Search results for: European Standard Classification

Authors: Aferdita Dinaku, Jonida Canaj

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Aflatoxins are potentially toxic metabolites produced by certain kinds of fungi (molds) that are found naturally all over the world; they can contaminate food crops and pose a serious health threat to humans by mutagenic and carcinogenic effects. Several types of aflatoxin (14 or more) occur in nature. In Albanian nutrition, cereals (especially wheat and corn) are common ingredients in some traditional meals. This study aimed to investigate the presence of aflatoxins in the flour of wheat and maize that are consumed in Albania’s markets. The samples were collected randomly in different markets in Albania and detected by the ELISA method, measured in 450 nm. The concentration of total aflatoxins was analyzed by enzyme-linked immunosorbent assay (ELISA), and they were ranged between 0.05-1.09 ppb. However, the screened mycotoxin levels in the samples were lower than the maximum permissible limits of European Commission No 1881/2006 (4 μg/kg). The linearity of calibration curves was good for total aflatoxins (B1, B2, G1, G2, M1) (R²=0.99) in the concentration range 0.005-4.05 ppb. The samples were analyzed in two replicated measurements and for each sample, the standard deviation (statistical parameter) is calculated. The results showed that the flour samples are safe, but the necessity of performing such tests is necessary.

Keywords: aflatoxins, ELISA technique, food contamination, flour

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Authors: Ghada A. Alfattni

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Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

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Authors: Mitja Dečman

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The ever-increasing presence and use of information and communication technology (ICT) influences the different social relationships of today's society. Gender differences are especially important from the viewpoint of modern society since ICT can either deepen the existing inequalities or diminish them. In a developed Western world, gender equality has been a well-focused area for decades in many parts of society including education, employment or politics and has led to a decrease in the inequality of women and men in these and other areas. The area of digital equality, or inequality for that matter, is one of the areas where gender differences still exist in many countries of the world. The research presented in this paper focuses on Slovenia, one of the smallest EU member states, being an average achiever in the area of e-society according to the many different European benchmarking indexes. On the other hand, Slovenia is working in an alignment with many European gender equality guidelines and showing good results. The results of our research are based on the analysis of survey data from 2014 to 2017 dealing with Slovenian citizens and their households and the use of ICT. Considering gender issues, the synthesis showed that cultural differences influence some measured ICT indicators but on the other hand the differences are low and only sometimes statistically significant.

Keywords: digital divide, gender inequality, Slovenia, e-society

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Authors: Beate Niemann, Fabian Pramel

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The formerly industrially or militarily used Urban Waterfront is a potential area for urban development. Extensive interventions in the urban space come along with the development of these previously inaccessible areas in the city. The development of the Urban Waterfront in the European City is not subject to any recognizable urban paradigm. In this study, the development of the Urban Waterfront as a new urban space typology is analyzed by case studies of Urban Waterfront developments in European Cities. For humans, perceptible spatial conditions are categorized and it is identified whether the themed Urban Waterfront Developments are congruent or incongruent urban design interventions and which deviations the Urban Waterfront itself induce. As congruent urban design, a design is understood, which fits in the urban fabric regarding its similar spatial conditions to the surrounding. Incongruent urban design, however, shows significantly different conditions in its shape. Finally, the spatial relationship of the themed Urban Waterfront developments and their associated environment are compared in order to identify contrasts between new and old urban space. In this way, conclusions about urban design paradigms of the new urban space typology are tried to be drawn.

Keywords: composition, congruence, identity, paradigm, spatial condition, urban design, urban development, urban waterfront

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Authors: Chongya Song, Kang Yen, Alexander Pons, Jin Liu

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In the field of machine learning, the ensemble has been employed as a common methodology to improve the performance upon multiple base classifiers. However, the true predictions are often canceled out by the false ones during consensus due to a phenomenon called “curse of correlation” which is represented as the strong interferences among the predictions produced by the base classifiers. In addition, the existing practices are still not able to effectively mitigate the problem of imbalanced classification. Based on the analysis on our experiment results, we conclude that the two problems are caused by some inherent deficiencies in the approach of consensus. Therefore, we create an enhanced ensemble algorithm which adopts a designed rank-based chain-mode consensus to overcome the two problems. In order to evaluate the proposed ensemble algorithm, we employ a well-known benchmark data set NSL-KDD (the improved version of dataset KDDCup99 produced by University of New Brunswick) to make comparisons between the proposed and 8 common ensemble algorithms. Particularly, each compared ensemble classifier uses the same 22 base classifiers, so that the differences in terms of the improvements toward the accuracy and reliability upon the base classifiers can be truly revealed. As a result, the proposed rank-based chain-mode consensus is proved to be a more effective ensemble solution than the traditional consensus approach, which outperforms the 8 ensemble algorithms by 20% on almost all compared metrices which include accuracy, precision, recall, F1-score and area under receiver operating characteristic curve.

Keywords: consensus, curse of correlation, imbalance classification, rank-based chain-mode ensemble

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Authors: Jiabei Zhang

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Although adapted physical education (APE) teacher preparation programs are available in the nation, a consistent standards-based competency evaluation scale for preparing of qualified personnel for teaching children with disabilities in APE cannot be identified in the literature. The purpose of this study was to develop a standard-based competency evaluation scale for assessing qualifications for teaching children with disabilities in APE. Standard-based competencies were reviewed and identified based on research evidence documented as effective in teaching children with disabilities in APE. A standard-based competency scale was developed for assessing qualifications for teaching children with disabilities in APE. This scale included 20 standard-based competencies and a 4-point Likert-type scale for each standard-based competency. The first standard-based competency is knowledgeable of the causes of disabilities and their effects. The second competency is the ability to assess physical education skills of children with disabilities. The third competency is able to collaborate with other personnel. The fourth competency is knowledgeable of the measurement and evaluation. The fifth competency is to understand federal and state laws. The sixth competency is knowledgeable of the unique characteristics of all learners. The seventh competency is the ability to write in behavioral terms for objectives. The eighth competency is knowledgeable of developmental characteristics. The ninth competency is knowledgeable of normal and abnormal motor behaviors. The tenth competency is the ability to analyze and adapt the physical education curriculums. The eleventh competency is to understand the history and the philosophy of physical education. The twelfth competency is to understand curriculum theory and development. The thirteenth competency is the ability to utilize instructional designs and plans. The fourteenth competency is the ability to create and implement physical activities. The fifteenth competency is the ability to utilize technology applications. The sixteenth competency is to understand the value of program evaluation. The seventeenth competency is to understand professional standards. The eighteenth competency is knowledgeable of the focused instruction and individualized interventions. The nineteenth competency is able to complete a research project independently. The twentieth competency is to teach children with disabilities in APE independently. The 4-point Likert-type scale ranges from 1 for incompetent to 4 for highly competent. This scale is used for assessing if one completing all course works is eligible for receiving an endorsement for teaching children with disabilities in APE, which is completed based on the grades earned on three courses targeted for each standard-based competency. A mean grade received in three courses primarily addressing a standard-based competency will be marked on a competency level in the above scale. The level 4 is marked for a mean grade of A one receives over three courses, the level 3 for a mean grade of B over three courses, and so on. One should receive a mean score of 3 (competent level) or higher (highly competent) across 19 standard-based competencies after completing all courses specified for receiving an endorsement for teaching children with disabilities in APE. The validity, reliability, and objectivity of this standard-based competency evaluation scale are to be documented.

Keywords: evaluation scale, teacher preparation, adapted physical education teachers, and children with disabilities

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Authors: Sajid Ali, Dietrich Schröder

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The merging of internet and Web has created many disciplines and Web GIS is one these disciplines which is effectively dealing with the geospatial data in a proficient way. Web GIS technologies have provided an easy accessing and sharing of geospatial data over the internet. However, there is a single platform for easy and multiple accesses of the data lacks for the European Caribbean Association (Europaische Karibische Gesselschaft - EKG) to assist their members and other research community. The technique presented in this paper deals with designing of a geodatabase using PostgreSQL/PostGIS as an object oriented relational database management system (ORDBMS) for competent dissemination and management of spatial data and Web GIS by using OpenGeo Suite for the fast sharing and distribution of the data over the internet. The characteristics of the required design for the geodatabase have been studied and a specific methodology is given for the purpose of designing the Web GIS. At the end, validation of this Web based geodatabase has been performed over two Desktop GIS software and a web map application and it is also discussed that the contribution has all the desired modules to expedite further research in the area as per the requirements.

Keywords: desktop GISSoftware, European Caribbean association, geodatabase, OpenGeo suite, postgreSQL/PostGIS, webGIS, web map application

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Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: color space, neural network, random forest, skin detection, statistical feature

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Authors: Afaf Alharbi, Qianni Zhang

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The identification of malignant tissue in histopathological slides holds significant importance in both clinical settings and pathology research. This paper introduces a methodology aimed at automatically categorizing cancerous tissue through the utilization of a multiple-instance learning framework. This framework is specifically developed to acquire knowledge of the Bernoulli distribution of the bag label probability by employing neural networks. Furthermore, we put forward a neural network based permutation-invariant aggregation operator, equivalent to attention mechanisms, which is applied to the multi-instance learning network. Through empirical evaluation of an openly available colon cancer histopathology dataset, we provide evidence that our approach surpasses various conventional deep learning methods.

Keywords: attention multiple instance learning, MIL and transfer learning, histopathological slides, cancer tissue classification

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Authors: Yasser F. Hassan

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Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.

Keywords: cellular automata, neural cellular automata, deep learning, classification

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Authors: Daniel M. Barry

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Since more than half a century ago, attempts have been made to culture cells and embryos outside the body (in vitro or ex vivo). This was done with different culture media and in various “incubators”. In the present study two different culture media were used: a standard TCM-199 culture medium and first trimester amniotic fluid (BAF) collected sterilely from pregnant cows after slaughter. Two different culture conditions were also investigated, the standard laboratory CO2 incubator versus culturing bovine embryos in the vagina of a goat doe. Two experiments were done: Firstly the permeability of different receptacles to CO2 gas was analyzed for possible culture in the vagina. Four-well plates and straws were used to incubate TCM-199 and BAF for a period of 120 h in the presence or absence of 5% CO2 gas. The pH values were measured and recorded every 24 h. In the second experiment pre-compacted morula stage bovine embryos were cultured in the above culture media in sealed 0.25 mL straws in a standard laboratory incubator and in the vagina of a goat doe. Evaluation was done on (1) stage of development and (2) number of blastomeres after 96 h of culture. In the first experiment it was shown that the CO2 gas diffused out of the 4-well plate as well as through the wall of the straws in the absence of CO2 gas, while in the presence of CO2 the pH of both media stabilized between 7.3 and 7.5. This meant that the semen straws were permeable to CO2 gas and could therefore be used as receptacles for culturing early stage bovine embryos. In the second experiment no statistical differences (p>0.05) were found in the number of pre-compacted bovine embryos that developed to the blastocyst stage, or the hatched blastocyst stage, neither for the culture medium used, or the method of culturing in the two incubators. Neither was there any difference (p>0.05) in the number of blastomeres that developed at the blastocyst stage between the two types of incubators. The bovine embryos tended to develop more blastomeres when cultured in BAF than when cultured in TCM-199 in both the standard laboratory incubator and when using the vagina of a goat doe as an incubator.

Keywords: alternative culture, bovine embryos, vagina, bovine amniotic fluid, incubator

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Authors: Md. Abid Khan

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Arc Flash safety assessment is a critical component for continuous improvement of any company’s safe electrical arc flash standard (SEAFS). The standard requires periodic internal or external audits to verify compliance and assess implementation. Assessments will identify strengths and opportunities for improvement, and serve as the basis for corrective actions. An arc flash safety assessment is comprised of a review of any existing safe electrical arc flash standard documentation (e.g., such as work procedures or other supporting documents), onsite interviews, and observations (e.g., facility inspections and work task observations). Substation-based arc flash assessment is very popular as it is more specific for each substation. The enhanced department-based arc flash safety assessment will shift focus to more effective hazard control measures and emphasis will be placed on highlighting inherently unsafe equipment to support resolution actions by facility management, rather than relying on lessor effective control methods in the hierarchy of controls currently deployed at a number of facilities.

Keywords: assessment, remote racking device (RRD), key performance indicator (KPI), personal protective equipment (PPE), operation & maintenance (O&M), safety management system (SMS), safe electrical arc flash standard (SEAFS)

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Authors: Luigi Delle Rose, Oliver Fischer, Ahmed Hammad

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In this article, we study the prospects of the proposed Large Hadron electron Collider (LHeC) in the search for heavy neutral scalar particles. We consider a minimal model with one additional complex scalar singlet that interacts with the Standard Model (SM) via mixing with the Higgs doublet, giving rise to an SM-like Higgs boson and a heavy scalar particle. Both scalar particles are produced via vector boson fusion and can be tested via their decays into pairs of SM particles, analogously to the SM Higgs boson. Using multivariate techniques, we show that the LHeC is sensitive to heavy scalars with masses between 200 and 800 GeV down to scalar mixing of order 0.01.

Keywords: beyond the standard model, large hadron electron collider, multivariate analysis, scalar singlet

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Authors: Nguyen The Hoang Anh, Tran Huy Hoang, Vu Tat Thang, T. T. Quyen Bui

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Mental state classification is an important step for realizing a control system based on electroencephalography (EEG) signals which could benefit a lot of paralyzed people including the locked-in or Amyotrophic Lateral Sclerosis. Considering that EEG signals are nonstationary and often contaminated by various types of artifacts, classifying thoughts into correct mental states is not a trivial problem. In this work, our contribution is that we present and realize a novel model which integrates different techniques: Independent component analysis (ICA), relative wavelet energy, and support vector machine (SVM) for the same task. We applied our model to classify thoughts in two types of experiment whether with two or three mental states. The experimental results show that the presented model outperforms other models using Artificial Neural Network, K-Nearest Neighbors, etc.

Keywords: EEG, ICA, SVM, wavelet

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Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

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The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: foot recognition, deep learning, knee rehabilitation, convolutional neural network

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Authors: Arafat Husain

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The lubricants are one of the most used resource in today’s world. Lot of the superpowers are dependent on the lubricant resource for their country to function. To see that the lubricants are not adulterated we need to develop some efficient ways and to see which fluid has been added to the lubricant. So to observe the these malpractices in the lubricant we need to develop a method. We take a elastic ball and through it at probability circle in the submerged in the lubricant at a fixed force and see the distance of pitching and the point of fall. Then we the ratio of distance of falling to the distance of pitching and if the measured ratio is greater than one the fluid is less viscous and if the ratio is lesser than the lubricant is viscous. We will check the falling point of pure lubricant at fixed force and every pure lubricant would have a fixed falling point. After that we would adulterate the lubricant and note the falling point and if the falling point is less than the standard value then adulterate is solid and if the adulterate is liquid the falling point will be more than the standard value. Hence the comparison with the standard falling point will give the efficiency of the lubricant.

Keywords: falling point of lubricant, falling point ratios, probability circle, octane number

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Authors: Iran Kalbasi, Foroozandeh Zardashti

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Dialectology is a branch of social linguistics that studies systematic language variations. Dialects are the branches of a unique language that have structural, morphological and phonetic differences with each other. In Iran, these dialects and language variations themselves have a lot of cultural loads, and studying them have linguistic and cultural importance. In this study, phonetic structure of two Iranian dialects, Bakhtiyari Lori of Masjedsoleyman and Shushtari in Khuzestan Province of Iran have been surveyed. Its statistical community includes twenty speakers of two dialects. The theoretic bases of this research is based on structuralism. Its data have been collected by interviewing the questionnaire that consist of 3000 words, 410 sentences and 110 complex and simple verbs. These datas are analysed and described synchronically. Then, the phonetic characteristics of these two dialects and standard Persian have been compared. Therefore, we can say that in phonetic level of these two dialects and standard Persian, there are clearly differences.

Keywords: standard language, dialectology, bakhtiyari lori dialect of Masjedsoleyman, Shushtari dialect, vowel, consonant

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

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Authors: Arbolino Roberta, Boffardi Raffaele

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Achieving the highest effectiveness for the National Plans for Recovery and Resilience (NPRR) while strengthening the objectives of cohesion and reduction of intra-EU unbalances is only possible by means of strategic, coordinated, and coherent policy planning. Therefore, the present research aims at assessing and quantifying the potential impact of NPRRs across the twenty-seven European Member States in terms of economic convergence, considering disaggregated data on industrial, construction, and service sectors. The first step of the research involves a performance analysis of the main macroeconomic indicators describing the trends of twenty-seven EU economies before the pandemic outbreak. Subsequently, in order to define the potential effect of the resources allocated, we perform an impact analysis of previous similar EU investment policies, estimating national-level sectoral elasticity associated with the expenditure of the 2007-2013 and 2014-2020 Cohesion programmes funds. These coefficients are then exploited to construct adjustment scenarios. Finally, convergence analysis is performed on the data used for constructing scenarios in order to understand whether the expenditure of funds might be useful to foster economic convergence besides driving recovery. The results of our analysis show that the allocation of resources largely mirrors the aims of the policy framework underlying the NPRR, thus reporting the largest investments in both those sectors most affected by the economic shock (services) and those considered fundamental for the digital and green transition. Notwithstanding an overall positive effect, large differences exist among European countries, while no convergence process seems to be activated or fostered by these interventions.

Keywords: NPRR, policy evaluation, cohesion policy, scenario Nalsysi

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Authors: Lae-Jeong Park, Kyusoo Chung, Jungho Moon

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This paper introduces an effective method of segmenting Korean text (place names in Korean) from a Korean road sign image. A Korean advanced directional road sign is composed of several types of visual information such as arrows, place names in Korean and English, and route numbers. Automatic classification of the visual information and extraction of Korean place names from the road sign images make it possible to avoid a lot of manual inputs to a database system for management of road signs nationwide. We propose a series of problem-specific heuristics that correctly segments Korean place names, which is the most crucial information, from the other information by leaving out non-text information effectively. The experimental results with a dataset of 368 road sign images show 96% of the detection rate per Korean place name and 84% per road sign image.

Keywords: segmentation, road signs, characters, classification

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Authors: Itsaree Padphai, Jiranan Pakpeian, Suksun Niponchai

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This study is a descriptive research which aims to: 1) Compare the difference of knowledge before and after using the e-Learning program entitled “Basic Cardiopulmonary Resuscitation for Students in an Emergency Medical Technician Diploma Program”, and 2) Assess the students’ satisfaction after using the said program. This research is a kind of teaching and learning management supplemented with the e-Learning system; therefore, the purposively selected samples are 44 first-year and class-16 students of an emergency medical technician diploma program who attend the class in a second semester of academic year 2012 in Sirindhorn College of Public Health, Khon Kaen province. The research tools include 1) the questionnaire for general information of the respondents, 2) the knowledge tests before and after using the e-Learning program, and 3) an assessment of satisfaction in using the e-Learning program. The statistics used in data analysis percentage, include mean, standard deviation, and inferential statistics: paired t-test. 1. The general information of the respondents was mostly 37 females representing 84.09 percent. The average age was 19.5 years (standard deviation was 0.81), the maximum age was 21 years, and the minimum age was 19 years respectively. Students (35 subjects) admitted that they preferred the methods of teaching and learning by using the e-Learning systems. This was totally 79.95 percent. 2. A comparison on the difference of knowledge before and after using the e-Learning program showed that the mean before an application was 6.64 (standard deviation was 1.94) and after was 18.84 (standard deviation 1.03), which was higher than the knowledge of students before using the e-Learning program with the statistical significance (P value < 0.001). 3. For the satisfaction after using the e-Learning program, it was found that students’ satisfaction was at a very good level with the mean of 4.93 (standard deviation was 0.11).

Keywords: e-Learning, cardiopulmonary resuscitation, diploma program, Khon Kaen Province

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Authors: Sherrene Bogle, Verlia Bogle, Tyrone Anderson

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In recent years, organizations have become increasingly interested in the possibility of analyzing social media as a means of gaining meaningful feedback about their products and services. The aspect based sentiment analysis approach is used to predict the sentiment for Twitter datasets for Digicel and Lime, the main mobile companies in Jamaica, using supervised learning classification techniques. The results indicate an average of 82.2 percent accuracy in classifying tweets when comparing three separate classification algorithms against the purported baseline of 70 percent and an average root mean squared error of 0.31. These results indicate that the analysis of sentiment on social media in order to gain customer feedback can be a viable solution for mobile companies looking to improve business performance.

Keywords: machine learning, sentiment analysis, social media, supervised learning

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Authors: Meenakshi Barsaul Chauhan, Aastha Chauhan, Shilpa Hurmade, Rajeev Sen, Jyotsna Sen, Monika Dalal

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Introduction: Ovarian masses are common forms of neoplasm in women and represent 2/3rd of gynaecological malignancies. A pre-operative suggestion of malignancy can guide the gynecologist to refer women with suspected pelvic mass to a gynecological oncologist for appropriate therapy and optimized treatment, which can improve survival. In the younger age group preoperative differentiation into benign or malignant pathology can decide for conservative or radical surgery. Imaging modalities have a definite role in establishing the diagnosis. By using International Ovarian Tumor Analysis (IOTA) classification with sonography, costly radiological methods like Magnetic Resonance Imaging (MRI) / computed tomography (CT) scan can be reduced, especially in developing countries like India. Thus, this study is being undertaken to evaluate the role of clinical methods and sonography for diagnosis of the nature of the ovarian tumor. Material And Methods: This prospective observational study was conducted on 40 patients presenting with ovarian masses, in the Department of Obstetrics and Gynaecology, at a tertiary care center in northern India. Functional cysts were excluded. Ultrasonography and color Doppler were performed on all the cases.IOTA rules were applied, which take into account locularity, size, presence of solid components, acoustic shadow, dopper flow etc . Magnetic Resonance Imaging (MRI) / computed tomography (CT) scans abdomen and pelvis were done in cases where sonography was inconclusive. In inoperable cases, Fine needle aspiration cytology (FNAC) was done. The histopathology report after surgery and cytology report after FNAC was correlated statistically with the pre-operative diagnosis made clinically and sonographically using IOTA rules. Statistical Analysis: Descriptive measures were analyzed by using mean and standard deviation and the Student t-test was applied and the proportion was analyzed by applying the chi-square test. Inferential measures were analyzed by sensitivity, specificity, negative predictive value, and positive predictive value. Results: Provisional diagnosis of the benign tumor was made in 16(42.5%) and of the malignant tumor was made in 24(57.5%) patients on the basis of clinical findings. With IOTA simple rules on sonography, 15(37.5%) were found to be benign, while 23 (57.5%) were found to be malignant and findings were inconclusive in 2 patients (5%). FNAC/Histopathology reported that benign ovarian tumors were 14 (35%) and 26(65%) were malignant, which was taken as the gold standard. The clinical finding alone was found to have a sensitivity of 66.6% and a specificity of 90.9%. USG alone had a sensitivity of 86% and a specificity of 80%. When clinical findings and IOTA simple rules of sonography were combined (excluding inconclusive masses), the sensitivity and specificity were 83.3% and 92.3%, respectively. While including inconclusive masses, sensitivity came out to be 91.6% and specificity was 89.2. Conclusion: IOTA's simple sonography rules are highly sensitive and specific in the prediction of ovarian malignancy and also easy to use and easily reproducible. Thus, combining clinical examination with USG will help in the better management of patients in terms of time, cost and better prognosis. This will also avoid the need for costlier modalities like CT, and MRI.

Keywords: benign, international ovarian tumor analysis classification, malignant, ovarian tumours, sonography

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Authors: Silvius Stanciu

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Romanian food products sold on European markets have been accused of several non-conformities of quality and safety. Most products incriminated last period were those of animal origin, especially meat and meat products. The study proposed an analysis of the notifications made by network members through Rapid Alert System for Food and Feed on products originating in Romania. As a source of information, the Rapid Alert System portal and the official communications of the National Sanitary Veterinary and Food Safety Authority were used. The research results showed that nearly a quarter of network notifications were rejected and were withdrawn by the European Authority. Although national authorities present these issues as success stories of national quality policies, the large number of notifications related to the volume of exported products is worrying. The paper is of practical and applicative importance for both the business environment and the academic environment, laying the basis for a wider research on the quality differences between Romanian and imported products.

Keywords: food, quality, RASFF, Rapid Alert System for Food and Feed, Romania

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Authors: Olga Lasaga, Carmen Parra

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Within the EU 'New Skills for New Jobs' initiative, the art and music sector is considered one of the most vulnerable. Its graduates are faced with the threat of the dole or of not finding work in the sector in which they trained. In this regard, an increasing number of students are graduating every year from European Conservatories and Fine Arts Centres, while the number of job opportunities in this sector has stagnated or decreased. Moreover, the traditional teaching of these institutes does not favour the acquisition of basic skills, such as team building, entrepreneurship, marketing, website design and the design of events, which are among the most important facets of project management and are precisely those aspects that are often most related to the improvement of employability in the art world. To remedy this situation, the results of the European Erasmus+ OMEGA project (Opening More Employment Gates for Art and Music Students) are presented. The OMEGA project aims to increase the employability of art and music students by equipping them with additional skills needed for the search for work. As a result of this project, a manual has been created, a pilot course has been designed and taught, and a comparative study has been conducted on the state of play of the participating countries.

Keywords: artists, employability, entrepreneurship, musicians, skills

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Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas

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This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).

Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems

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Authors: Samedin Mehmeti

Abstract:

Very tens political and economic situation, in particular armed conflicts that followed at the time of the destruction of the former Yugoslavia, influenced migrations and displacement of population. Especially setting international sanctions and embargo influenced the creation of organized criminal groups. A lot of members of the former Yugoslav security apparatus in collaboration with ordinary criminal groups engaged in: smuggling of goods, petroleum and arms, sale and transport of drugs, payable murder, damage to public property, kidnappings, extortion, racketeering, etc. This tradition of criminality, of course in other forms and with other methods, has continued after conflicts and continues with a high intensity even in nowadays. One of the most delicate problems of organized crime activity is the impact on the economic sphere, where organized crime opposes and severely damages national security and economy to criminalize it in certain sectors and directions. Organized crime groups including who find Kosovo as a place to develop their criminal activities are characterized by: loyalty of many people especially through family connections and kinship in carrying out criminal activities and the existence of powerful hierarchy of leadership which in many cases include the corrupt officials of state apparatus. Groups have clear hierarchy and flexible structure of command, each member within the criminal group knows his duties concrete. According to statistics presented in police reports its notable that Kosovo has a large number of cases of organized crime, cultivation, trafficking and possession of narcotics. As already is very well known that one of the primary conditions that must be fulfilled on track toward integration in the European Union is precisely to prevent and combat organized crime. Kosovo has serious problems with prosecutorial and judicial system. But the misuse of public funds, even those coming directly from EU budget or the budget of the European Union member states, have a negative impact on this process. The economic crisis that has gripped some of the EU countries has led to the creation of an environment in which there are far fewer resources and opportunities to invest in preventing and combating organized crime within member states. This automatically reduces the level of financial support for other countries in the fight against organized crime. Kosovo as a poor country, now has less likely benefiting from the support tools that will be eventually offered by Europe set of in this area.

Keywords: police, european integration, organized crime, narcotics

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Authors: Piyush Swami, Bijaya Ketan Panigrahi, Sneh Anand, Manvir Bhatia, Tapan Gandhi

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Visual inspection of electroencephalogram (EEG) signals to detect epileptic signals is very challenging and time-consuming task even for any expert neurophysiologist. This problem is most challenging in under-developed and developing countries due to shortage of skilled neurophysiologists. In the past, notable research efforts have gone in trying to automate the seizure detection process. However, due to high false alarm detections and complexity of the models developed so far, have vastly delimited their practical implementation. In this paper, we present a novel scheme for epileptic seizure detection using empirical mode decomposition technique. The intrinsic mode functions obtained were then used to calculate the standard deviations. This was followed by probability density based classifier to discriminate between non-ictal and ictal patterns in EEG signals. The model presented here demonstrated very high classification rates ( > 97%) without compromising the statistical performance. The computation timings for each testing phase were also very low ( < 0.029 s) which makes this model ideal for practical applications.

Keywords: electroencephalogram (EEG), epilepsy, ictal patterns, empirical mode decomposition

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Authors: Mohammad Zamani, Ramin Mansouri

Abstract:

Spillways are one of the most important hydraulic structures of dams that provide the stability of the dam and downstream areas at the time of flood. A circular vertical spillway with various inlet forms is very effective when there is not enough space for the other spillway. Hydraulic flow in a vertical circular spillway is divided into three groups: free, orifice, and under pressure (submerged). In this research, the hydraulic flow characteristics of a Circular Vertical Spillway are investigated with the CFD model. Two-dimensional unsteady RANS equations were solved numerically using Finite Volume Method. The PISO scheme was applied for the velocity-pressure coupling. The mostly used two-equation turbulence models, k-ε and k-ω, were chosen to model Reynolds shear stress term. The power law scheme was used for the discretization of momentum, k, ε, and ω equations. The VOF method (geometrically reconstruction algorithm) was adopted for interface simulation. In this study, three types of computational grids (coarse, intermediate, and fine) were used to discriminate the simulation environment. In order to simulate the flow, the k-ε (Standard, RNG, Realizable) and k-ω (standard and SST) models were used. Also, in order to find the best wall function, two types, standard wall, and non-equilibrium wall function, were investigated. The laminar model did not produce satisfactory flow depth and velocity along the Morning-Glory spillway. The results of the most commonly used two-equation turbulence models (k-ε and k-ω) were identical. Furthermore, the standard wall function produced better results compared to the non-equilibrium wall function. Thus, for other simulations, the standard k-ε with the standard wall function was preferred. The comparison criterion in this study is also the trajectory profile of jet water. The results show that the fine computational grid, the input speed condition for the flow input boundary, and the output pressure for the boundaries that are in contact with the air provide the best possible results. Also, the standard wall function is chosen for the effect of the wall function, and the turbulent model k-ε (Standard) has the most consistent results with experimental results. When the jet gets closer to the end of the basin, the computational results increase with the numerical results of their differences. The mesh with 10602 nodes, turbulent model k-ε standard and the standard wall function, provide the best results for modeling the flow in a vertical circular Spillway. There was a good agreement between numerical and experimental results in the upper and lower nappe profiles. In the study of water level over crest and discharge, in low water levels, the results of numerical modeling are good agreement with the experimental, but with the increasing water level, the difference between the numerical and experimental discharge is more. In the study of the flow coefficient, by decreasing in P/R ratio, the difference between the numerical and experimental result increases.

Keywords: circular vertical, spillway, numerical model, boundary conditions

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Authors: Jiahe Liu, Hongyang Yu, Miao Luo, Feng Qian

Abstract:

The widespread application of 3D human body reconstruction spans various fields. Smplify-X, an algorithm reliant on single-image input, employs three distinct body parameter templates, necessitating gender classification of individuals within the input image. Researchers employed a ResNet18 network to train a gender classifier within the Smplify-X framework, setting the threshold at 0.9, designating images falling below this threshold as having neutral gender. This model achieved 62.38% accurate predictions and 7.54% incorrect predictions. Our improvement involved refining the MobileNet network, resulting in a raised threshold of 0.97. Consequently, we attained 78.89% accurate predictions and a mere 0.2% incorrect predictions, markedly enhancing prediction precision and enabling more precise 3D human body reconstruction.

Keywords: SMPLX, mobileNet, gender classification, 3D human reconstruction

Procedia PDF Downloads 81