Search results for: evaluation metrics

Authors: Imen Amari, Kaouther Gasmi, Asma Rebaya, Salem Hasnaoui

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The problem of scheduling and mapping data flow applications on multi-core architectures is notoriously difficult. This difficulty is related to the rapid evaluation of Telecommunication and multimedia systems accompanied by a rapid increase of user requirements in terms of latency, execution time, consumption, energy, etc. Having an optimal scheduling on multi-cores DSP (Digital signal Processors) platforms is a challenging task. In this context, we present a novel technic and algorithm in order to find a valid schedule that optimizes the key performance metrics particularly the Latency. Our contribution is based on Satisfiability Modulo Theories (SMT) solving technologies which is strongly driven by the industrial applications and needs. This paper, describe a scheduling module integrated in our proposed Workflow which is advised to be a successful approach for programming the applications based on NoC-DSP platforms. This workflow transform automatically a Simulink model to a synchronous dataflow (SDF) model. The automatic transformation followed by SMT solver scheduling aim to minimize the final latency and other software/hardware metrics in terms of an optimal schedule. Also, finding the optimal numbers of cores to be used. In fact, our proposed workflow taking as entry point a Simulink file (.mdl or .slx) derived from embedded Matlab functions. We use an approach which is based on the synchronous and hierarchical behavior of both Simulink and SDF. Whence, results of running the scheduler which exist in the Workflow mentioned above using our proposed SMT solver algorithm refinements produce the best possible scheduling in terms of latency and numbers of cores.

Keywords: multi-cores DSP, scheduling, SMT solver, workflow

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Authors: Nicholas Reinicke, Brennan Borlaug, Matthew Moniot

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The COVID-19 pandemic has resulted in a significant change in driving behavior as people respond to the new environment. However, existing methods for analyzing driver behavior, such as travel surveys and travel demand models, are not suited for incorporating abrupt environmental disruptions. To address this, we analyze a set of high-resolution trip data and introduce two new metrics for quantifying driving behavioral shifts as a function of time, allowing us to compare the time periods before and after the pandemic began. We apply these metrics to the Denver, Colorado metropolitan statistical area (MSA) to demonstrate the utility of the metrics. Then, we present a case study for comparing two distinct MSAs, Louisville, Kentucky, and Des Moines, Iowa, which exhibit significant differences in the makeup of their labor markets. The results indicate that although the regions of study exhibit certain unique driving behavioral shifts, emerging trends can be seen when comparing between seemingly distinct regions. For instance, drivers in all three MSAs are generally shown to have spent more time at residential locations and less time in workplaces in the time period after the pandemic started. In addition, workplaces that may be incompatible with remote working, such as hospitals and certain retail locations, generally retained much of their pre-pandemic travel activity.

Keywords: COVID-19, driver behavior, GPS data, signal analysis, telework

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Authors: Ivayla Trifonova, Naoum Jamous, Holger Schrödl

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A good web design is a prerequisite for a successful business nowadays, especially since the internet is the most common way for people to inform themselves. Web design includes the optical composition, the structure, and the user guidance of websites. The importance of each website leads to the question if there is a way to measure its usefulness. The aim of this paper is to suggest a methodology for the evaluation of web design. The desired outcome is to have an evaluation that is concentrated on a specific website and its target group.

Keywords: evaluation methodology, factor analysis, target group, web design

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Authors: Namwinwelbere Dabire, Eugene C. Ezin, Adandedji M. Firmin

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The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Nokoue Lake in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Nokoue Lake, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué Lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

Keywords: forecasting, long short-term memory cell, recurrent artificial neural network, Nokoué lake

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Authors: Panwasn Mahalawalert

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The objective of this study was to meta-evaluation of Master Degree theses in Science Program of Evaluation Methodology at Srinakharinwirot University, published during 2008-2011. This study was summative meta-evaluation that evaluated all theses of Master Degree in Science Program of Evaluation Methodology. Data were collected using the theses characteristics recording form and the evaluation meta-evaluation checklist. The collected data were analyzed by two parts: 1) Quantitative data were analyzed by descriptive statistics presented in frequency, percentages, mean, and standard deviation and 2) Qualitative data were analyzed by content analysis. The results of this study were found the theses characteristics was results revealed that most of theses were published in 2011. The largest group of theses researcher were female and were from the government office. The evaluation model of all theses were Decision-Oriented Evaluation Model. The objective of all theses were evaluate the project or curriculum. The most sampling technique were used the multistage random sampling technique. The most tool were used to gathering the data were questionnaires. All of the theses were analysed by descriptive statistics. The meta-evaluation results revealed that most of theses had fair on Utility Standards and Feasibility Standards, good on Propriety Standards and Accuracy Standards.

Keywords: meta-evaluation, evaluation, master degree theses, Srinakharinwirot University

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Authors: Sakib Hasn, Shahid Anwar

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This study highlights the vital importance of assessing the Simulator Sickness Questionnaire and presence measures as virtual reality (VR) incorporation into language teaching gains popularity. To address user discomfort, which prevents efficient learning in VR environments, the measurement of SSQ becomes crucial. Additionally, evaluating presence metrics is essential to determine the level of engagement and immersion, both crucial for rich language learning experiences. This paper designs a VR-based Chinese language application and proposes a thorough test technique aimed at systematically analyzing SSQ and presence measures. Subjective tests and data analysis were carried out to highlight the significance of addressing user discomfort in VR language education. The results of this study shed light on the difficulties posed by user discomfort in VR language learning and offer insightful advice on how to improve VR language learning applications. Furthermore, the outcome of the research explores ‘VR-based language education,’ ‘inclusive language learning platforms," and "cross-cultural communication,’ highlighting the potential for VR to facilitate language learning across diverse cultural backgrounds. Overall, the analysis results contribute to the enrichment of language learning experiences in the virtual realm and underscore the need for continued exploration and improvement in this field.

Keywords: virtual reality (VR), language education, simulator sickness questionnaire, presence metrics, VR-based Chinese language education

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Authors: Julie Delany

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To thrive in today’s digital climate, it is vital that organisations adopt new technology and prepare for rising digital trends. This proves more difficult in government where, traditionally, people lack change readiness. While individuals may have a desire to work smarter, this does not necessarily mean embracing technology. This paper discusses the rollout of an application into a small department of highly experienced nurses. The goal was to both streamline the department's workflow and provide a platform for gathering essential business metrics. The biggest challenges were adoption and motivating the nurses to change their routines and learn new computer skills. Two-thirds struggled with the change, and as a result, some jeopardised the validity of the business metrics. In conclusion, there are lessons learned and recommendations for similar projects.

Keywords: change ready, information technology, end-user, iterative method, rollout plan, data analytics

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Authors: Emin Gundogar, Burak Erkayman, Nusret Sazak

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Providing effective management performance through the whole supply chain is critical issue and hard to applicate. The proper evaluation of integrated data may conclude with accurate information. Analysing the supply chain data through OLAP (On-Line Analytical Processing) technologies may provide multi-angle view of the work and consolidation. In this study, association rules and classification techniques are applied to measure the supply chain performance metrics of an automotive manufacturer in Turkey. Main criteria and important rules are determined. The comparison of the results of the algorithms is presented.

Keywords: supply chain performance, performance measurement, data mining, automotive

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Authors: Rong Guo, Mengshi Huang, Yujing Bai

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With the rapid development of urbanization, the urban population increases and urban sprawl appeared. And these issues led to a sharp deterioration of the ecological environment. So, the urban ecological security evaluation was imminent. The weights identify of index was a key step of the research of ecological security evaluation. The AHP was widely used in the extensive research of weights identify of ecological security index. The characteristics of authority and quantitative can fully reflect the views of relevant experts. On the basis of building the ecological security evaluation index of Harbin, the paper combed and used the basic principle of the AHP, and calculated the weights of Harbin ecological security evaluation index through the process of the expert opinions “summary-feedback-summary”. And lay a foundation of future study of Harbin ecological security index, and guide the quantitative evaluation of Harbin ecological security.

Keywords: AHP, ecological security, evaluation Index, weights identify, harbin

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Authors: Berhanu Keno Terfa

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To realize the consequences of urbanization, accurate, and up-to-date representation of the urban landscape patterns is critical for urban planners and policymakers. Thus, the study quantitatively characterized the spatiotemporal composition and configuration of the urban landscape and urban expansion process in Dire Dawa City, Ethiopia, form the year 2006 to 2018. The integrated approaches of various sensors satellite data, Spot (2006) and Sentinel 2 (2018) combined with landscape metrics analysis was employed to explore the pattern, process, and overall growth status in the city. The result showed that the built-up area had increased by 62% between 2006 and 2018, at an average annual increment of 3.6%, while the other land covers were lost significantly due to urban expansion. The highest urban expansion has occurred in the northwest direction, whereas the most fragmented landscape pattern was recorded in the west direction. Overall, the analysis showed that Dire Dawa City experienced accelerated urban expansion with a fragmented and complicated spatiotemporal urban landscape patterns, suggesting a strong tendency towards sprawl over the past 12 years. The findings in the study could help planners and policy developers to insight the historical dynamics of the urban region for sustainable development.

Keywords: zonal metrics, multi-temporal, multi-resolution, urban growth, remote sensing data

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Authors: Mohammad Alnabhan, Awni Hammouri, Mustafa Hammad, Anas Al-Badareen, Omamah Al-Thnebat

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Software visualization is one of the main techniques used to simplify the presentation of software systems and enhance their understandability. It is used to present the software system in a visual manner using simple, clear and meaningful symbols. This study proposes a new 2D software visualization approach. In this approach, each class is represented by rectangle, the name of the class placed above the rectangle, the size of class (Line of Code) represented by the height of the rectangle. The methods and the attributes are represented by circles and triangles respectively. The relationships among classes correspond to arrows. The proposed visualization approach was evaluated in terms of applicability and efficiency. Results have confirmed successful implementation of the proposed approach, and its ability to provide a simple and effective graphical presentation of extracted software components and properties.

Keywords: software visualization, software metrics, calling relationships, 2D graphs

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Authors: T. Francis Thamburaj, A. Aloysius

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Polymorphism is one of the main pillars of the object-oriented paradigm. It induces hidden forms of class dependencies which may impact software quality, resulting in higher cost factor for comprehending, debugging, testing, and maintaining the software. In this paper, a new cognitive complexity metric called Cognitive Weighted Polymorphism Factor (CWPF) is proposed. Apart from the software structural complexity, it includes the cognitive complexity on the basis of type. The cognitive weights are calibrated based on 27 empirical studies with 120 persons. A case study and experimentation of the new software metric shows positive results. Further, a comparative study is made and the correlation test has proved that CWPF complexity metric is a better, more comprehensive, and more realistic indicator of the software complexity than Abreu’s Polymorphism Factor (PF) complexity metric.

Keywords: cognitive complexity metric, object-oriented metrics, polymorphism factor, software metrics

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Authors: I. Baddari, A. Riahla, M. Mezghich

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Today in the literature, we discover a lot of routing algorithms which some have been the subject of normalization. Two great classes Routing algorithms are defined, the first is the class reactive algorithms and the second that of algorithms proactive. The aim of this work is to make a comparative study between some routing algorithms. Two comparisons are considered. The first will focus on the protocols of the same class and second class on algorithms of different classes (one reactive and the other proactive). Since they are not based on analytical models, the exact evaluation of some aspects of these protocols is challenging. Simulations have to be done in order to study their performances. Our simulation is performed in NS2 (Network Simulator 2). It identified a classification of the different routing algorithms studied in a metrics such as loss of message, the time transmission, mobility, etc.

Keywords: ad-hoc network routing protocol, simulation, NS2, delay, packet loss, wideband, mobility

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Authors: Saba Ebrahimi, Hedieh Ashrafi

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Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations.

Keywords: classification methods, support vector machine, decision tree, forest cover-type dataset

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Authors: Jennifer Leach, Umashanger Thayasivam

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The use of technology has benefited society in more ways than one ever thought possible. Unfortunately, though, as society’s knowledge of technology has advanced, so has its knowledge of ways to use technology to manipulate people. This has led to a simultaneous advancement in the world of fraud. Machine learning techniques can offer a possible solution to help decrease this advancement. This research explores how the use of various machine learning techniques can aid in detecting fraudulent activity across two different types of fraudulent data, and the accuracy, precision, recall, and F1 were recorded for each method. Each machine learning model was also tested across five different training and testing splits in order to discover which testing split and technique would lead to the most optimal results.

Keywords: data science, fraud detection, machine learning, supervised learning

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Authors: Ramkumar R., Gaurav Shinde, Pratik Shroff, Sachin Kumar Jain, Nagesh Walke

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Advancements in electric vehicles have significantly reduced the powertrain noise and moving components of vehicles. As a result, in-cab noises have become more noticeable to passengers inside the car. To ensure a comfortable ride for drivers and other passengers, it has become crucial to eliminate undesirable component noises during the development phase. Standard practices are followed to identify the severity of noises based on subjective ratings, but it can be a tedious process to identify the severity of each development sample and make changes to reduce it. Additionally, the severity rating can vary from jury to jury, making it challenging to arrive at a definitive conclusion. To address this, an automotive component was identified to evaluate squeak and rattle noise issue. Physical tests were carried out for random and sine excitation profiles. Aim was to subjectively assess the noise using jury rating method and objectively evaluate the same by measuring the noise. Suitable jury evaluation method was selected for the said activity, and recorded sounds were replayed for jury rating. Objective data sound quality metrics viz., loudness, sharpness, roughness, fluctuation strength and overall Sound Pressure Level (SPL) were measured. Based on this, correlation co-efficients was established to identify the most relevant sound quality metrics that are contributing to particular identified noise issue. Regression analysis was then performed to establish the correlation between subjective and objective data. Mathematical model was prepared using artificial intelligence and machine learning algorithm. The developed model was able to predict the subjective rating with good accuracy.

Keywords: BSR, noise, correlation, regression

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Authors: Tusar Kanti Dash, Ganapati Panda

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The evaluation of speech quality and intelligence is critical to the overall effectiveness of the Speech Enhancement Algorithms. Several intrusive and non-intrusive measures are employed to calculate these parameters. Non-Intrusive Evaluation is most challenging as, very often, the reference clean speech data is not available. In this paper, a novel non-intrusive speech evaluation measure is proposed using audio features derived from the Stockwell transform. These features are used with the Light Gradient Boosting Machine for the effective prediction of speech quality and intelligibility. The proposed model is analyzed using noisy and reverberant speech from four databases, and the results are compared with the standard Intrusive Evaluation Measures. It is observed from the comparative analysis that the proposed model is performing better than the standard Non-Intrusive models.

Keywords: non-Intrusive speech evaluation, S-transform, light GBM, speech quality, and intelligibility

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Authors: S. S. Nafee, A. M. Al-Ramady, S. A. Shaheen

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Evaluated nuclear decay data for the 217Po nuclide ispresented in the present work. These data include recommended values for the half-life T1/2, α-, β--, and γ-ray emission energies and probabilities. Decay data from 221Rn α and 217Bi β—decays are presented. Q(α) has been updated based on the recent published work of the Atomic Mass Evaluation AME2012. In addition, the logft values were calculated using the Logft program from the ENSDF evaluation package. Moreover, the total internal conversion electrons has been calculated using Bricc program. Meanwhile, recommendation values or the multi-polarities have been assigned based on recently measurement yield a better intensity balance at the 254 keV and 264 keV gamma transitions.

Keywords: nuclear decay data evaluation, mass evaluation, total converison coefficients, atomic mass evaluation

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Authors: Zuzanna Miodonska, Michal Krecichwost, Pawel Badura

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Sigmatism (lisping) is a speech disorder in which sibilant consonants are mispronounced. The diagnosis of this phenomenon is usually based on the auditory assessment. However, the progress in speech analysis techniques creates a possibility of developing computer-aided sigmatism diagnosis tools. The aim of the study is to statistically verify whether specific acoustic features of sibilant sounds may be related to pronunciation correctness. Such knowledge can be of great importance while implementing classifiers and designing novel tools for automatic sibilants pronunciation evaluation. The study covers analysis of various speech signal measures, including features proposed in the literature for the description of normative sibilants realization. Amplitudes and frequencies of three fricative formants (FF) are extracted based on local spectral maxima of the friction noise. Skewness, kurtosis, four normalized spectral moments (SM) and 13 mel-frequency cepstral coefficients (MFCC) with their 1st and 2nd derivatives (13 Delta and 13 Delta-Delta MFCC) are included in the analysis as well. The resulting feature vector contains 51 measures. The experiments are performed on the speech corpus containing words with selected sibilant sounds (/ʃ, ʒ/) pronounced by 60 preschool children with proper pronunciation or with natural pathologies. In total, 224 /ʃ/ segments and 191 /ʒ/ segments are employed in the study. The Mann-Whitney U test is employed for the analysis of stigmatism and normative pronunciation. Statistically, significant differences are obtained in most of the proposed features in children divided into these two groups at p < 0.05. All spectral moments and fricative formants appear to be distinctive between pathology and proper pronunciation. These metrics describe the friction noise characteristic for sibilants, which makes them particularly promising for the use in sibilants evaluation tools. Correspondences found between phoneme feature values and an expert evaluation of the pronunciation correctness encourage to involve speech analysis tools in diagnosis and therapy of sigmatism. Proposed feature extraction methods could be used in a computer-assisted stigmatism diagnosis or therapy systems.

Keywords: computer-aided pronunciation evaluation, sigmatism diagnosis, speech signal analysis, statistical verification

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Authors: Zhaoqi He

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Research Context: Endurance performance in hot environments is influenced by the interplay of hydration status and physiological responses. This study aims to investigate how dehydration, up to 2.11% body weight loss, affects the 3 km running performance of trained male athletes under conditions mimicking high temperatures. Methodology: In a randomized crossover design, five male athletes participated in two trials – euhydrated (EU) and dehydrated (HYPO). Both trials included a 70-minute preload run at 55-60% VO2max in 32°C and 50% humidity, followed by a 3-kilometer time trial. Fluid intake was restricted in HYPO to induce a 2.11% body weight loss. Physiological metrics, including heart rate, core temperature, and oxygen uptake, were measured, along with perceptual metrics like perceived exertion and thirst sensation. Findings: The 3-kilometer run completion times showed no significant differences between EU and HYPO trials (p=0.944). Physiological indicators, including heart rate, core temperature, and oxygen uptake, did not significantly vary (p>0.05). Thirst sensation was markedly higher in HYPO (p=0.013), confirming successful induction of dehydration. Other perceptual metrics and gastrointestinal comfort remained consistent. Conclusion: Contrary to the hypothesis, the study reveals that dehydration, inducing up to 2.11% body weight loss, does not significantly impair 3 km running performance in trained male athletes under hot conditions. Thirst sensation was notably higher in the dehydrated state, emphasizing the importance of considering perceptual factors in hydration strategies. The findings suggest that trained runners can maintain performance despite moderate dehydration, highlighting the need for nuanced hydration guidelines in hot-weather running.

Keywords: hypohydration, euhydration, hot environment, 3km running time trial, endurance performance, trained athletes, perceptual metrics, dehydration impact, physiological responses, hydration strategies

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Authors: Kostas Metaxiotis, Kostas Liagkouras

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This study presents an Expert System specially designed to be used with Multiobjective Evolutionary Algorithms (MOEAs) for the solution of the portfolio selection problem. The validation of the proposed hybrid System is done by using data sets from Hang Seng 31 in Hong Kong, DAX 100 in Germany and FTSE 100 in UK. The performance of the proposed system is assessed in comparison with the Non-dominated Sorting Genetic Algorithm II (NSGAII). The evaluation of the performance is based on different performance metrics that evaluate both the proximity of the solutions to the Pareto front and their dispersion on it. The results show that the proposed hybrid system is efficient for the solution of this kind of problems.

Keywords: expert systems, multi-objective optimization, evolutionary algorithms, portfolio selection

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Authors: Suratno

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Metacognition thinking skills should be developed early on in learning. The aim of research builds metacognition thinking skills through collaborative learning with self-evaluation. Approach to action research study involving 32 middle school students in Jember Indonesia. Indicators metacognition skills consist of planning, information management strategies, comprehension monitoring, and debugging strategies. Data were analyzed by t test and analysis of instructional videos. Results of the study here were significant differences in metacognition skills before and after the implementation of collaborative learning with self-evaluation. Analysis instructional video showing the difference artifacts of student learning activities to learning before and after implementation of collaborative learning with self-evaluation. Self-evaluation makes students familiar practice thinking skills metacognition.

Keywords: metacognition, collaborative, evaluation, thinking skills

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Authors: Guangzhuang Yu, Chun-Chu Liu

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With the advent of the Internet era in recent years, social music education has gradually shifted from the original entity education mode to the mode of entity plus network teaching. No matter for school music education, professional music education or social music education, the teaching quality is the most important evaluation index. Regarding the research on teaching quality evaluation, scholars at home and abroad have contributed a lot of research results on the basis of multiple methods and evaluation subjects. However, to our best knowledge the complete evaluation model for the virtual teaching interaction mode of the emerging network music education Application (APP) has not been established. This research firstly found out the basic dimensions that accord with the teaching quality required by the three parties, constructing the quality evaluation index system; and then, on the basis of expounding the connotation of each index, it determined the weight of each index by using method of fuzzy analytic hierarchy process, providing ideas and methods for scientific, objective and comprehensive evaluation of the teaching quality of network education APP.

Keywords: network music education APP, teaching quality evaluation, index and connotation

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Authors: Volkan Uygun, H. Birkan Yilmaz, Tuna Tugcu

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This study focuses on the development and use of algorithms that address the issue of resource management in response to emergency and disaster situations. The presented system, named Disaster Management Platform (DMP), takes the data from the data sources of service providers and distributes the incoming requests accordingly both to manage load balancing and minimize service time, which results in improved user satisfaction. Three different resource management algorithms, which give different levels of importance to load balancing and service time, are proposed for the study. The first one is the Minimum Distance algorithm, which assigns the request to the closest resource. The second one is the Minimum Load algorithm, which assigns the request to the resource with the minimum load. Finally, the last one is the Hybrid algorithm, which combines the previous two approaches. The performance of the proposed algorithms is evaluated with respect to waiting time, success ratio, and maximum load ratio. The metrics are monitored from simulations, to find the optimal scheme for different loads. Two different simulations are performed in the study, one is time-based and the other is lambda-based. The results indicate that, the Minimum Load algorithm is generally the best in all metrics whereas the Minimum Distance algorithm is the worst in all cases and in all metrics. The leading position in performance is switched between the Minimum Distance and the Hybrid algorithms, as lambda values change.

Keywords: emergency and disaster response, resource management algorithm, disaster situations, disaster management platform

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Authors: Iffat Ara Ebu, Fahmida Islam, Mohammad Abdus Shahid Rafi, Mahfuzur Rahman, Umar Iqbal, John Ball

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The application of multi-sensor fusion for enhanced distance estimation accuracy in dynamic environments is crucial for advanced driver assistance systems (ADAS) and autonomous vehicles. Limitations of single sensors such as cameras or radar in adverse conditions motivate the use of combined camera and radar data to improve reliability, adaptability, and object recognition. A multi-sensor fusion approach using an extended Kalman filter (EKF) is proposed to combine sensor measurements with a dynamic system model, achieving robust and accurate distance estimation. The research utilizes the Mississippi State University Autonomous Vehicular Simulator (MAVS) to create a controlled environment for data collection. Data analysis is performed using MATLAB. Qualitative (visualization of fused data vs ground truth) and quantitative metrics (RMSE, MAE) are employed for performance assessment. Initial results with simulated data demonstrate accurate distance estimation compared to individual sensors. The optimal sensor measurement noise variance and plant noise variance parameters within the EKF are identified, and the algorithm is validated with real-world data from a Chevrolet Blazer. In summary, this research demonstrates that multi-sensor fusion with an EKF significantly improves distance estimation accuracy in dynamic environments. This is supported by comprehensive evaluation metrics, with validation transitioning from simulated to real-world data, paving the way for safer and more reliable autonomous vehicle control.

Keywords: sensor fusion, EKF, MATLAB, MAVS, autonomous vehicle, ADAS

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Authors: Hochan Seok, Woosik Jang, Seung-Heon Han

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The stagnant global construction market has intensified competition since 2008 among firms that aim to win overseas contracts. Against this backdrop, subcontractor selection is identified as one of the most critical success factors in overseas construction project. However, it is difficult to select qualified subcontractors due to the lack of evaluation standards and reliability. This study aims to identify the problems associated with existing subcontractor evaluations using a correlations analysis and a multiple regression analysis with pre-qualification and performance evaluation of 121 firms in six countries.

Keywords: subcontractor evaluation system, pre-qualification, performance evaluation, correlation analysis, multiple regression analysis

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Authors: Mohammadali Tavakoli, Atefeh Heydari, Zuriati Ismail, Naomie Salim

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With the increasing number of people reviewing products online in recent years, opinion sharing websites has become the most important source of customers’ opinions. Unfortunately, spammers generate and post fake reviews in order to promote or demote brands and mislead potential customers. These are notably destructive not only for potential customers but also for business holders and manufacturers. However, research in this area is not adequate, and many critical problems related to spam detection have not been solved to date. To provide green researchers in the domain with a great aid, in this paper, we have attempted to create a high-quality framework to make a clear vision on review spam-detection methods. In addition, this report contains a comprehensive collection of detection metrics used in proposed spam-detection approaches. These metrics are extremely applicable for developing novel detection methods.

Keywords: fake reviews, feature collection, opinion spam, spam detection

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Authors: Ricardo Avila, Gabriel Lopes, Vania Vidal, Jose Macedo

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This proposal aims for semantic enrichment between glossaries using the Simple Knowledge Organization System (SKOS) vocabulary to discover synonyms, hyponyms and hyperonyms semiautomatically, in Brazilian Portuguese, generating new semantic relationships based on WordNet. To evaluate the quality of this proposed model, experiments were performed by the use of two sets containing new relations, being one generated automatically and the other manually mapped by the domain expert. The applied evaluation metrics were precision, recall, f-score, and confidence interval. The results obtained demonstrate that the applied method in the field of Oil Production and Extraction (E&P) is effective, which suggests that it can be used to improve the quality of terminological mappings. The procedure, although adding complexity in its elaboration, can be reproduced in others domains.

Keywords: ontology matching, mapping enrichment, semantic web, linked data, SKOS

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Authors: Prateek Rastogi

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Medical teaching in present era concentrates not only on teaching but on effective teaching. For effective teaching a combination of effective carefully designed curriculum, an educated educator, competent learner and fool proof evaluation system is required. Keeping these parameters in mind and study was undertaken at Kasturba Medical College, Mangalore among medical students. In this study, evaluation of Forensic Medicine syllabus along with its teaching and evaluation methodology was done using 20 different parameters. This questionnaire based study was done over a period of two years i.e. 2013 and 2014. Batch of students who just passed the forensic medicine subject was included for study. Carefully designed questionnaire contained questions related to course content, teaching methodology and evaluation system along with provisions to mention merits and demerits of subject. The feedbacks in first round were analyzed and suggestions were implemented before conducting the second round of study. Overall evaluation of course was done as well as it was compared with other subjects of second MBBS. It was noted that Scores improved in 2nd survey thus stressing the importance of course evaluation and student feedback in teaching improvement.

Keywords: teaching methodology, system of evaluation, course content, bioinformatics, biomedicine

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Authors: Li Donghong, Peng Fuyang, Yang Guanghua, Su Xiaoyan

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Using open source code is a popular method of software development. How to evaluate the quality of software becomes more important. This paper introduces an evaluation model. The model evaluates the quality from four dimensions: technology, production, management, and development. Each dimension includes many indicators. The weight of indicator can be modified according to the purpose of evaluation. The paper also introduces a method of using the model. The evaluating result can provide good advice for evaluating or purchasing the software.

Keywords: evaluation model, software quality, open source code, evaluation indicator

Procedia PDF Downloads 390