetic field 2 G and flow rate 0.4 ml/min.

Keywords: magnetic particles, magnetoresistive sensors, microfluidics, biosensor

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Authors: Samabia Tehsin, Sumaira Kausar

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Due to the vast increase in the multimedia data in recent years, efficient and robust retrieval techniques are needed to retrieve and index images/ videos. Text embedded in the images can serve as the strong retrieval tool for images. This is the reason that text extraction is an area of research with increasing attention. English text extraction is the focus of many researchers but very less work has been done on other languages like Urdu. This paper is focusing on Urdu text extraction from video frames. This paper presents a text detection feature set, which has the ability to deal up with most of the problems connected with the text extraction process. To test the validity of the method, it is tested on Urdu news dataset, which gives promising results.

Keywords: caption text, content-based image retrieval, document analysis, text extraction

Procedia PDF Downloads 516