Search results for: classification quality

Authors: Lily Ingsrisawang, Tasanee Nacharoen

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Introduction: The problems of unbalanced data sets generally appear in real world applications. Due to unequal class distribution, many research papers found that the performance of existing classifier tends to be biased towards the majority class. The k -nearest neighbors’ nonparametric discriminant analysis is one method that was proposed for classifying unbalanced classes with good performance. Hence, the methods of discriminant analysis are of interest to us in investigating misclassification error rates for class-imbalanced data of three diabetes risk groups. Objective: The purpose of this study was to compare the classification performance between parametric discriminant analysis and nonparametric discriminant analysis in a three-class classification application of class-imbalanced data of diabetes risk groups. Methods: Data from a healthy project for 599 staffs in a government hospital in Bangkok were obtained for the classification problem. The staffs were diagnosed into one of three diabetes risk groups: non-risk (90%), risk (5%), and diabetic (5%). The original data along with the variables; diabetes risk group, age, gender, cholesterol, and BMI was analyzed and bootstrapped up to 50 and 100 samples, 599 observations per sample, for additional estimation of misclassification error rate. Each data set was explored for the departure of multivariate normality and the equality of covariance matrices of the three risk groups. Both the original data and the bootstrap samples show non-normality and unequal covariance matrices. The parametric linear discriminant function, quadratic discriminant function, and the nonparametric k-nearest neighbors’ discriminant function were performed over 50 and 100 bootstrap samples and applied to the original data. In finding the optimal classification rule, the choices of prior probabilities were set up for both equal proportions (0.33: 0.33: 0.33) and unequal proportions with three choices of (0.90:0.05:0.05), (0.80: 0.10: 0.10) or (0.70, 0.15, 0.15). Results: The results from 50 and 100 bootstrap samples indicated that the k-nearest neighbors approach when k = 3 or k = 4 and the prior probabilities of {non-risk:risk:diabetic} as {0.90:0.05:0.05} or {0.80:0.10:0.10} gave the smallest error rate of misclassification. Conclusion: The k-nearest neighbors approach would be suggested for classifying a three-class-imbalanced data of diabetes risk groups.

Keywords: error rate, bootstrap, diabetes risk groups, k-nearest neighbors

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Authors: Danilo Habermann, Aleksander Medella, Carla Cremon, Yusef Caceres

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This paper aims to analyze the ability of 2d point clouds features to classify different models of helicopters using radars. This method does not need to estimate the blade length, the number of blades of helicopters, and the period of their micro-Doppler signatures. It is also not necessary to generate spectrograms (or any other image based on time and frequency domain). This work transforms a radar return signal into a 2D point cloud and extracts features of it. Three classifiers are used to distinguish 9 different helicopter models in order to analyze the performance of the features used in this work. The high accuracy obtained with each of the classifiers demonstrates that the 2D point clouds features are very useful for classifying helicopters from radar signal.

Keywords: helicopter classification, point clouds features, radar, supervised classifiers

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Authors: T. M. Al Muhareb

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Any organization should take into consideration the customers’ satisfaction while providing any service to their customers. The quality of services is always considered as the main aspect that attracts the customers’ attention and helps the airports to develop their services and operations. King Khalid International Airport is considered as the gateway of the Kingdom of Saudi Arabia. Therefore, the aim of this paper was to identify the quality service in the departure area at in King Khalid Airport. The SERVQUAL questionnaire was distributed among the passengers in King Khalid International Airport and the respondents have reached to 500 passengers. The results that are obtained from the SERVQUAL questionnaire showed that the quality of airport’s services is low.

Keywords: service quality, SERVQUAL methodology, King Khalid International Airport (KKIA), customers’ satisfaction

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Authors: Dina Ahmed Selim, Eman Shawky Anwar, Rasha Mohamed Abu El-Khair

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A rapid, simple and efficient method with minimal sample treatment was developed for authentication of Garcinia cambogia fruit peel powder, along with determining undeclared active pharmaceutical ingredients (APIs) in its herbal slimming dietary supplements using near infrared spectroscopy combined with chemometrics. Five featured adulterants, including sibutramine, metformin, orlistat, ephedrine, and theophylline are selected as target compounds. The Near infrared spectral data matrix of authentic Garcinia cambogia fruit peel and specimens degraded by intentional contamination with the five selected APIs was subjected to hierarchical clustering analysis to investigate their bundling figure. SIMCA models were established to ensure the genuiness of Garcinia cambogia fruit peel which resulted in perfect classification of all tested specimens. Adulterated samples were utilized for construction of PLSR models based on different APIs contents at minute levels of fraud practices (LOQ < 0.2% w/w).The suggested approach can be applied to enhance and guarantee the safety and quality of Garcinia fruit peel powder as raw material and in dietary supplements.

Keywords: Garcinia cambogia, Quality control, NIR spectroscopy, Chemometrics

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Authors: Hassan Y. Kikhia, Jin P. Zhang, Khaldoon G. Albiatr

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The Enron and Arthur Andersen scandal has raised concerns internationally about auditor independence and audit quality. Furthermore, the debate continues about the relationship between audit fees, auditor tenure and audit quality in spite of extensive empirical evidence examining audit failures and earnings management. Therefore, the purpose of current research is to determine the effect of audit fee and audit tenure both partially and simultaneously on the audit quality. Using a sample of Chinese firms, an environment where we believe it provides us with an opportunity to test whether the development of market and legal institutions affects the impact of audit fees and auditor tenure on audit quality. We employ the standard deviation of residuals from regressions relating current accruals to cash flows as proxy for audit quality. The paper documents statistically significant negative association between audit fees and audit quality. These findings are consistent with economic bonding being a determinant of auditor behavior rather than auditor reputational concerns. Further, the current paper shows a positive association between auditor tenure and audit quality in the earlier years of audit tenure. These results support the proposition that when the Learning Effect dominates the Bonding Effect in the earlier years of tenure, then audit quality is likely to be higher. Taken audit fees and audit tenure together, the results suggest that there is positive association between audit fees and audit quality in the earlier years of auditor tenure. Interestingly, the findings of our study have important implications for auditors, policymakers, multinational firms, and users of financial reports. As the rapid growth of China's economy gains global recognition, the Chinese stock market is capturing the attention of international investors. To a lesser extent, our paper also differs from the prior studies in methodology and findings in the investigation of audit quality.

Keywords: audit quality, accruals quality, audit fees, auditor tenure

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Authors: Andhiani Sharfina Arnellya, Mochammad Indra Permana, Dika Pramita Destiani, Ellin Febrina

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Health Research Data, Ministry of Health of Indonesia in 2007, showed coronary heart disease (CHD) or coronary artery disease (CAD) was the third leading cause of death in Indonesia after hypertension and stroke with 7.2% incidence rate. Antiplatelet is one of the important therapy in management of patients with CHD. In addition to therapeutic effect on patients, quality of life is one aspect of another assessment to see the success of antiplatelet therapy. The purpose of this study was to determine the quality of life of patients on oral antiplatelet therapy in outpatient cardiac department Dr. Hasan Sadikin central general hospital, Bandung, Indonesia. This research is a cross sectional by collecting data through quality of life questionnaire of patients which performed prospectively as primary data and secondary data from medical record of patients. The results of this study showed that 54.3% of patients had a good quality of life, 45% had a moderate quality of life, and 0.7% had a poor quality of life. There are no significant differences in quality of life-based on age, gender, diagnosis, and duration of drug use.

Keywords: antiplatelet, quality of life, coronary artery disease, coronary heart disease

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Authors: Danielle Shackley, Yetunde Folajimi

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As more people turn to the internet seeking health-related information, there is more risk of finding false, inaccurate, or dangerous information. Sentiment analysis is a natural language processing technique that assigns polarity scores to text, ranging from positive, neutral, and negative. In this research, we evaluate the weight of a sentiment analysis feature added to fake health news classification models. The dataset consists of existing reliably labeled health article headlines that were supplemented with health information collected about COVID-19 from social media sources. We started with data preprocessing and tested out various vectorization methods such as Count and TFIDF vectorization. We implemented 3 Naive Bayes classifier models, including Bernoulli, Multinomial, and Complement. To test the weight of the sentiment analysis feature on the dataset, we created benchmark Naive Bayes classification models without sentiment analysis, and those same models were reproduced, and the feature was added. We evaluated using the precision and accuracy scores. The Bernoulli initial model performed with 90% precision and 75.2% accuracy, while the model supplemented with sentiment labels performed with 90.4% precision and stayed constant at 75.2% accuracy. Our results show that the addition of sentiment analysis did not improve model precision by a wide margin; while there was no evidence of improvement in accuracy, we had a 1.9% improvement margin of the precision score with the Complement model. Future expansion of this work could include replicating the experiment process and substituting the Naive Bayes for a deep learning neural network model.

Keywords: sentiment analysis, Naive Bayes model, natural language processing, topic analysis, fake health news classification model

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Authors: Jigg Pelayo, Ricardo Villar, Einstine Opiso

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The rampant and unintended spread of urban areas resulted in increasing artificial component features in the land cover types of the countryside and bringing forth the urban heat island (UHI). This paved the way to wide range of negative influences on the human health and environment which commonly relates to air pollution, drought, higher energy demand, and water shortage. Land cover type also plays a relevant role in the process of understanding the interaction between ground surfaces with the local temperature. At the moment, the depiction of the land surface temperature (LST) at city/municipality scale particularly in certain areas of Misamis Oriental, Philippines is inadequate as support to efficient mitigations and adaptations of the surface urban heat island (SUHI). Thus, this study purposely attempts to provide application on the Landsat 8 satellite data and low density Light Detection and Ranging (LiDAR) products in mapping out quality automated LST model and crop-level land cover classification in a local scale, through theoretical and algorithm based approach utilizing the principle of data analysis subjected to multi-dimensional image object model. The paper also aims to explore the relationship between the derived LST and land cover classification. The results of the presented model showed the ability of comprehensive data analysis and GIS functionalities with the integration of object-based image analysis (OBIA) approach on automating complex maps production processes with considerable efficiency and high accuracy. The findings may potentially lead to expanded investigation of temporal dynamics of land surface UHI. It is worthwhile to note that the environmental significance of these interactions through combined application of remote sensing, geographic information tools, mathematical morphology and data analysis can provide microclimate perception, awareness and improved decision-making for land use planning and characterization at local and neighborhood scale. As a result, it can aid in facilitating problem identification, support mitigations and adaptations more efficiently.

Keywords: LiDAR, OBIA, remote sensing, local scale

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Authors: Aman Kaur, Colin Ribton, Wamadeva Balachandaran

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Electron beam welding due to its inherent advantages is being extensively used for material processing where high precision is required. Especially in aerospace or nuclear industries, there are high quality requirements and the cost of materials and processes is very high which makes it very important to ensure the beam quality is maintained and checked prior to carrying out the welds. Although the processes in these industries are highly controlled, however, even the minor changes in the operating parameters of the electron gun can make large enough variations in the beam quality that can result in poor welding. To measure the beam quality a simple device has been designed that can be used at high powers. The device consists of two slits in x and y axis which collects a small portion of the beam current when the beam is deflected over the slits. The signals received from the device are processed in data acquisition hardware and the dedicated software developed for the device. The device has been used in controlled laboratory environments to analyse the signals and the weld quality relationships by varying the focus current. The results showed matching trends in the weld dimensions and the beam characteristics. Further experimental work is being carried out to determine the ability of the device and signal processing software to detect subtle changes in the beam quality and to relate these to the physical weld quality indicators.

Keywords: electron beam welding, beam quality, high power, weld quality indicators

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Authors: Nonlapun Meenil, Pisitpong Intarapong, Thitima Wongsheree, Pranchalee Samanpiboon

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In Thailand, once pineapples are harvested, they must be classified into two classes based on their sweetness: sweet and unsweet. This paper has studied and developed the assessment of internal quality of pineapples using a low-cost compact spectroscopy sensor according to the Spectroscopy approach and Neural Network (NN). During the experiments, Batavia pineapples were utilized, generating 100 samples. The extracted pineapple juice of each sample was used to determine the Soluble Solid Content (SSC) labeling into sweet and unsweet classes. In terms of experimental equipment, the sensor cover was specifically designed to install the sensor and light source to read the reflectance at a five mm depth from pineapple flesh. By using a spectroscopy sensor, data on visible and near-infrared reflectance (Vis-NIR) were collected. The NN was used to classify the pineapple classes. Before the classification step, the preprocessing methods, which are Class balancing, Data shuffling, and Standardization were applied. The 510 nm and 900 nm reflectance values of the middle parts of pineapples were used as features of the NN. With the Sequential model and Relu activation function, 100% accuracy of the training set and 76.67% accuracy of the test set were achieved. According to the abovementioned information, using a low-cost compact spectroscopy sensor has achieved favorable results in classifying the sweetness of the two classes of pineapples.

Keywords: neural network, pineapple, soluble solid content, spectroscopy

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Authors: Rizwan Ali, Hammad Zafar

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The purpose of this research is to investigate the mediation effect of customer satisfaction in the relationship between service quality, corporate image to customer loyalty, in Pakistan banking sector. The population of this research is banking customers and sample size of 210 respondents. This research uses the SPSS, Correlation, ANOVA and regression analysis techniques along with AMOS methods. The service quality and corporate image applied by the banks are not all variables can directly affect customer loyalty, but must first going through satisfaction. Which means that banks must first need to understand what the customer basic needs through variable service quality and corporate image so that the customers feel loyal when the level of satisfaction is resolved. The service quality provided by the banking industry needs to be improved in order to improve customer satisfaction and loyalty of banking services, especially for banks in Pakistan.

Keywords: customer loyalty, service quality, corporate image, customer satisfaction

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Authors: Ayala Kobo-Greenhut, Amos Notea, Izhar Ben-Shlomo

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In R&D projects, there is no doubt about the need to change a current concept to an alternative one over time (i.e., concept leaping). Concept leaping is required since with most R&D projects uncertainty is present as they take place in dynamic environments. Despite the importance of concept leaping when needed, R&D teams may fail to do so (i.e., frozen concept). This research suggests a possible reason why frozen concept happens in the framework of quality engineering and control engineering. We suggest that frozen concept occurs since concept determines the derived plan and its implementation may be considered as equivalent to a closed-loop process, and is subject to the problem of not recognizing gaps as failures. We suggest that although implementing quality practices into an R&D project’s routine has many advantages, it intensifies the frozen concept problem since working according to quality practices relates to exploitation of learning behavior, while leaping to a new concept relates to exploring learning behavior.

Keywords: closed loop, control engineering, design, leaping, frozen concept, quality engineering, quality practices

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Authors: Hsin-Hung Lai, Wei Lo

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Whether it is in virtue or vice for a construction quality of public construction project, it is one of the important indicators for national economic development and overall construction, the impact on the quality of national life is very deep. In recent years, a number of scandal of public construction project occurred, the requirements of the government agencies and the public require the quality of construction of public construction project are getting stricter than ever, the three-level public construction project construction quality of quality control system implemented by the government has a profound impact. This study mainly aggregated the evolution of ISO 9000 quality control system, the difference between the practice of implementing management of construction quality by many countries and three-level quality control of our country, so we explored and found that almost all projects of enhancing construction quality are dominated by civil organizations in foreign countries, whereas, it is induced by the national power in our country and develop our three-level quality control system and audit mechanism based on IOS system and implement the works by legislation, we also explored its enhancement and relevance with construction quality of public construction project that are intervened by such system and national power, and it really presents the effectiveness of construction quality been enhanced by the audited result. The three-level quality control system of our country to promote the policy of public construction project is almost same with the quality control system of many developed countries; however our country mainly implements such system on public construction project only, we promote the three-level quality control system is for enhancing the quality of public construction project, for establishing effective quality management system, so as to urge, correct and prevent the defects of quality management by manufacturers, whereas, those developed countries is comprehensively promoting (both public construction project and civil construction) such system. Therefore, this study is to explore the scope for public construction project only; the most important is the quality recognition by the executor, either good quality or deterioration is not a single event, there is a certain procedure extends from the demand and feasibility analysis, design, tendering, contracting, construction performance, inspection, continuous improvement, completion and acceptance, transferring and meeting the needs of the users, all of mentioned above have a causal relationship and it is a systemic problems. So the best construction quality would be manufactured and managed by reasonable cost if it is by extensive thinking and be preventive. We aggregated the implemented results in the past 10 years (2005 to 2015), the audited results of both in central units and local ones were slightly increased in A-grade while those listed in B-grade were decreased, although the levels were not evidently upgraded, yet, such result presents that the construction quality of concept of manufacturers are improving, and the construction quality has been established in the design stage, thus it is relatively beneficial to the enhancement of construction quality of overall public construction project.

Keywords: ISO 9000, three-level quality control system, audit and review mechanism for construction implementation, quality of construction implementation

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Authors: Abdullah Alshehab

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Governments worldwide are intensely focused on digitizing public transactions to establish reliable e-government services. The advent of new digital technologies and ongoing advancements in ICT have profoundly transformed business operations. Citizen engagement and participation in e-government services are crucial for the system's success. However, it is essential to measure and enhance citizen satisfaction levels to effectively evaluate and improve these systems. Citizen satisfaction is a key criterion that allows government institutions to assess the quality of their services. There is a strong connection between information quality, service quality, and system quality, all of which directly impact user satisfaction. Additionally, both system quality and information quality have indirect effects on citizen satisfaction. A causal map, which is a network diagram representing causes and effects, can illustrate these relationships. According to the literature, the main factors influencing citizen satisfaction are trust, reliability, citizen support, convenience, and transparency. This paper investigates the causal relationships among these factors and identifies any interrelatedness between them.

Keywords: e-government services, e-satisfaction, citizen satisfaction, causal map.

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Authors: Roghayeh Karami, Ahmad Gharaei

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Backgrounds and objectives: This study was undertaken at four different sites (north polluted, south polluted, south healthy and north healthy) in Tehran, in order to examine whether there was a relationship between publicly available air quality data and the public’s perception of air quality and to suggest some guidelines for reducing air pollution. Materials and Methods: A total of 200 people were accidentally filled out the research questionnaires at mentioned sites and air quality data were obtained simultaneously from the Air Quality Control Department. Data was analyzed in Excel and SPSS software. Results: Clean air and secure job were of great importance to people comparing to other pleasant aspect of life. Also air pollution and fear of dangerous diseases were the most important of people concerns. The Indies bored /news paper services on air quality were little used by the public as a means of obtaining information on air pollution. Using public transportation and avoid unessential journeys are the most important ways for reducing air pollution. Conclusion: The results reveal that the public’s perception of air quality is not a reliable indicator of the actual levels of air pollution. Current earths to down actions are not effective and enough in reducing air pollution, therefore it seems participatory management and public participation is suitable guideline.

Keywords: air pollution, quality of life, opinion poll, public participation

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Authors: Fateme Hadadian, Behnam Khaledi Paveh, Hosein Feizi

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Background: High number of patients with end-stage renal disease worldwide, and Iran and the patients required hemodialysis, As well as symptoms and treatment process and its impact on quality of life The researcher had to take a step towards solving these problems. Methods: In randomized clinical trial in 60 hemodialysis patients admitted to hospital hemodialysis Imam Reza (AS) were studied. Using questionnaires dialysis patients' QOL, quality of life was measured in patients and controls were divided randomly into two groups. Benson's relaxation method for the experimental group and two months at home, once per day, respectively and the control group received no special action. Immediately after the end of the period with was used for evaluating the quality of life in both the experimental and control groups were survey and data using independent t-test were used for statistical analysis. Results: The general dimensions of quality of life scores before and after intervention, there was significant difference (P=0/001). But this difference was not significant after QOL (P=0/2). Between QOL scores before and after treatment between the two groups was statistically significant (P=0/02). Conclusion: Benson relaxation has the desired effect on quality of life in hemodialysis patients and can be used as a useful method to enhance the quality of life in hemodialysis patients, implementation and training will be given.

Keywords: hemodialysis, quality of life, Benson muscle relaxation, biomedicine

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Authors: C. Silveira, A. Ascenso, J. Ferreira, A. I. Miranda, P. Tuccella, G. Curci

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Poor air quality is one of the main environmental causes of premature deaths worldwide, and mainly in cities, where the majority of the population lives. It is a consequence of successive land cover (LC) and use changes, as a result of the intensification of human activities. Knowing these landscape modifications in a comprehensive spatiotemporal dimension is, therefore, essential for understanding variations in air pollutant concentrations. In this sense, the use of air quality models is very useful to simulate the physical and chemical processes that affect the dispersion and reaction of chemical species into the atmosphere. However, the modelling performance should always be evaluated since the resolution of the input datasets largely dictates the reliability of the air quality outcomes. Among these data, the updated LC is an important parameter to be considered in atmospheric models, since it takes into account the Earth’s surface changes due to natural and anthropic actions, and regulates the exchanges of fluxes (emissions, heat, moisture, etc.) between the soil and the air. This work aims to evaluate the performance of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), when different LC classifications are used as an input. The influence of two LC classifications was tested: i) the 24-classes USGS (United States Geological Survey) LC database included by default in the model, and the ii) CLC (Corine Land Cover) and specific high-resolution LC data for Portugal, reclassified according to the new USGS nomenclature (33-classes). Two distinct WRF-Chem simulations were carried out to assess the influence of the LC on air quality over Europe and Portugal, as a case study, for the year 2015, using the nesting technique over three simulation domains (25 km², 5 km² and 1 km² horizontal resolution). Based on the 33-classes LC approach, particular emphasis was attributed to Portugal, given the detail and higher LC spatial resolution (100 m x 100 m) than the CLC data (5000 m x 5000 m). As regards to the air quality, only the LC impacts on tropospheric ozone concentrations were evaluated, because ozone pollution episodes typically occur in Portugal, in particular during the spring/summer, and there are few research works relating to this pollutant with LC changes. The WRF-Chem results were validated by season and station typology using background measurements from the Portuguese air quality monitoring network. As expected, a better model performance was achieved in rural stations: moderate correlation (0.4 – 0.7), BIAS (10 – 21µg.m^-3) and RMSE (20 – 30 µg.m^-3), and where higher average ozone concentrations were estimated. Comparing both simulations, small differences grounded on the Leaf Area Index and air temperature values were found, although the high-resolution LC approach shows a slight enhancement in the model evaluation. This highlights the role of the LC on the exchange of atmospheric fluxes, and stresses the need to consider a high-resolution LC characterization combined with other detailed model inputs, such as the emission inventory, to improve air quality assessment.

Keywords: land use, spatial resolution, WRF-Chem, air quality assessment

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Authors: Akinbuluma Ayodeji Theophilus, Adewale Olutaiwo

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When building roads and paved surfaces, a strong foundation is always essential. A durable material that can withstand years of traffic while staying trustworthy must be used to build the foundation. A frequent problem in the construction of roads and pavements is the lack of high-quality, long-lasting materials for the pavement structure (base, subbase, and subgrade). Hence, this study examined the stabilization of lateritic soil samples from Ijoko with cement kiln dust and lime. The study adopted the experimental design. Laboratory tests were conducted on classification, swelling potential, compaction, California bearing ratio (CBR), and unconfined compressive tests, among others, were conducted on the laterite sample treated with cement kiln dust (CKD) and lime in incremental order of 2% up to 10% of dry weight soft soil sample. The results of the test showed that the studied soil could be classified as an A-7-6 and CL soil using the American Association of State Highway and transport officials (AASHTO) and the unified soil classification system (USCS), respectively. The plasticity (PI) of the studied soil reduced from 30.5% to 29.9% at the application of CKD. The maximum dry density on the application of CKD reduced from 1.9.7 mg/m3 to 1.86mg/m3, and lime application yielded a reduction from 1.97mg/m3 to 1.88.mg/m3. The swell potential on CKD application was reduced from 0.05 to 0.039%. The study concluded that soil stabilizations are effective and economic way of improving road pavement for engineering benefit. The degree of effectiveness of stabilization in pavement construction was found to depend on the type of soil to be stabilized. The study therefore recommended that stabilized soil mixtures should be used to subbase material for flexible pavement since is a suitable.

Keywords: lateritic soils, sand, cement, stabilization, road pavement

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Authors: Fakhruddin Esa, Yusri Yusof

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The confirmatory of Total Quality Management (TQM) implementation is most vital in quality management. This paper focuses on employees' perceptions towards TQM implementation in a local automotive company supplier. The objectives of this study are first and foremost to determine the perception of TQM implementation among the staff, and secondly to ascertain the correlation between the variables, and lastly to identify the relative influence of the 10 TQM variables on the cost of poor quality (COPQ). The TQM implementation is perceived to be moderate. All correlation is found to be significant and five variables having positively moderate to high correlation. Out of 10 variables, quality system improvement, reward and recognition and customer focus influence the perceived COPQ. This study extended a discussion on these three variables contribution to TQM in general and the human resource development in the organization. A significant recommendation to lowering costs of internal error, such as trouble shooting and scraps are also discussed. Certain components of further research that would add value to this study have also been suggested and perhaps could be implemented at policy-level initiatives.

Keywords: cost of poor quality (COPQ), correlation, total quality management (TQM), variables

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Authors: Rosa Figueroa, Christopher Flores

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Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

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Authors: Eyob Seife, Tesfalem Teshome, Bereket Seyoum, Behailu Getachew, Yohans Demis

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Developing countries continue to face preventable communicable diseases, such as vaccine-preventable diseases. The Expanded Programme on Immunization (EPI) was established by the World Health Organization in 1974 to control these diseases. Health data use is crucial in decision-making, but ensuring data quality remains challenging. The study aimed to assess the accuracy ratio, timeliness, and quality index of regular immunization programme data in the Birkod district of the Somali Region, Ethiopia. For poor data quality, technical, contextual, behavioral, and organizational factors are among contributors. The study used a quantitative cross-sectional design conducted in September 2022GC using WHO-recommended data quality self-assessment tools. The accuracy ratio and timeliness of reports on regular immunization programmes were assessed for two health centers and three health posts in the district for one fiscal year. Moreover, the quality index assessment was conducted at the district level and health facilities by trained assessors. The study found poor data quality in the accuracy ratio and timeliness of reports at all health units, which includes zeros. Overreporting was observed for most facilities, particularly at the health post level. Health centers showed a relatively better accuracy ratio than health posts. The quality index assessment revealed poor quality at all levels. The study recommends that responsible bodies at different levels improve data quality using various approaches, such as the capacitation of health professionals and strengthening the quality index components. The study highlighted the need for attention to data quality in general, specifically at the health post level, and improving the quality index at all levels, which is essential.

Keywords: Birkod District, data quality, quality index, regular immunization programme, Somali Region-Ethiopia

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Authors: H. Abas Nejad, A. R. Teymoori

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Facial expression recognition is one of the open problems in computer vision. Robust neutral face recognition in real time is a major challenge for various supervised learning based facial expression recognition methods. This is due to the fact that supervised methods cannot accommodate all appearance variability across the faces with respect to race, pose, lighting, facial biases, etc. in the limited amount of training data. Moreover, processing each and every frame to classify emotions is not required, as the user stays neutral for the majority of the time in usual applications like video chat or photo album/web browsing. Detecting neutral state at an early stage, thereby bypassing those frames from emotion classification would save the computational power. In this work, we propose a light-weight neutral vs. emotion classification engine, which acts as a preprocessor to the traditional supervised emotion classification approaches. It dynamically learns neutral appearance at Key Emotion (KE) points using a textural statistical model, constructed by a set of reference neutral frames for each user. The proposed method is made robust to various types of user head motions by accounting for affine distortions based on a textural statistical model. Robustness to dynamic shift of KE points is achieved by evaluating the similarities on a subset of neighborhood patches around each KE point using the prior information regarding the directionality of specific facial action units acting on the respective KE point. The proposed method, as a result, improves ER accuracy and simultaneously reduces the computational complexity of ER system, as validated on multiple databases.

Keywords: neutral vs. emotion classification, Constrained Local Model, procrustes analysis, Local Binary Pattern Histogram, statistical model

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Authors: Fernanda A. P. Peres, Thiago N. Peres, Flavio S. Fogliatto, Michel J. Anzanello

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Batch processes are widely used in food industry and have an important role in the production of high added value products, such as chocolate. Process performance is usually described by variables that are monitored as the batch progresses. Data arising from these processes are likely to display a strong correlation-autocorrelation structure, and are usually monitored using control charts based on multiway principal components analysis (MPCA). Process control of a new batch is carried out comparing the trajectories of its relevant process variables with those in a reference set of batches that yielded products within specifications; it is clear that proper determination of the reference set is key for the success of a correct signalization of non-conforming batches in such quality control schemes. In chocolate manufacturing, misclassifications of non-conforming batches in the conching phase may lead to significant financial losses. In such context, the accuracy of process control grows in relevance. In addition to that, the main assumption in MPCA-based monitoring strategies is that all batches are synchronized in duration, both the new batch being monitored and those in the reference set. Such assumption is often not satisfied in chocolate manufacturing process. As a consequence, traditional techniques as MPCA-based charts are not suitable for process control and monitoring. To address that issue, the objective of this work is to compare the performance of three dynamic time warping (DTW) methods in the alignment and synchronization of chocolate conching process variables’ trajectories, aimed at properly determining the reference distribution for multivariate statistical process control. The power of classification of batches in two categories (conforming and non-conforming) was evaluated using the k-nearest neighbor (KNN) algorithm. Real data from a milk chocolate conching process was collected and the following variables were monitored over time: frequency of soybean lecithin dosage, rotation speed of the shovels, current of the main motor of the conche, and chocolate temperature. A set of 62 batches with durations between 495 and 1,170 minutes was considered; 53% of the batches were known to be conforming based on lab test results and experts’ evaluations. Results showed that all three DTW methods tested were able to align and synchronize the conching dataset. However, synchronized datasets obtained from these methods performed differently when inputted in the KNN classification algorithm. Kassidas, MacGregor and Taylor’s (named KMT) method was deemed the best DTW method for aligning and synchronizing a milk chocolate conching dataset, presenting 93.7% accuracy, 97.2% sensitivity and 90.3% specificity in batch classification, being considered the best option to determine the reference set for the milk chocolate dataset. Such method was recommended due to the lowest number of iterations required to achieve convergence and highest average accuracy in the testing portion using the KNN classification technique.

Keywords: batch process monitoring, chocolate conching, dynamic time warping, reference set distribution, variable duration

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Authors: Tawfig Falani, Abdulalaziz Saleh

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Air pollution has become a major environmental issue with definitive repercussions on human health. Global concerns have been raised about the health effects of deteriorating air quality due mainly to widespread industrialization and urbanization. To assess the quality of air in Brega, air quality indicators were calculated using the U.S. Environmental Protection Agency procedure. Air quality was monitored from 01/10/2019 to 28/02/2021 with a daily average measuring six pollutants of particulate matter <2.5µm (PM2.5), and <10µm (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and carbon monoxide (CO). The result indicated that air pollution at general air quality monitoring sites for sulphur dioxide, carbon monoxide, PM₁₀ and PM2.5 and nitrogen dioxide are always within the permissible limit. Referring to a monthly average of Pollutants in the Brega Industrial area, all months were out of AQG limit for NO₂, and the same with O₃ except for two months. For PM2.5 and PM₁₀ 7, 5 out of 17 months were out of limits, respectively. Relative AQI for ozone is found in the range of moderate category of general air pollution, and the worst month was Nov. 2020, which was marked as Very Unhealthy category, then the next two months (Dec. 2020 and Jan. 2021 ) were Unhealthy categories. It's the first time that we have used the AQI in SOC, and not usually used in Libya to identify the quality of air pollution. So, I think it will be useful if AQI is used as guidance for specified air pollution. That dictate putting monitoring stations beside any industrial activity that has emissions of the six major air pollutants.

Keywords: air quality, air pollutants, air quality index (AQI), particulate matter

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Authors: Ceren Kaya, Okan Erkaymaz, Orhan Ayar, Mahmut Özer

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Diabetes Mellitus (Diabetes) is a disease based on insulin hormone disorders and causes high blood glucose. Clinical findings determine that diabetes can be diagnosed by electrophysiological signals obtained from the vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases resulting on diabetes and it is the leading cause of vision loss due to structural alteration of the retinal layer vessels. In this study, features of horizontal and vertical Video-Oculography (VOG) signals have been used to classify non-proliferative and proliferative diabetic retinopathy disease. Twenty-five features are acquired by using discrete wavelet transform with VOG signals which are taken from 21 subjects. Two models, based on multi-layer perceptron and radial basis function, are recommended in the diagnosis of Diabetic Retinopathy. The proposed models also can detect level of the disease. We show comparative classification performance of the proposed models. Our results show that proposed the RBF model (100%) results in better classification performance than the MLP model (94%).

Keywords: diabetic retinopathy, discrete wavelet transform, multi-layer perceptron, radial basis function, video-oculography (VOG)

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Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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Authors: Nor Asrina Binti Ramlee

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Voltage sag, voltage swell, high-frequency noise and voltage transients are kinds of disturbances in power quality. They are also known as power quality events. Equipment used in the industry nowadays has become more sensitive to these events with the increasing complexity of equipment. This leads to the importance of distributing clean power quality to the consumer. To provide better service, the best analysis on power quality is very vital. Thus, this paper presents the events detection focusing on voltage sag and swell. The method is developed by applying time domain signal analysis using wavelet transform approach in MATLAB. Four types of mother wavelet namely Haar, Dmey, Daubechies, and Symlet are used to detect the events. This project analyzed real interrupted signal obtained from 22 kV transmission line in Skudai, Johor Bahru, Malaysia. The signals will be decomposed through the wavelet mothers. The best mother is the one that is capable to detect the time location of the event accurately.

Keywords: power quality, voltage sag, voltage swell, wavelet transform

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Authors: Khadidja Mebarki, Naceur Boussouar, Nabila Ihaddadene, M. Akermi

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Food products are particularly sensitive, since they concern the health of the consumer, whether it’s be from the health point of view or commercial, this kind of product must be subjected to rigorous controls, in order to prevent any fraud. Quality and safety are essential for food security, public health and economic development. The strengthening of food security is essential to increase food security which is considered reached when all individuals can at any time access safe and nutritious food they need to lead healthy and active lives. The objective of this project is to initiate a quality approach in the laboratories of the quality control and the repression of fraud. It will be directed towards the application of good laboratory practices, traceability, management of quality documents (quality, procedures and specification manual) and quality audits. And to prepare the ground for a possible accreditation by ISO 17025 standard of BECHAR laboratory’s. The project will take place in four main stages: 1- Preparation of an audit grid; 2- Realization of a quality audit according to the method of 5 M completed by a section on quality documentation; 3- Drafting of an audit report and proposal for recommendations; 4- Implementation of corrective actions on the ground. This last step consisted in the formalization of the cleaning disinfection plan; work on good hygiene practices, establishment of a mapping of processes and flow charts of the different processes of the laboratory, classifying quality documents and formalizing the process of document management. During the period of the study within the laboratory, all facets of the work were almost appreciated, as we participated in the expertise performed in within it.

Keywords: quality, management, ISO 17025 accreditation, GLP

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Authors: Nadia Fatima Zahra Malki

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The aim of the study is to show the role of total Quality Management on firm performance, research relied on the views of a sample managers working in the Marinel pharmaceutical company. The research aims to achieve many objectives, including increasing awareness of the concepts of Total Quality Management on Firm Performance, especially in the manufacturing firm, providing a future vision of the possibility of success, and the actual application of the Principles of Total Quality Management in the manufacturing company. The research adopted a default model was built after a review and analysis of the literature review in the context of one hypothesis's main points at the origin of a group of sub-hypotheses. The research presented a set of conclusions, and the most important of these conclusions was that there is a relationship between the Principles of TQM and Firm Performance.

Keywords: total quality management, competitive advantage, companies, objectives

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Authors: Yoshimoto Kurihara, Tad Gonsalves

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Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

Procedia PDF Downloads 155