Search results for: European Standard Classification

Authors: Bernd Kiefer, Abraham Gebru Tesfay, Dietrich Klakow

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The motivation of our work is to detect different terrain types traversed by a robot based on acoustic data from the robot-terrain interaction. Different acoustic features and classifiers were investigated, such as Mel-frequency cepstral coefficient and Gamma-tone frequency cepstral coefficient for the feature extraction, and Gaussian mixture model and Feed forward neural network for the classification. We analyze the system’s performance by comparing our proposed techniques with some other features surveyed from distinct related works. We achieve precision and recall values between 87% and 100% per class, and an average accuracy at 95.2%. We also study the effect of varying audio chunk size in the application phase of the models and find only a mild impact on performance.

Keywords: acoustic features, autonomous robots, feature extraction, terrain classification

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Authors: Mohamed R. Mhereeg

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The paper discusses the implementation of the MultiAgent classification System (MACS) and utilizing it to provide an automated and accurate classification of end users developing applications in the spreadsheet domain. However, different technologies have been brought together to build MACS. The strength of the system is the integration of the agent technology with the FIPA specifications together with other technologies, which are the .NET widows service based agents, the Windows Communication Foundation (WCF) services, the Service Oriented Architecture (SOA), and Oracle Data Mining (ODM). Microsoft's .NET windows service based agents were utilized to develop the monitoring agents of MACS, the .NET WCF services together with SOA approach allowed the distribution and communication between agents over the WWW. The Monitoring Agents (MAs) were configured to execute automatically to monitor excel spreadsheets development activities by content. Data gathered by the Monitoring Agents from various resources over a period of time was collected and filtered by a Database Updater Agent (DUA) residing in the .NET client application of the system. This agent then transfers and stores the data in Oracle server database via Oracle stored procedures for further processing that leads to the classification of the end user developers.

Keywords: MACS, implementation, multi-agent, SOA, autonomous, WCF

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Authors: Rim Messaoudi, Nogaye-Gueye Gning, François Azelart

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Automatic text classification applies mostly natural language processing (NLP) and other AI-guided techniques to automatically classify text in a faster and more accurate manner. This paper discusses the subject of using predictive maintenance to manage incident tickets inside the sociality. It focuses on proposing a tool that treats and analyses comments and notes written by administrators after resolving an incident ticket. The goal here is to increase the quality of these comments. Additionally, this tool is based on NLP and machine learning techniques to realize the textual analytics of the extracted data. This approach was tested using real data taken from the French National Railways (SNCF) company and was given a high-quality result.

Keywords: machine learning, text classification, NLP techniques, semantic representation

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Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: Nitin Shravan, Sudarsun Santhiappan, B. Sivaselvan

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Subsection identification, in the context of Electronic Health Records (EHRs), is identifying the important sections for down-stream tasks like auto-coding. In this work, we classify the text present in EHRs according to their information, using machine learning and deep learning techniques. We initially describe briefly about the problem and formulate it as a text classification problem. Then, we discuss upon the methods from the literature. We try two approaches - traditional feature extraction based machine learning methods and deep learning methods. Through experiments on a private dataset, we establish that the deep learning methods perform better than the feature extraction based Machine Learning Models.

Keywords: deep learning, machine learning, semantic clinical classification, subsection identification, text classification

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Authors: Alberto Piatto, Zaza Nadja Lee Hansen, Peter Jacobsen

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This paper examines the quantitative and qualitative factors influencing the buyer-supplier relationship. Understanding and acting on the right factors influencing supplier relationship management is crucial when a company outsource an important part of its business as it can be for engineering to order (ETO) company executing only the designing part in-house. Acting on these factors increase the quality of the relationship obtaining for both parties what they want and expect from an improved relationship. Best practices in supplier relationship management are considered and a case study of a large global company, called Cement A/S, operating in the cement business is carried out. One study is conducted including a large international company and hundreds of its suppliers. Data from the company is collected using semi-structured interviews and data from the suppliers is collected using a survey. Based on these inputs and an extensive literature review a classification of factors influencing the relationship buyer-supplier is presented and discussed. The results show that different managers among the company are assessing supplier from various perspectives, a standard approach to measure the performance of suppliers does not exist. The factors used nowadays in the company to measure performances of the suppliers are mostly related to time and cost. Quality is a key factor, but it has not been addressed properly since no data are available in the system. From a practical perspective, managers can learn from this paper which factors to consider when applying best practices of Supplier Relationship Management. Furthermore, from a theoretical perspective, this paper contributes with new knowledge in the area as limited research in collaboration with the company has been conducted. For this reason, a company, its suppliers and few studies for this type of industry have been conducted. For further research, it is suggested to define the correlation of factors to the profitability of the company and calculate its impact. When conducting this analysis it is important to focus on the efficient and effective use of factors that can be measurable and accepted from the supplier.

Keywords: buyer-supplier relationship, cement industry, classification of factors, ETO

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Authors: Barbora Herdová, Rastislav Lagaňa

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The paper investigates the mechanical properties of European aspen (Populus tremula L.) as a potential replacement for load-bearing elements in historical structures. One of the main aims of the research has been the quantification of mechanical properties via destructive testing and the subsequent calculation of characteristic values of these properties. The research encompasses experimental testing of wood specimens for the determination of dynamic modulus of elasticity (MOEdyn), modulus of elasticity (MOE), modulus of rupture (MOR), and density. The results were analyzed and compared to established standards for structural timber. The results confirmed statistically significant dependence between MOR and MOEdyn. The correlation between the MOR and the dynamic MOEdyn enabled non-destructive strength grading using the Sylvatest Duo® system. The findings of this research contribute to the potential use of European aspen as a structural timber, which could have implications for the sustainable use of this abundant and renewable resource in the construction industry. They also show the usability of European aspen in the reconstruction of historical buildings.

Keywords: populus tremula, MOE, MOR, sylvatest Duo®.

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Authors: Khairani Binti Supyan, Fatimah Khalid, Mas Rina Mustaffa, Azreen Bin Azman, Amirul Azuani Romle

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Perennial plant classification plays a significant role in various agricultural and environmental applications, assisting in plant identification, disease detection, and biodiversity monitoring. Nevertheless, attaining high accuracy in perennial plant image classification remains challenging due to the complex variations in plant appearance, the diverse range of environmental conditions under which images are captured, and the inherent variability in image quality stemming from various factors such as lighting conditions, camera settings, and focus. This paper proposes an adaptation approach to optimize perennial plant image classification by fine-tuning the pre-trained DNNs model. This paper explores the efficacy of fine-tuning prevalent architectures, namely VGG16, ResNet50, and InceptionV3, leveraging transfer learning to tailor the models to the specific characteristics of perennial plant datasets. A subset of the MYLPHerbs dataset consisted of 6 perennial plant species of 13481 images under various environmental conditions that were used in the experiments. Different strategies for fine-tuning, including adjusting learning rates, training set sizes, data augmentation, and architectural modifications, were investigated. The experimental outcomes underscore the effectiveness of fine-tuning deep neural networks for perennial plant image classification, with ResNet50 showcasing the highest accuracy of 99.78%. Despite ResNet50's superior performance, both VGG16 and InceptionV3 achieved commendable accuracy of 99.67% and 99.37%, respectively. The overall outcomes reaffirm the robustness of the fine-tuning approach across different deep neural network architectures, offering insights into strategies for optimizing model performance in the domain of perennial plant image classification.

Keywords: perennial plants, image classification, deep neural networks, fine-tuning, transfer learning, VGG16, ResNet50, InceptionV3

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Authors: Moohyun Lee, Soojung Hur, Yongwan Park

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In this paper is proposed a method uses only LIDAR system to classification an obstacle and determine its type by establishing database for classifying obstacles based on LIDAR. The existing LIDAR system, in determining the recognition of obstruction in an autonomous vehicle, has an advantage in terms of accuracy and shorter recognition time. However, it was difficult to determine the type of obstacle and therefore accurate path planning based on the type of obstacle was not possible. In order to overcome this problem, a method of classifying obstacle type based on existing LIDAR and using the width of obstacle materials was proposed. However, width measurement was not sufficient to improve accuracy. In this research, the width data was used to do the first classification; database for LIDAR intensity data by four major obstacle materials on the road were created; comparison is made to the LIDAR intensity data of actual obstacle materials; and determine the obstacle type by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that data declined in quality in comparison to 3D LIDAR and it was possible to classify obstacle materials using 2D LIDAR.

Keywords: obstacle, classification, database, LIDAR, segmentation, intensity

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Authors: Shajida M., Sakthiyadharshini N. P., Kamalesh S., Aswitha B.

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Natural Language Processing (NLP) continues to play a strategic part in complaint discovery and medicine discovery during the current epidemic. This abstract provides an overview of performance analysis with a combination of visualization and classification techniques of NLP for a medical chatbot. Sentiment analysis is an important aspect of NLP that is used to determine the emotional tone behind a piece of text. This technique has been applied to various domains, including medical chatbots. In this, we have compared the combination of the decision tree with heatmap and Naïve Bayes with Word Cloud. The performance of the chatbot was evaluated using accuracy, and the results indicate that the combination of visualization and classification techniques significantly improves the chatbot's performance.

Keywords: sentimental analysis, NLP, medical chatbot, decision tree, heatmap, naïve bayes, word cloud

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Authors: Babak Bashari Rad

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A metamorphic computer virus uses different code transformation techniques to mutate its body in duplicated instances. Characteristics and function of new instances are mostly similar to their parents, but they cannot be easily detected by the majority of antivirus in market, as they depend on string signature-based detection techniques. The purpose of this research is to propose a Hidden Markov Model for classification of metamorphic viruses in executable files. In the proposed solution, portable executable files are inspected to extract the instructions opcodes needed for the examination of code. A Hidden Markov Model trained on portable executable files is employed to classify the metamorphic viruses of the same family. The proposed model is able to generate and recognize common statistical features of mutated code. The model has been evaluated by examining the model on a test data set. The performance of the model has been practically tested and evaluated based on False Positive Rate, Detection Rate and Overall Accuracy. The result showed an acceptable performance with high average of 99.7% Detection Rate.

Keywords: malware classification, computer virus classification, metamorphic virus, metamorphic malware, Hidden Markov Model

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Authors: Xiao Chen, Xiaoying Kong, Min Xu

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This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: vehicle classification, signal processing, road traffic model, magnetic sensing

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Authors: M. C. Paliwal, A. K. Jain, S. K. Katiyar

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Classification of satellite imagery is very important for the assessment of its accuracy. In order to determine the accuracy of the classified image, usually the assumed-true data are derived from ground truth data using Global Positioning System. The data collected from satellite imagery and ground truth data is then compared to find out the accuracy of data and error matrices are prepared. Overall and individual accuracies are calculated using different methods. The study illustrates advanced classification and accuracy assessment of land use/land cover mapping using satellite imagery. IRS-1C-LISS IV data were used for classification of satellite imagery. The satellite image was classified using the software in fourteen classes namely water bodies, agricultural fields, forest land, urban settlement, barren land and unclassified area etc. Classification of satellite imagery and calculation of accuracy was done by using ERDAS-Imagine software to find out the best method. This study is based on the data collected for Bhopal city boundaries of Madhya Pradesh State of India.

Keywords: resolution, accuracy assessment, land use mapping, satellite imagery, ground truth data, error matrices

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Authors: Chen Wang, Chun Liang

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Microsatellite instability (MSI) is characterized by high degree of polymorphism in microsatellite (MS) length due to a deficiency in mismatch repair (MMR) system. MSI is associated with several tumor types and its status can be considered as an important indicator for tumor prognostic. Conventional clinical diagnosis of MSI examines PCR products of a panel of MS markers using electrophoresis (MSI-PCR) which is laborious, time consuming, and less reliable. MSIpred, a python 2 package for automatic classification of MSI was released by this study. It computes important somatic mutation features from files in mutation annotation format (MAF) generated from paired tumor-normal exome sequencing data, subsequently using these to predict tumor MSI status with a support vector machine (SVM) classifier trained by MAF files of 1074 tumors belonging to four types. Evaluation of MSIpred on an independent 358-tumor test set achieved overall accuracy of over 98% and area under receiver operating characteristic (ROC) curve of 0.967. These results indicated that MSIpred is a robust pan-cancer MSI classification tool and can serve as a complementary diagnostic to MSI-PCR in MSI diagnosis.

Keywords: microsatellite instability, pan-cancer classification, somatic mutation, support vector machine

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Authors: Chih-Fong Tsai, Ya-Han Hu

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The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets.

Keywords: data mining, feature selection, pattern classification, dimensionality reduction

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Authors: Aleksandra Nocoń (Szunke)

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The study is a review of the literature concerning the consequences of non-standard monetary policy, which are used by central banks during unconventional periods, threatening instability of the banking sector. In particular, the attention was paid to the effects of non-standard monetary policy tools for financial markets. However, the empirical evidence about their effects and real consequences for the financial markets are still not final. The main aim of the study is to survey the consequences of standard and non-standard monetary policy instruments, implemented during the global financial crisis in the United States, United Kingdom and Euroland, with particular attention to the results for the stabilization of global financial markets. The study analyses the consequences for short and long-term market interest rates, interbank interest rates and LIBOR-OIS spread. The study consists mainly of the empirical review, indicating the impact of the implementation of these tools for the financial markets. The following research methods were used in the study: literature studies, including domestic and foreign literature, cause and effect analysis and statistical analysis.

Keywords: asset purchase facility, consequences of monetary policy instruments, non-standard monetary policy, quantitative easing

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Authors: Strahinja Z. Kovačević, Lidija R. Jevrić, Sanja O. Podunavac Kuzmanović

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The relationship between antibacterial activity of eighteen different substituted benzimidazole derivatives and their molecular characteristics was studied using chemometric QSAR (Quantitative Structure–Activity Relationships) approach. QSAR analysis has been carried out on inhibitory activity towards Staphylococcus aureus, by using molecular descriptors, as well as minimal inhibitory activity (MIC). Molecular descriptors were calculated from the optimized structures. Principal component analysis (PCA) followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR) was performed in order to select molecular descriptors that best describe the antibacterial behavior of the compounds investigated, and to determine the similarities between molecules. The HCA grouped the molecules in separated clusters which have the similar inhibitory activity. PCA showed very similar classification of molecules as the HCA, and displayed which descriptors contribute to that classification. MLR equations, that represent MIC as a function of the in silico molecular descriptors were established. The statistical significance of the estimated models was confirmed by standard statistical measures and cross-validation parameters (SD = 0.0816, F = 46.27, R = 0.9791, R2CV = 0.8266, R2adj = 0.9379, PRESS = 0.1116). These parameters indicate the possibility of application of the established chemometric models in prediction of the antibacterial behaviour of studied derivatives and structurally very similar compounds.

Keywords: antibacterial, benzimidazole, molecular descriptors, QSAR

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Authors: Prisecaru Diana-Sorina

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The paper will show that, while the EU legislator tackled a series of UX patterns used in e-commerce to induce the consumers take actions that they would not normally undertake, it leaves out many other aspects related to misuse or poor UX design that adversely affect EU consumers. Further, the paper proposes a reevaluation of the regulatory addressability of the issue and hand and focuses on explaining why a joint strategy, based on the interplay between provisions aiming consumer protection and personal data protection is the key approach to this matter.

Keywords: algorithms, consumer protection, European Union, user experience design

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Authors: Khaled Moaddy

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In this paper, we present a fast and accurate numerical scheme for the solution of a Laplace equation with Dirichlet boundary conditions. The non-standard finite difference scheme (NSFD) is applied to construct the numerical solutions of a Laplace equation with two different Dirichlet boundary conditions. The solutions obtained using NSFD are compared with the solutions obtained using the standard finite difference scheme (SFD). The NSFD scheme is demonstrated to be reliable and efficient.

Keywords: standard finite difference schemes, non-standard schemes, Laplace equation, Dirichlet boundary conditions

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

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Authors: Tokihiko Akita, Seiichi Mita

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A millimeter-wave radar is the most robust against adverse environments, making it an essential environment recognition sensor for automated driving. However, the reflection signal is sparse and unstable, so it is difficult to obtain the high recognition accuracy. Deep learning provides high accuracy even for them in recognition, but requires large scale datasets with ground truth. Specially, it takes a lot of cost to annotate for a millimeter-wave radar. For the solution, utilizing a simulator that can generate an annotated huge dataset is effective. Simulation of the radar is more difficult to match with real world data than camera image, and recognition by deep learning with higher-order features using the simulator causes further deviation. We have challenged to improve the accuracy of traffic participant classification by fusing simulator and real-world data with domain adaptation technique. Experimental results with the domain adaptation network created by us show that classification accuracy can be improved even with a few real-world data.

Keywords: millimeter-wave radar, object classification, deep learning, simulation, domain adaptation

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Authors: Ming Lu, Xiaojun Li, Bodi Lu, Juehui Xing

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Earthquake damage phenomenon of each large earthquake gives comprehensive and profound real test to the dynamic performance and failure mechanism of different engineering structures. Cognitive engineering structure characteristics through seismic damage phenomenon are often far superior to expensive shaking table experiments. After the earthquake, people will record a variety of different types of engineering damage photos. However, a large number of earthquake damage photographs lack sufficient information and reduce their using value. To improve the research value and the use efficiency of engineering seismic damage photographs, this paper objects to explore and show seismic damage background information, which includes the earthquake magnitude, earthquake intensity, and the damaged structure characteristics. From the research requirement in earthquake engineering field, the authors use the 2008 China Wenchuan M8.0 earthquake photographs, and provide four kinds of attribute indexes and classification, which are seismic information, structure types, earthquake damage parts and disaster causation factors. The final object is to set up an engineering structural seismic damage database based on these four attribute indicators and classification, and eventually build a website providing seismic damage photographs.

Keywords: attribute index, classification method, earthquake damage picture, engineering structure

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Authors: Jeroen De Ceuster

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This study is the first empirical analysis of the link between corporate governance and European Union competition law. Although competition law enforcement is often studied through the lens of competition law, we offer an alternative perspective by looking at a number of corporate governance factor at the level of the board of directors. We find that undertakings where the Chief Executive Officer is also chairman of the board are twice as likely to violate European Union competition law. No significant relationship was found between European Union competition law infringements and gender diversity of the board, the size of the board, the percentage of directors appointed after the Chief Executive Officer, the percentage of independent directors, or the presence of corporate social responsibility (CSR) committee. This contribution is based on a 1-1 matched peer study. Our sample includes all ultimate parent companies with a board that have been sanctioned by the European Commission for either anticompetitive agreements or abuse of dominance for the period from 2004 to 2018. These companies were matched to a company with headquarters in the same country, belongs to the same industry group, is active in the European Economic Area, and is the nearest neighbor to the infringing company in terms of revenue. Our final sample includes 121 pairs. As is common with matched peer studies, we use CLR to analyze the differences within these pairs. The only statistically significant independent variable after controlling for size and performance is CEO/Chair duality. The results indicate that companies whose Chief Executive Officer also functions as chairman of the board are twice as likely to infringe European Union competition law. This is in line with the monitoring theory of the board of directors, which states that its primary function is to monitor top management. Since competition law infringements are mostly organized by management and hidden from board directors, the results suggest that a Chief Executive Officer who is also chairman is more likely to be either complicit in the infringement or less critical towards his day-to-day colleagues and thus impedes proper detection by the board of competition law infringements.

Keywords: corporate governance, competition law, board of directors, board independence, ender diversity, corporate social responisbility

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Authors: Usamah Al-Ali

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We explore the effect of expanding space on the exoticity of the matter supporting a traversable Lorentzian wormhole of zero radial tide whose line element is given by ds2 = dt^2 − a^2(t)[ dr^2/(1 − kr2 −b(r)/r)+ r2dΩ^2 in the context of General Relativity. This task is achieved by deriving the Einstein field equations for anisotropic matter field corresponding to the considered cosmological wormhole metric and performing a classification of their solutions on the basis of a variable equations of state (EoS) of the form p = ω(r)ρ. Explicit forms of the shape function b(r) and the scale factor a(t) arising in the classification are utilized to construct the corresponding energy-momentum tensor where the energy conditions for each case is investigated. While the violation of energy conditions is inevitable in case of static wormholes, the classification we performed leads to interesting solutions in which this violation is either reduced or eliminated.

Keywords: general relativity, Einstein field equations, energy conditions, cosmological wormhole

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Authors: María Dolores Caro, Eugenio M. Fedriani, Ángel F. Tenorio

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The transportation of dangerous goods by lorries throughout Europe must be done by using the roads conforming the European Road Transport Network. In this network, there are several parking areas where lorry drivers can park to rest according to the regulations. According to the "European Agreement concerning the International Carriage of Dangerous Goods by Road", parking areas where lorries transporting dangerous goods can park to rest, must follow several security stipulations to keep safe the rest of road users. At this respect, these lorries must be parked in adapted areas with strict and permanent surveillance measures. Moreover, drivers must satisfy several restrictions about resting and driving time. Under these facts, one may expect that there exist enough parking areas for the transport of this type of goods in order to obey the regulations prescribed by the European Union and its member countries. However, the already-existing parking areas are not sufficient to cover all the stops required by drivers transporting dangerous goods. Our main goal is, starting from the already-existing parking areas and the loading-and-unloading location, to provide an optimal answer to the following question: how many additional parking areas must be built and where must they be located to assure that lorry drivers can transport dangerous goods following all the stipulations about security and safety for their stops? The sense of the word “optimal” is due to the fact that we give a global solution for the location of parking areas throughout the whole European Road Transport Network, adjusting the number of additional areas to be as lower as possible. To do so, we have modeled the problem using graph theory since we are working with a road network. As nodes, we have considered the locations of each already-existing parking area, each loading-and-unloading area each road bifurcation. Each road connecting two nodes is considered as an edge in the graph whose weight corresponds to the distance between both nodes in the edge. By applying a new efficient algorithm, we have found the additional nodes for the network representing the new parking areas adapted for dangerous goods, under the fact that the distance between two parking areas must be less than or equal to 400 km.

Keywords: trans-european transport network, dangerous goods, parking areas, graph-based modeling

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Authors: Ermias A. Tegegn, Million Meshesha

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Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome (HIV/AIDS) is a major cause of death for most African countries. Ethiopia is one of the seriously affected countries in sub Saharan Africa. Previously in Ethiopia, having HIV/AIDS was almost equivalent to a death sentence. With the introduction of Antiretroviral Therapy (ART), HIV/AIDS has become chronic, but manageable disease. The study focused on a data mining technique to predict future living status of HIV/AIDS patients at the time of drug regimen change when the patients become toxic to the currently taking ART drug combination. The data is taken from University of Gondar Hospital ART program database. Hybrid methodology is followed to explore the application of data mining on ART program dataset. Data cleaning, handling missing values and data transformation were used for preprocessing the data. WEKA 3.7.9 data mining tools, classification algorithms, and expertise are utilized as means to address the research problem. By using four different classification algorithms, (i.e., J48 Classifier, PART rule induction, Naïve Bayes and Neural network) and by adjusting their parameters thirty-two models were built on the pre-processed University of Gondar ART program dataset. The performances of the models were evaluated using the standard metrics of accuracy, precision, recall, and F-measure. The most effective model to predict the status of HIV patients with drug regimen substitution is pruned J48 decision tree with a classification accuracy of 98.01%. This study extracts interesting attributes such as Ever taking Cotrim, Ever taking TbRx, CD4 count, Age, Weight, and Gender so as to predict the status of drug regimen substitution. The outcome of this study can be used as an assistant tool for the clinician to help them make more appropriate drug regimen substitution. Future research directions are forwarded to come up with an applicable system in the area of the study.

Keywords: HIV drug regimen, data mining, hybrid methodology, predictive model

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Authors: Vadim Vagin, Marina Fomina, Oleg Morosin

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This paper contains the description of argumentation approach for the problem of inductive concept formation. It is proposed to use argumentation, based on defeasible reasoning with justification degrees, to improve the quality of classification models, obtained by generalization algorithms. The experiment’s results on both clear and noisy data are also presented.

Keywords: argumentation, justification degrees, inductive concept formation, noise, generalization

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Authors: Bashar Dahiru Bashar

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Growing research reveals an unprecedented increase in African applicants for PhD positions across European universities. Meanwhile, a very small percentage is accepted as qualified candidates to marginalize, perpetuate stereotypes, and institute racial discrimination. Candidates of color very often encounter barriers and prejudices that not only diminish their sense of belonging but also hinder their academic progress. Although this issue has existed for quite some time, it attracts little attention, even from the academic community in higher education. Moreover, the focus is mostly on the applicants. In this contribution, concern has been raised that the African applicants for PhD positions in European Universities are the victims rather than the perpetrators. The Universities designed a recruitment process that is in all respects exclusive, biased, and European. The recruitment exercise is a hocus-post in order to cover language and racial and ethnic rejection. Just in the same way legacy admission is practiced in the US. The paper further expressed that the logic is to systematically maintain racial hierarchy and social dominance within the education sector. And because those at an advantage are also the ones that have the media and are predominant in academia, issues like this are not receiving deserved attention. Many people were victims of this recruitment process, while others survived severely wounded as a result of mental, social, and economic trauma. It is not the aim of this paper to provide an armchair solution to this issue but only to showcase the process with the hope of providing something that is needed to improve the present day's literacy and situation. The findings contribute to the broader discourse on diversity, equity, and inclusiveness within European Universities, emphasizing, amongst others, the need for cultivating an atmosphere where individuals are valued for their contributions rather than assessed based on race and ethnicity is essential for creating a vibrant and equitable global academic community, forging a path towards a just and harmonious educational landscape where everyone irrespective of race or ethnicity can thrive and contribute to the collective pursuit of knowledge.

Keywords: admission and recruitment for PhD position, globalization of education, systemic rejection, European university

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Authors: Katarzyna Stoklosa

Abstract:

Introduction: European Public Prosecutor’s Office (EPPO) is an independent office in European Union which was established under the article 86 of the Treaty on the Functioning of the European Union by the Treaty of Lisbon following the method of enhanced cooperation. EPPO is aimed at combating crimes against the EU’s financial interest et fraud against the EU budgets on the one hand, EPPO will give a chance to effective fight with organized criminality, on the other it seems to be a threat for member-states which bound with justice the problem of sovereignty. It is a new institution that will become effective from 2020, which is why it requires prior analysis. Methodology: The author uses statistical and comparative methods by collecting and analyzing the work of current institutions such as Europol, Eurojust, as well as the future impact of EPPO on detection and prosecution of crimes. The author will also conduct questionnaire among students and academic staff involved in the perception of EU institutions and the need to create new entities dealing with inter-agency cooperation in criminal matters. Thanks to these research the author will draw up present ways of cooperation between member-states and changes in fighting with financial crimes which will grow up under new regulation. Major Finding of the Study: Analysis and research show that EPPO is an institution based on the principle of mutual recognition, which often does not work in cooperation between Member States. Distrust and problems with the recognition of judgments of other EU Member States may significantly affect the functioning of EPPO. Poland is not part of the EPPO, because arguments have been raised that the European Public Prosecutor's Office interferes too much with the Member States’ pro-active sovereignty and duplicates competences. The research and analyzes carried out by the author show that EPPO has completely new competences, for example, it may file indictments against perpetrators of financial crimes. However, according to the research carried out by the author, such competences may undermine the sovereignty and the principle of protecting the public order of the EU. Conclusion: After the analysis, it will be possible to set following thesis: EPPO is only possible way to effective fight with organized financial criminality. However in conclusion Polish doubts should not be criticized at all. Institutions as EPPO must properly respect sovereignty of member-states. Even instruments like that cannot provoke political contraventions, because there are no other ways to effective resolving of international criminality problem.

Keywords: criminal trial, economic crimes, European Public Prosecutor's Office, European Union

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Authors: Svetlana Zhikrivetskaya, Nataliya Shirokova, Roman Bikanov, Elizaveta Musatova, Yana Kovaleva, Nataliya Vetrova, Ekaterina Pomerantseva

Abstract:

Nowadays there is a growing number of couples with conceiving problems due to male or female infertility. Genetic abnormalities are responsible for about 31% of all cases of male infertility. These abnormalities include both chromosomal aberrations or aneuploidies and mutations in certain genes. Chromosomal abnormalities can be easily identified, thus the development of screening panels able to reveal genetic reasons of male infertility on gene level is of current interest. There are approximately 2,000 genes involved in male fertility that is the reason why it is very important to determine the most clinically relevant in certain population and ethnic conditions. An infertility screening panel containing 48 mutations in genes AMHR2, CFTR, DNAI1, HFE, KAL1, TSSK2 and AZF locus which are the most clinically relevant for the European population according to databases NCBI and ClinVar was designed. The aim of this research was to confirm clinic relevance of these mutations in the Russian population. Genotyping was performed in 220 patients with different types of male infertility and in 57 healthy males with normozoospermia. Mutations were identified by end-point PCR with TaqMan probes in microfluidic plates. The frequency of 5 mutations in healthy males and 13 mutations in patients with infertility was revealed and estimated. The frequency of mutation c.187C>G in HFE gene was significantly lower for healthy males (8.8%) compared with patients (17.7%) and the values for the European population according to ExAc database (13.7%) and dbSNP (17.2%). Analysis of c.3454G>C, and c.1545_1546delTA mutations in the CFTR gene revealed increased frequency (0.9 and 0.2%, respectively) in patients with infertility compared with data for the European population (0.04%, respectively (ExAc, European (Non-Finnish) and for the Aggregated Populations (0.002% (ExAc), because there is no data for European population for c.1545_1546delTA mutation. The frequency of del508 mutation (CFTR) in patients (1.59%) were lower comparing with male infertility Europeans (3.34-6.25% depending on nationality) and at the same level with healthy Europeans (1.06%, ExAc, European (Non-Finnish). Analysis of c.845G>A (HFE) mutation resulted in decreased frequency in patients (1.8%) in contrast with the European population data (5.1%, respectively, ExAc, European (Non-Finnish). Moreover, obtained data revealed no statistically significant frequency difference for c.845G>A mutation (HFE) between healthy males in the Russian and the European populations. Allele frequencies of mutations c.350G>A (CFTR), c.193A>T (HFE), c.774C>T, and c.80A>G (gene TSSK2) showed no significantly difference among patients with infertility, healthy males and Europeans. Analysis of AZF locus revealed increased frequency for AZFc microdeletion in patients with male infertility. Thereby, the new data of the allele frequencies in infertility patients in the Russian population was obtained. As well as the frequency differences of mutations associated with male infertility among patients, healthy males in the Russian population and the European one were estimated. The revealed differences showed that for high effectiveness of screening panel detecting genetically caused male infertility it is very important to consider ethnic and population characteristics of patients which will be screened.

Keywords: allele frequency, azoospermia, male infertility, mutation, population

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