Search results for: evaluation and prediction of quality answer

Authors: Suzan Wedyan

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Associative classification (AC) is a data mining approach that combines association rule and classification to build classification models (classifiers). AC has attracted a significant attention from several researchers mainly because it derives accurate classifiers that contain simple yet effective rules. In the last decade, a number of associative classification algorithms have been proposed such as Classification based Association (CBA), Classification based on Multiple Association Rules (CMAR), Class based Associative Classification (CACA), and Classification based on Predicted Association Rule (CPAR). This paper surveys major AC algorithms and compares the steps and methods performed in each algorithm including: rule learning, rule sorting, rule pruning, classifier building, and class prediction.

Keywords: Associative Classification, Classification, Data Mining, Learning, Rule Ranking, Rule Pruning, Prediction.

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Authors: E. Siemianowska, K.A. Skibniewska, M. Warechowska, M.F. Jędrzejczak, J. Tyburski

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The article contains results of the flour and bread quality assessment from the grains of spring spelt, also called as an ancient wheat. Spelt was cultivated on heavy and medium soils observing principles of organic farming. Based on flour and bread laboratory studies, as well as laboratory baking, the technological usefulness of studied flour has been determined. These results were referred to the standard derived from common wheat cultivated in the same conditions. Grain of spring spelt is a good raw material for manufacturing bread flour, from which to get high-quality bakery products, but this is strictly dependent on the variety of ancient wheat.

Keywords: Bread, dark flour, wholemeal, flour quality, spelt

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Authors: Zohreh O. Akbari, Ahmad Faraahi

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Many agent-oriented software engineering methodologies have been proposed for software developing; however their application is still limited due to their lack of maturity. Evaluating the strengths and weaknesses of these methodologies plays an important role in improving them and in developing new stronger methodologies. This paper presents an evaluation framework for agent-oriented methodologies, which addresses six major areas: concepts, notation, process, pragmatics, support for software engineering and marketability. The framework is then used to evaluate the Gaia methodology to identify its strengths and weaknesses, and to prove the ability of the framework for promoting the agent-oriented methodologies by detecting their weaknesses in detail.

Keywords: Agent-Oriented Software Engineering, Evaluation Framework, Methodology.

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Authors: Redha Benzid, Farid Marir, Nour-Eddine Bouguechal

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This paper presents a new Quality-Controlled, wavelet based, compression method for electrocardiogram (ECG) signals. Initially, an ECG signal is decomposed using the wavelet transform. Then, the resulting coefficients are iteratively thresholded to guarantee that a predefined goal percent root mean square difference (GPRD) is matched within tolerable boundaries. The quantization strategy of extracted non-zero wavelet coefficients (NZWC), according to the combination of RLE, HUFFMAN and arithmetic encoding of the NZWC and a resulting look up table, allow the accomplishment of high compression ratios with good quality reconstructed signals.

Keywords: ECG compression, Non-uniform Max-Lloydquantizer, PRD, Quality-Controlled, Wavelet transform

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Authors: Mohammad Shoyaib, M. Abdullah-Al-Wadud, Oksam Chae

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Proteomics is one of the largest areas of research for bioinformatics and medical science. An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. Predicting Protein-Protein Interaction (PPI) is one of the crucial and decisive problems in current research. Genomic data offer a great opportunity and at the same time a lot of challenges for the identification of these interactions. Many methods have already been proposed in this regard. In case of in-silico identification, most of the methods require both positive and negative examples of protein interaction and the perfection of these examples are very much crucial for the final prediction accuracy. Positive examples are relatively easy to obtain from well known databases. But the generation of negative examples is not a trivial task. Current PPI identification methods generate negative examples based on some assumptions, which are likely to affect their prediction accuracy. Hence, if more reliable negative examples are used, the PPI prediction methods may achieve even more accuracy. Focusing on this issue, a graph based negative example generation method is proposed, which is simple and more accurate than the existing approaches. An interaction graph of the protein sequences is created. The basic assumption is that the longer the shortest path between two protein-sequences in the interaction graph, the less is the possibility of their interaction. A well established PPI detection algorithm is employed with our negative examples and in most cases it increases the accuracy more than 10% in comparison with the negative pair selection method in that paper.

Keywords: Interaction graph, Negative training data, Protein-Protein interaction, Support vector machine.

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Authors: Suk-Hyun Yu, Heeyong Kwon

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Recently PM-10 has become a social and global issue. It is one of major air pollutants which affect human health. Therefore, it needs to be forecasted rapidly and precisely. However, PM-10 comes from various emission sources, and its level of concentration is largely dependent on meteorological and geographical factors of local and global region, so the forecasting of PM-10 concentration is very difficult. Neural network model can be used in the case. But, there are few cases of high concentration PM-10. It makes the learning of the neural network model difficult. In this paper, we suggest a simple input balancing method when the data distribution is uneven. It is based on the probability of appearance of the data. Experimental results show that the input balancing makes the neural networks’ learning easy and improves the forecasting rates.

Keywords: AI, air quality prediction, neural networks, pattern recognition, PM-10.

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Authors: Zhizhong Jiang, Peter Naudé

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Effective evaluation of software development effort is an important aspect of successful project management. Based on a large database with 4106 projects ever developed, this study statistically examines the factors that influence development effort. The factors found to be significant for effort are project size, average number of developers that worked on the project, type of development, development language, development platform, and the use of rapid application development. Among these factors, project size is the most critical cost driver. Unsurprisingly, this study found that the use of CASE tools does not necessarily reduce development effort, which adds support to the claim that the use of tools is subtle. As many of the current estimation models are rarely or unsuccessfully used, this study proposes a parsimonious parametric model for the prediction of effort which is both simple and more accurate than previous models.

Keywords: Development effort, function points, team size, development language, CASE tool, rapid application development.

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Authors: Muhibul Haque Bhuyan

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In the current context, the engineering program educators need to think about how to develop the concepts and complex engineering problem-solving skills through various complex engineering activities by the undergraduate engineering students in various engineering courses. But most of them are facing challenges to assess and evaluate these skills of their students. In this study, detailed assessment and evaluation methods for the undergraduate Electrical and Electronic Engineering (EEE) program are stated using the Outcome-Based Education (OBE) approach. For this purpose, a final year course titled control systems has been selected. The assessment and evaluation approach, course contents, course objectives, course outcomes (COs), and their mapping to the program outcomes (POs) with complex engineering problems and activities via the knowledge profiles, performance indicators, rubrics of assessment, CO and PO attainment data, and other statistics, are reported for a student-cohort of control systems course registered by the students of BSc in EEE program in Spring 2021 Semester at the EEE Department of Southeast University (SEU). It is found that the target benchmark was achieved by the students of that course. Several recommendations for the continuous quality improvement (CQI) process are also provided.

Keywords: Complex engineering problem, knowledge profiles, OBE, control systems course, COs, PIs, POs, assessment rubrics.

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Authors: Chih-Wei Hsieh, Chau-Yi Chou, Sheng-HsiuKuo, Tsung-Che Tsai, I-Chen Wu

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This paper presents the benchmarking results and performance evaluation of differentclustersbuilt atthe National Center for High-Performance Computingin Taiwan. Performance of processor, memory subsystem andinterconnect is a critical factor in the overall performance of high performance computing platforms. The evaluation compares different system architecture and software platforms. Most supercomputer used HPL to benchmark their system performance, in accordance with the requirement of the TOP500 List. In this paper we consider system memory access factors that affect benchmark performance, such as processor and memory performance.We hope these works will provide useful information for future development and construct cluster system.

Keywords: Performance Evaluation, Benchmarking and High-Performance Computing

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Authors: Caihong Sun, Xizi Zhang

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Rating prediction is an important problem for recommender systems. The task is to predict the rating for an item that a user would give. Most of the existing algorithms for the task ignore the effect of negative ratings rated by users on items, but the negative ratings have a significant impact on users’ purchasing decisions in practice. In this paper, we present a rating prediction algorithm based on factorization machines that consider the effect of negative ratings inspired by Loss Aversion theory. The aim of this paper is to develop a concave and a convex negative disgust function to evaluate the negative ratings respectively. Experiments are conducted on MovieLens dataset. The experimental results demonstrate the effectiveness of the proposed methods by comparing with other four the state-of-the-art approaches. The negative ratings showed much importance in the accuracy of ratings predictions.

Keywords: Factorization machines, feature engineering, negative ratings, recommendation systems.

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Authors: M. Gomaa Abdoelatef, Robert M. Field, Lee, Yong-Kwan

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Growing human population has placed increased demands on water supplies and spurred a heightened interest in desalination infrastructure. Key elements of the economics of desalination projects are thermal and electrical inputs. With growing concerns over use of fossil fuels to (indirectly) supply these inputs, coupling of desalination with nuclear power production represents a significant opportunity. Individually, nuclear and desalination technologies have a long history and are relatively mature. For desalination, Reverse Osmosis (RO) has the lowest energy inputs. However, the economically driven output quality of the water produced using RO, which uses only electrical inputs, is lower than the output water quality from thermal desalination plants. Therefore, modern desalination projects consider that RO should be coupled with thermal desalination technologies (MSF, MED, or MED-TVC) with attendant steam inputs to permit blending to produce various qualities of water. A large nuclear facility is well positioned to dispatch large quantities of both electrical and thermal power. This paper considers the supply of thermal energy to a large desalination facility to examine heat balance impact on the nuclear steam cycle. The APR1400 nuclear plant is selected as prototypical from both a capacity and turbine cycle heat balance perspective to examine steam supply and the impact on electrical output. Extraction points and quantities of steam are considered parametrically along with various types of thermal desalination technologies to form the basis for further evaluations of economically optimal approaches to the interface of nuclear power production with desalination projects. In our study, the thermodynamic evaluation will be executed by DE-TOP, an IAEA sponsored program. DE-TOP has capabilities to analyze power generation systems coupled to desalination plants through various steam extraction positions, taking into consideration the isolation loop between the nuclear and the thermal desalination facilities (i.e., for radiological isolation).

Keywords: APR1400, Cogeneration, Desalination, DE-TOP, IAEA, MED, MED-TVC, MSF, RO.

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Authors: Hossein Morshedlou, Ahmad Abdollahzade Barforoush

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Recent developments in storage technology and networking architectures have made it possible for broad areas of applications to rely on data streams for quick response and accurate decision making. Data streams are generated from events of real world so existence of associations, which are among the occurrence of these events in real world, among concepts of data streams is logical. Extraction of these hidden associations can be useful for prediction of subsequent concepts in concept shifting data streams. In this paper we present a new method for learning association among concepts of data stream and prediction of what the next concept will be. Knowing the next concept, an informed update of data model will be possible. The results of conducted experiments show that the proposed method is proper for classification of concept shifting data streams.

Keywords: Data Stream, Classification, Concept Shift, History.

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Authors: J. McCullagh, T. Whitfort

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Artificial Neural Networks (ANNs) have been used successfully in many scientific, industrial and business domains as a method for extracting knowledge from vast amounts of data. However the use of ANN techniques in the sporting domain has been limited. In professional sport, data is stored on many aspects of teams, games, training and players. Sporting organisations have begun to realise that there is a wealth of untapped knowledge contained in the data and there is great interest in techniques to utilise this data. This study will use player data from the elite Australian Football League (AFL) competition to train and test ANNs with the aim to predict the onset of injuries. The results demonstrate that an accuracy of 82.9% was achieved by the ANNs’ predictions across all examples with 94.5% of all injuries correctly predicted. These initial findings suggest that ANNs may have the potential to assist sporting clubs in the prediction of injuries.

Keywords: Artificial Neural Networks, data, injuries, sport

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Authors: Costin Pribeanu, Rytis Vilkonis, Dragoş Daniel Iordache

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User interaction components of Augmented Reality (AR) systems have to be tested with users in order to find and fix usability problems as early as possible. In this paper we will report on a user-centered design approach for AR systems following the experience acquired during the design and evaluation of a software prototype for an AR-based educational platform. In this respect we will focus on the re-design of the user task based on the results from a formative usability evaluation. The basic idea of our approach is to describe task scenarios in a tabular format, to develop a task model in a task modeling environment and then to simulate the execution.

Keywords: AR-based educational systems, task-based design, usability evaluation.

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Authors: Chaiwat Waree, Kalanyoo Petcharaporn

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This research aims to develop and evaluate a training course to promote learning activities of 2nd year, Suan Sunandha Rajabhat University, faculty of education students using multiple intelligences theory. The process is divided into two phases: Phase 1 development of training course to promote learning activities consisting of principles, objectives of the course, structure, training duration, content, training materials, training activities, media training, monitoring, measurement and evaluation quality of the course. Phase 2 evaluation efficiency of training course was to use the improved curriculum with experimental group which is 2nd year, Suan Sunandha Rajabhat University, faculty of education students was drawn randomly 152 students. The experimental pattern was randomized Control Group Pre-Test Post-Test Design, Analysis Data by t-Test with the software SPFSS for Windows. Research has shown that: 1). the ability of teaching and learning according to the theory of multiple intelligences after training is higher than before training significantly in statistic at .01 level, 2). The satisfaction of students to the training courses was overall at the highest level.

Keywords: A training course, learning activities, multiple intelligences.

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Authors: N.Poolsawad, C.Kambhampati, J. G. F. Cleland

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In this paper, we investigated the characteristic of a clinical dataseton the feature selection and classification measurements which deal with missing values problem.And also posed the appropriated techniques to achieve the aim of the activity; in this research aims to find features that have high effect to mortality and mortality time frame. We quantify the complexity of a clinical dataset. According to the complexity of the dataset, we proposed the data mining processto cope their complexity; missing values, high dimensionality, and the prediction problem by using the methods of missing value replacement, feature selection, and classification.The experimental results will extend to develop the prediction model for cardiology.

Keywords: feature selection, missing values, classification, clinical dataset, heart failure.

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Authors: I. Oikonomou, I. Lappa, D. Daferera, C. Kanakis, L. Kiokakis, K. Skordilis, A. Avramouli, E. Kalli, C. Pappas, P. A. Tarantilis, E. Skotti

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Grape seeds are waste products of wineries and often referred to as an important agricultural and industrial waste product with the potential to be used in pharmaceutical, food, and cosmetic applications. In this study, grape seed oil from traditional Ionian varieties was examined for the determination of the quality and the characteristics of each variety. Initially, the fatty acid methyl ester (FAME) profiles were analyzed using Gas Chromatography-Mass Spectrometry, after transesterification. Furthermore, other quality parameters of the grape seed oils were determined by Spectroscopy techniques, UV-Vis and Raman included. Moreover, the antioxidant capacity of the oil was measured by 2,2'-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and 2,2-Diphenyl-1-picrylhydrazyl (DPPH) assays and their antioxidant capacity expressed in Trolox equivalents. K and ΔΚ indices were measured in 232, 268, 270 nm, as an oil quality index. The results indicate that the air-dried grape seed total oil content ranged from 5.26 to 8.77% w/w, which is in accordance with the other grape seed varieties tested in similar studies. The composition of grape seed oil is predominated with linoleic and oleic fatty acids, with the linoleic fatty acid ranging from 53.68 to 69.95% and both the linoleic and oleic fatty acids totaling 78-82% of FAMEs, which is analogous to the fatty acid composition of safflower oil. The antioxidant assays ABTS and DPPH scored high, exhibiting that the oils have potential in the cosmetic and culinary businesses. Above that, our results demonstrate that Ionian grape seed oils have prospects that can go further than cosmetic or culinary use, into the pharmaceuticals industry. Finally, the reclamation of grape seeds from wineries waste stream is in accordance with the bio-economy strategic framework and contributes to environmental protection.

Keywords: Antioxidant capacity, fatty acid methyl esters, grape seed oil, GC-MS.

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Authors: Bouttout Abdelouahab, Amara Mohamed, Djakabe Saad, Remram Youcef

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This paper presents the results of an evaluation of acoustic comfort such as background noise and reverberation time in teaching rooms in Height National School of Civil Engineering, Algeria. Four teaching rooms are evaluated: conference room, two classroom and amphitheatre. The acoustic quality of the classrooms has been analyzed based on measurements of sound pressure level inside room and reverberations time. The measurement results show that impulse decays dependent on the position of the microphone inside room and the background noise is with agreement of National Official Journal of Algeria published in July 1993. Therefore there exists a discrepancy between the obtained reverberation time value and recommended reverberation time in some classrooms. Three methods have been proposed to reduce the reverberation time values in such room. We developed a program with FORTRAN 6.0 language based on the absorption acoustic values of the Technical Document Regulation (DTR C3.1.1). The important results of this paper can be used to regulate the construction and execute the acoustic rehabilitations of teaching room in Algeria, especially the classrooms of the pupils in primary and secondary schools.

Keywords: Room acoustic, reverberation time, background noise, absorptions materials.

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Authors: Fayza Obied-Allah

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Customer’ needs, quality, and value creation while reducing costs through supply chain management provides challenges and opportunities for companies and researchers. In the light of these challenges, modern ideas must contribute to counter these challenges and exploit opportunities. Therefore, this paper discusses the impact of the quality cost on revenue sharing as a most important incentive to configure business networks. This paper develops the quality cost approach to align with the modern era. It develops a model to measure quality costs which might enable firms to manage revenue sharing in a supply chain. The developed model includes five categories; besides the well-known four categories (namely prevention costs, appraisal costs, internal failure costs, and external failure costs), a new category has been developed in this research as a new vision of the relationship between quality costs and innovations in industry. This new category is Recycle Cost. This paper also examines whether such quality costs in supply chains influence the revenue sharing between partners. Using the author's quality cost model, the relationship between quality costs and revenue sharing among partners is examined using a case study in an Egyptian manufacturing company which is a part of a supply chain. This paper argues that the revenue-sharing proportion allocated to supplier increases as the recycle cost of supplier increases, and the revenue-sharing proportion allocated to manufacturer increases as the prevention and appraisal costs increase, as well as the failure costs, the recycle costs of manufacturer, and the recycle costs of suppliers decrease. However, the results present surprising findings. The purposes of this study are developing quality cost approach and understanding the relationships between quality costs and revenue sharing in supply chains. Therefore, the present study contributes to theory and practice by explaining how the cost of recycling can be combined in quality cost model to better understanding the revenue sharing among partners in supply chains.

Keywords: Quality cost, Recycle cost, Revenue sharing, Supply chain.

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Authors: T. Arakawa, H. Sato

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There are many drivers who feel right A pillar of Japanese right-hand-drive car preventing visibility on turning right or left at intersection. On the other hand, there is a report that almost pedestrian accident is caused by the delay of finding pedestrian by drivers and this is found by drivers’ eye movement. Thus, we developed the evaluation method of quantification using drivers’ eye movement data by least squares estimation and we applied this method to commercial vehicle and evaluation the visibility. It is suggested that visibility of vehicle can be quantified and estimated by linear model obtained from experimental eye fixation data and information of vehicle dimensions.

Keywords: Eye fixation, modeling, obstacle feeling, right A pillar.

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Authors: Hamidullah Binol, Abdullah Bal

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Nowadays, food safety is a great public concern; therefore, robust and effective techniques are required for detecting the safety situation of goods. Hyperspectral Imaging (HSI) is an attractive material for researchers to inspect food quality and safety estimation such as meat quality assessment, automated poultry carcass inspection, quality evaluation of fish, bruise detection of apples, quality analysis and grading of citrus fruits, bruise detection of strawberry, visualization of sugar distribution of melons, measuring ripening of tomatoes, defect detection of pickling cucumber, and classification of wheat kernels. HSI can be used to concurrently collect large amounts of spatial and spectral data on the objects being observed. This technique yields with exceptional detection skills, which otherwise cannot be achieved with either imaging or spectroscopy alone. This paper presents a nonlinear technique based on kernel Fukunaga-Koontz transform (KFKT) for detection of fat content in ground meat using HSI. The KFKT which is the nonlinear version of FKT is one of the most effective techniques for solving problems involving two-pattern nature. The conventional FKT method has been improved with kernel machines for increasing the nonlinear discrimination ability and capturing higher order of statistics of data. The proposed approach in this paper aims to segment the fat content of the ground meat by regarding the fat as target class which is tried to be separated from the remaining classes (as clutter). We have applied the KFKT on visible and nearinfrared (VNIR) hyperspectral images of ground meat to determine fat percentage. The experimental studies indicate that the proposed technique produces high detection performance for fat ratio in ground meat.

Keywords: Food (Ground meat) inspection, Fukunaga-Koontz transform, hyperspectral imaging, kernel methods.

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Authors: Dong-Jae Kang, Nam-Woo Kim, Duk-Joo Son, Sung-In Jung

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According to dramatic growth of internet services, an easy and prompt service deployment has been important for internet service providers to successfully maintain time-to-market. Before global service deployment, they have to pay the big cost for service evaluation to make a decision of the proper system location, system scale, service delay and so on. But, intra-Lab evaluation tends to have big gaps in the measured data compared with the realistic situation, because it is very difficult to accurately expect the local service environment, network congestion, service delay, network bandwidth and other factors. Therefore, to resolve or ease the upper problems, we propose multiple cloud based GPES Broker system and use case that helps internet service providers to alleviate the above problems in beta release phase and to make a prompt decision for their service launching. By supporting more realistic and reliable evaluation information, the proposed GPES Broker system saves the service release cost and enables internet service provider to make a prompt decision about their service launching to various remote regions.

Keywords: GPES Broker system, Cloud Service Broker, Multiple Cloud, Global performance evaluation service (GPES), Service provisioning

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Authors: Ayse Gecer, Nihal Erginel

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Quality control is the crucial step for ISO 9001 Quality System Management Standard for companies. While measuring the quality level of both raw material and semi product/product, the calibration of the measuring device is an essential requirement. Calibration suppliers are in the service sector and therefore the calibration supplier selection is becoming a worthy topic for improving service quality. This study presents the results of a questionnaire about the selection criteria of a calibration supplier. The questionnaire was applied to 103 companies and the results are discussed in this paper. The analysis was made with MINITAB 14.0 statistical programs. “Competence of documentations" and “technical capability" are defined as the prerequisites because of the ISO/IEC17025:2005 standard. Also “warranties and complaint policy", “communication", “service features", “quality" and “performance history" are defined as very important criteria for calibration supplier selection.

Keywords: Calibration, criteria of calibration supplier selection, calibration supplier selection, questionnaire

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Authors: Chia-Ling Chang, Chung-Sheng Liao

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The present study focuses on the discussion over the parameter of Artificial Neural Network (ANN). Sensitivity analysis is applied to assess the effect of the parameters of ANN on the prediction of turbidity of raw water in the water treatment plant. The result shows that transfer function of hidden layer is a critical parameter of ANN. When the transfer function changes, the reliability of prediction of water turbidity is greatly different. Moreover, the estimated water turbidity is less sensitive to training times and learning velocity than the number of neurons in the hidden layer. Therefore, it is important to select an appropriate transfer function and suitable number of neurons in the hidden layer in the process of parameter training and validation.

Keywords: Artificial Neural Network (ANN), sensitivity analysis, turbidity.

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Authors: J. K. Alhassan, B. Attah, S. Misra

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Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. Medical dataset is a vital ingredient used in predicting patient’s health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. WEKA software was used for the implementation of the algorithms. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. From the results obtained, DTA performed better than ANN. The Root Mean Squared Error (RMSE) of MLP is 0.3913 that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively.

Keywords: Artificial neural network, classification, decision tree, diabetes mellitus.

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Authors: R. Rezaeipour

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One of the key aspects of power quality improvement in power system is the mitigation of voltage sags/swells and flicker. Custom power devices have been known as the best tools for voltage disturbances mitigation as well as reactive power compensation. Dynamic Voltage Restorer (DVR) which is the most efficient and effective modern custom power device can provide the most commercial solution to solve several problems of power quality in distribution networks. This paper deals with analysis and simulation technique of DVR based on instantaneous power theory which is a quick control to detect signals. The main purpose of this work is to remove three important disturbances including voltage sags/swells and flicker. Simulation of the proposed method was carried out on two sample systems by using Matlab software environment and the results of simulation show that the proposed method is able to provide desirable power quality in the presence of wide range of disturbances.

Keywords: DVR, Power quality, Voltage sags, Voltage swells, Flicker.

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to prevent deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network. 

Keywords: Accident risks estimation, artificial neural network, deep learning, K-mean, road safety.

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Authors: Talbi Mohammed, Oustous Aziz, Ben Messaoud Mounir, Sebihi Rajaa, Khalis Mohammed

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Background: Breast cancer is the leading cause of death for women around the world. Screening mammography is the reference examination, due to its sensitivity for detecting small lesions and micro-calcifications. Therefore, it is essential to ensure quality mammographic examinations with the most optimal dose. These conditions depend on the choice of exposure parameters. Clinically, practices must be evaluated in order to determine the most appropriate exposure parameters. Material and Methods: We performed our measurements on a mobile mammography unit (PLANMED Sofie-classic.) in Morocco. A solid dosimeter (AGMS Radcal) and a MTM 100 phantom allow to quantify the delivered dose and the image quality. For image quality assessment, scores are defined by the rate of visible inserts (MTM 100 phantom), obtained and compared for each acquisition. Results: The results show that the parameters of the mammography unit on which we have made our measurements can be improved in order to offer a better compromise between image quality and breast dose. The last one can be reduced up from 13.27% to 22.16%, while preserving comparable image quality.

Keywords: Mammography, image quality, breast dose.

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Authors: H. Meradi, S. Bouhouche, M. Lahreche

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In this work, we consider an application of neural networks in LD converter. Application of this approach assumes a reliable prediction of steel temperature and reduces a reblow ratio in steel work. It has been applied a conventional model to charge calculation, the obtained results by this technique are not always good, this is due to the process complexity. Difficulties are mainly generated by the noisy measurement and the process non linearities. Artificial Neural Networks (ANNs) have become a powerful tool for these complex applications. It is used a backpropagation algorithm to learn the neural nets. (ANNs) is used to predict the steel bath temperature in oxygen converter process for the end condition. This model has 11 inputs process variables and one output. The model was tested in steel work, the obtained results by neural approach are better than the conventional model.

Keywords: LD converter, bath temperature, neural networks.

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Authors: Muhammad Shahid, Suhaimi Ibrahim, Mohd Naz'ri Mahrin

Abstract:

Requirements management is critical to software delivery success and project lifecycle. Requirements management and their traceability provide assistance for many software engineering activities like impact analysis, coverage analysis, requirements validation and regression testing. In addition requirements traceability is the recognized component of many software process improvement initiatives. Requirements traceability also helps to control and manage evolution of a software system. This paper aims to provide an evaluation of current requirements management and traceability tools. Management and test managers require an appropriate tool for the software under test. We hope, evaluation identified here will help to select the efficient and effective tool.

Keywords: Requirements Traceability, Requirements TraceabilityTools; Requirements Management, Requirement Engineering

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