Search results for: discriminant%20analysis

Authors: Rasmita Mishra, Chander Shekher

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Background: Population ageing is an inevitable outcome of demographic transition. Due to increased life expectancy, the old age population in India and worldwide has increased, and it will continue to grow more alarmingly in the near future. There are redundant austerity that has been bestowed upon the widows, thus, the life of widows is never been easy in India. The loss of spouse along with other disadvantaged socioeconomic intermediaries like illiteracy and poverty often make the life of widows more difficult to live. Methodology: Ethical statement: The study used secondary data available in the public domain for its wider use in social research. Thus, there was no requirement of ethical consent in the present study. Data source: Building a Knowledge Base on Population Aging in India (BKPAI), 2011 dataset is used to fulfill the objectives of this study. It was carried out in seven states – Himachal Pradesh, Kerala, Maharashtra, Odisha, Punjab, Tamil Nadu, and West Bengal – having a higher percentage of the population in the age group 60 years and above compared to the national average. Statistical analysis: Descriptive and inferential statistics were used to understand the level of elderly widows and incidence of abuse against them in India and selected states. Bivariate and Trivariate analysis were carried out to check the pattern of abuse by selected covariates. Chi-Square test is used to verify the significance of the association. Further, Discriminant Analysis (DA) is carried out to understand which factor can separate out group of neglect and non-neglect elderly. Result: With the addition of 27 million from 2001 to 2011, the total elderly population in India is more than 100 million. Elderly females aged 60+ were more widows than their counterpart elderly males. This pattern was observed across selected states and at national level. At national level, more than one tenth (12 percent) of elderly experienced abuse in their lifetime. Incidence of abuse against elderly widows within family was considerably higher than the outside the family. This pattern was observed across the selected place and abuse in the study. In discriminant analysis, the significant difference between neglected and non-neglected elderly on each of the independent variables was examined using group mean and ANOVA. Discussion: The study is the first of its kind to assess the incidence of abuse against elderly widows using large-scale survey data. Another novelty of this study is that it has assessed for those states in India whereby the proportion of elderly is higher than the national average. Place and perpetrators involved in the abuse against elderly widows certainly envisaged the safeness in the present living arrangement of elderly widows. Conclusion: Due to the increasing life expectancy it is expected that the number of elderly will increase much faster than before. As biologically women live longer than men, there will be more women elderly than men. With respect to the living arrangement, after the demise of the spouse, elderly widows are more likely to live with their children who emerged as the main perpetrator of abuse.

Keywords: elderly abuse, emotional abuse physical abuse, material abuse, psychological abuse, quality of life

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Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

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In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

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Authors: Akbar Gharbali, Ali Abbasian Ardekani, Afshin Mohammadi

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Introduction: Thyroid nodules have an incidence of 33-68% in the general population. More than 5-15% of these nodules are malignant. Early detection and treatment of thyroid nodules increase the cure rate and provide optimal treatment. Between the medical imaging methods, Ultrasound is the chosen imaging technique for assessment of thyroid nodules. The confirming of the diagnosis usually demands repeated fine-needle aspiration biopsy (FNAB). So, current management has morbidity and non-zero mortality. Objective: To explore diagnostic potential of automatic texture analysis (TA) methods in differentiation benign and malignant thyroid nodules by ultrasound imaging in order to help for reliable diagnosis and monitoring of the thyroid nodules in their early stages with no need biopsy. Material and Methods: The thyroid US image database consists of 70 patients (26 benign and 44 malignant) which were reported by Radiologist and proven by the biopsy. Two slices per patient were loaded in Mazda Software version 4.6 for automatic texture analysis. Regions of interests (ROIs) were defined within the abnormal part of the thyroid nodules ultrasound images. Gray levels within an ROI normalized according to three normalization schemes: N1: default or original gray levels, N2: +/- 3 Sigma or dynamic intensity limited to µ+/- 3σ, and N3: present intensity limited to 1% - 99%. Up to 270 multiscale texture features parameters per ROIs per each normalization schemes were computed from well-known statistical methods employed in Mazda software. From the statistical point of view, all calculated texture features parameters are not useful for texture analysis. So, the features based on maximum Fisher coefficient and the minimum probability of classification error and average correlation coefficients (POE+ACC) eliminated to 10 best and most effective features per normalization schemes. We analyze this feature under two standardization states (standard (S) and non-standard (NS)) with Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA). The 1NN classifier was performed to distinguish between benign and malignant tumors. The confusion matrix and Receiver operating characteristic (ROC) curve analysis were used for the formulation of more reliable criteria of the performance of employed texture analysis methods. Results: The results demonstrated the influence of the normalization schemes and reduction methods on the effectiveness of the obtained features as a descriptor on discrimination power and classification results. The selected subset features under 1%-99% normalization, POE+ACC reduction and NDA texture analysis yielded a high discrimination performance with the area under the ROC curve (Az) of 0.9722, in distinguishing Benign from Malignant Thyroid Nodules which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Conclusions: Our results indicate computer-aided diagnosis is a reliable method, and can provide useful information to help radiologists in the detection and classification of benign and malignant thyroid nodules.

Keywords: ultrasound imaging, thyroid nodules, computer aided diagnosis, texture analysis, PCA, LDA, NDA

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Authors: Ng'etich K. Stephen

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Abstract—Globally analytics in basketball has advanced tremendously in the last decade. Organizations are leveraging the insights to improve team and player performance and, in the long run, generate revenue out of it. Due to limited basketball game-related statistics in African competitions, teams are unaware of how they compete with other continental basketball teams. The purpose of this study is to evaluate the regional difference in basketball game-related statistics between African teams that played in the newly formed league, the basketball African league and the European league. The basketball African league, a competition created through the partnership between NBA and FIBA, offers a good starting point since it has valuable basketball metrics to analyze. This study sought to use multivariate linear discriminant analysis to identify the game-related statistics that discriminate the teams in Euro league and the basketball African league.

Keywords: basketball africa league, basketball, euroleague, fiba, africa

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Authors: Artemis Louppis, Michalis Constantinou, Ioanna Kosma, Federica Blando, Michael Kontominas, Anastasia Badeka

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The aim of the present study was to differentiate yellow prickly pear according to geographical origin based on the combination of mineral content, physicochemical parameters, vitamins and antioxidants. A total of 240 yellow prickly pear samples from Cyprus, Spain, Italy and Greece were analyzed for pH, titratable acidity, electrical conductivity, protein, moisture, ash, fat, antioxidant activity, individual antioxidants, sugars and vitamins by UPLC-MS/MS as well as minerals by ICP-MS. Statistical treatment of the data included multivariate analysis of variance followed by linear discriminant analysis. Based on results, a correct classification of 66.7% was achieved using the cross validation by mineral content while 86.1% was achieved using the cross validation method by combination of all analytical parameters.

Keywords: geographical differentiation, prickly pear, chemometrics, analytical techniques

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Authors: Young-Kwang Lee, Eun-Gu Ji, Min-Joo Kim, Seung-Jae Oh

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The purpose of this study is to develop a social participation activity scale for the elderly. As a result of exploratory factor analysis, confirmatory factor analysis was conducted using maximum likelihood method using bundled items. In conclusion, thirteen items of social participation activity scale seemed appropriate. Finally, convergent validity and discriminant validity were verified on the scale with the fit. The convergent validity was based on the variance extracted value. In other words, the hypothesis that the variables are the same is rejected and the validity is confirmed. This study extensively considered the measurement items of the social participation activity scale used to measure social participation activities of the elderly. In the future, it will be meaningful that it can be used as a tool to verify the effectiveness of services in organizations that provide social welfare services to elderly people such as comprehensive social welfare centers and the elderly comprehensive social welfare centers.

Keywords: elderly, social participation, scale development, validity

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Authors: Mercedes Bertotto, Marcelo Bello, Hector Goicoechea, Veronica Fusca

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The National Service of Agri-Food Health and Quality (SENASA), controls honey to detect contamination by synthetic or natural chemical substances and establishes and controls the traceability of the product. The utility of near-infrared spectroscopy for the detection of adulteration of honey with high fructose corn syrup (HFCS) was investigated. First of all, a mixture of different authentic artisanal Argentinian honey was prepared to cover as much heterogeneity as possible. Then, mixtures were prepared by adding different concentrations of high fructose corn syrup (HFCS) to samples of the honey pool. 237 samples were used, 108 of them were authentic honey and 129 samples corresponded to honey adulterated with HFCS between 1 and 10%. They were stored unrefrigerated from time of production until scanning and were not filtered after receipt in the laboratory. Immediately prior to spectral collection, honey was incubated at 40°C overnight to dissolve any crystalline material, manually stirred to achieve homogeneity and adjusted to a standard solids content (70° Brix) with distilled water. Adulterant solutions were also adjusted to 70° Brix. Samples were measured by NIR spectroscopy in the range of 650 to 7000 cm⁻¹. The technique of specular reflectance was used, with a lens aperture range of 150 mm. Pretreatment of the spectra was performed by Standard Normal Variate (SNV). The ant colony optimization genetic algorithm sample selection (ACOGASS) graphical interface was used, using MATLAB version 5.3, to select the variables with the greatest discriminating power. The data set was divided into a validation set and a calibration set, using the Kennard-Stone (KS) algorithm. A combined method of Potential Functions (PF) was chosen together with Partial Least Square Linear Discriminant Analysis (PLS-DA). Different estimators of the predictive capacity of the model were compared, which were obtained using a decreasing number of groups, which implies more demanding validation conditions. The optimal number of latent variables was selected as the number associated with the minimum error and the smallest number of unassigned samples. Once the optimal number of latent variables was defined, we proceeded to apply the model to the training samples. With the calibrated model for the training samples, we proceeded to study the validation samples. The calibrated model that combines the potential function methods and PLSDA can be considered reliable and stable since its performance in future samples is expected to be comparable to that achieved for the training samples. By use of Potential Functions (PF) and Partial Least Square Linear Discriminant Analysis (PLS-DA) classification, authentic honey and honey adulterated with HFCS could be identified with a correct classification rate of 97.9%. The results showed that NIR in combination with the PT and PLS-DS methods can be a simple, fast and low-cost technique for the detection of HFCS in honey with high sensitivity and power of discrimination.

Keywords: adulteration, multivariate analysis, potential functions, regression

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Authors: Madhusmita Dehingia, Supriyo Sen, Bhuwan Bhaskar, Tulsi Joishy, Mojibur R. Khan

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Human gut microbes and their metabolites are important for maintaining homeostasis in the gut and are responsible for many metabolic and immune mediated diseases. In the present study, we determined the profiles of the gut metabolites of five different ethnic groups (Bodo, Tai-Phake, Karbi, Tea tribe and Tai-Aiton) of Assam. Fecal metabolite profiling of the 39 individuals belonging to the ethnic groups was carried out using Gas chromatography – Mass spectrometry (GC-MS), and comparison was performed among the tribes for common and unique metabolites produced within their gut. Partial Least Squares Discriminant Analysis (PLS-DA) of the metabolites suggested that the individuals grouped according to their ethnicity. Among the 66 abundant metabolites, 12 metabolites were found to be common among the five ethnic groups. Additionally, ethnicity wise some unique metabolites were also detected. For example, the tea tribe of Assam contained the tea components, Aniline and Benzoate more in their gut in comparison to others. Metabolites of microbial origin were also correlated with the already published metagenomic data of the same ethnic group and functional analysis were carried out based on human metabolome database.

Keywords: ethnicity, gut microbiota, GC-MS, metabolites

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Authors: Angela Oktavia Suryani, Bernadeth Gloria, Edwin Sutamto, Jessica Kristianty, Ni Made Rai Sapitri, Patricia Catherine Agla, Sitti Arlinda Rochiadi

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It was found that some blogs or personal websites in Indonesia sell standardized intelligence tests (for example, Progressive Matrices (PM), Intelligence Structure Test (IST), and Culture Fair Intelligence Test (CFIT)) and other psychological tests, together with the manual and the key answers for public. Individuals can buy and prepare themselves for selection or recruitment with the real test. This action drives people to lie to the institution (education or company) and also to themselves. It was also found that those tests are old. Some items are not relevant with the current context, for example a question about a diameter of a certain coin that does not exist anymore. These problems motivate us to develop a new intelligence battery test, namely of Multiple Aptitude Battery (MAB). The battery test was built by using Thurstone’s Primary Mental Abilities theory and intended to be used by high schools students, university students, and worker applicants. The battery tests consist of 9 subtests. In the current study we examine six subtests, namely Reading Comprehension, Verbal Analogies, Numerical Inductive Reasoning, Numerical Deductive Reasoning, Mechanical Ability, and Two Dimensional Spatial Reasoning for university students. The study included 1424 data from students recruited by convenience sampling from eight faculties at Atma Jaya Catholic University of Indonesia. Classical and modern test approaches (Item Response Theory) were carried out to identify the item difficulties of the items and confirmatory factor analysis was applied to examine their internal validities. The validity of each subtest was inspected by using convergent–discriminant method, whereas the reliability was examined by implementing Kuder–Richardson formula. The result showed that the majority of the subtests were difficult in medium level, and there was only one subtest categorized as easy, namely Verbal Analogies. The items were found homogenous and valid measuring their constructs; however at the level of subtests, the construct validity examined by convergent-discriminant method indicated that the subtests were not unidimensional. It means they were not only measuring their own constructs but also other construct. Three of the subtests were able to predict academic performance with small effect size, namely Reading Comprehension, Numerical Inductive Reasoning, and Two Dimensional Spatial Reasoning. GPAs in intermediate level (GPAs at third semester and above) were considered as a factor for predictive invalidity. The Kuder-Richardson formula showed that the reliability coefficients for both numerical reasoning subtests and spatial reasoning were superior, in the range 0.84 – 0.87, whereas the reliability coefficient for the other three subtests were relatively below standard for ability test, in the range of 0.65 – 0.71. It can be concluded that some of the subtests are ready to be used, whereas some others are still need some revisions. This study also demonstrated that the convergent-discrimination method is useful to identify the general intelligence of human.

Keywords: intelligence, psychometric examination, multiple aptitude battery, university students

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Authors: Ntandazo Dlatu, Benjamin Longo-Mbenza, Andre Renzaho, Ruffin Appalata, Yolande Yvonne Valeria Matoumona Mavoungou, Mbenza Ben Longo, Kenneth Ekoru, Blaise Makoso, Gedeon Longo Longo

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Background: The gap between complexities related to the integration of Tuberculosis /HIV control and evidence-based knowledge motivated the initiation of the study. Therefore, the objective of this study was to explore correlations between national TB management guidelines, multiple deprivation indexes, quantiles, components and levels of Tuberculosis control programme using mathematical modeling in rural O.R. Tambo District Health Facilities, South Africa. Methods: The study design used mixed secondary data analysis and cross-sectional analysis between 2009 and 2013 across O.R Tambo District, Eastern Cape, South Africa using univariate/ bivariate analysis, linear multiple regression models, and multivariate discriminant analysis. Health inequalities indicators and component of an impediment to the tuberculosis control programme were evaluated. Results: In total, 62 400 records for TB notification were analyzed for the period 2009-2013. There was a significant but negative between Financial Year Expenditure (r= -0.894; P= 0.041) Seropositive HIV status(r= -0.979; P= 0.004), Population Density (r = -0.881; P= 0.048) and the number of TB defaulter in all TB cases. It was shown unsuccessful control of TB management program through correlations between numbers of new PTB smear positive, TB defaulter new smear-positive, TB failure all TB, Pulmonary Tuberculosis case finding index and deprivation-concentration-dispersion index. It was shown successful TB program control through significant and negative associations between declining numbers of death in co-infection of HIV and TB, TB deaths all TB and SMIAD gradient/ deprivation-concentration-dispersion index. The multivariate linear model was summarized by unadjusted r of 96%, adjusted R2 of 95 %, Standard Error of estimate of 0.110, R2 changed of 0.959 and significance for variance change for P=0.004 to explain the prediction of TB defaulter in all TB with equation y= 8.558-0.979 x number of HIV seropositive. After adjusting for confounding factors (PTB case finding the index, TB defaulter new smear-positive, TB death in all TB, TB defaulter all TB, and TB failure in all TB). The HIV and TB death, as well as new PTB smear positive, were identified as the most important, significant, and independent indicator to discriminate most deprived deprivation index far from other deprivation quintiles 2-5 using discriminant analysis. Conclusion: Elimination of poverty such as overcrowding, lack of sanitation and environment of highest burden of HIV might end the TB threat in O.R Tambo District, Eastern Cape, South Africa. Furthermore, ongoing adequate budget comprehensive, holistic and collaborative initiative towards Sustainable Developmental Goals (SDGs) is necessary for complete elimination of TB in poor O.R Tambo District.

Keywords: tuberculosis, HIV/AIDS, success, failure, control program, health inequalities, South Africa

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Authors: Reskino Resky

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This research purposes to create a model to detecting financial statement fraud. This research examines the variable of fraud triangle and auditor industry specialization with financial statement fraud. This research used sample of company which is listed in Indonesian Stock Exchange that have sanctions and cases by Financial Services Authority in 2011-2013. The number of company that were became in this research were 30 fraud company and 30 non-fraud company. The method of determining the sample is by using purposive sampling method with judgement sampling, while the data processing methods used by researcher are mann-whitney u and discriminants analysis. This research have two from five variable that can be process with discriminant analysis. The result shows the financial targets can be detect financial statement fraud, while financial stability can’t be detect financial statement fraud.

Keywords: fraud triangle analysis, financial targets, financial stability, auditor industry specialization, financial statement fraud

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Authors: Yasmine Abu Adla, Racha Soubra, Milana Kasab, Mohamad O. Diab, Aly Chkeir

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Over the past several years, researchers have shown a great interest in assessing the mobility of elderly people to measure their functional status. Usually, such an assessment is done by conducting tests that require the subject to walk a certain distance, turn around, and finally sit back down. Consequently, this study aims to provide an at home monitoring system to assess the patient’s status continuously. Thus, we proposed a technique to automatically detect when a subject sits down while walking at home. In this study, we utilized a Doppler radar system to capture the motion of the subjects. More than 20 features were extracted from the radar signals, out of which 11 were chosen based on their intraclass correlation coefficient (ICC > 0.75). Accordingly, the sequential floating forward selection wrapper was applied to further narrow down the final feature vector. Finally, 5 features were introduced to the linear discriminant analysis classifier, and an accuracy of 93.75% was achieved as well as a precision and recall of 95% and 90%, respectively.

Keywords: Doppler radar system, stand-to-sit phase, TUG test, machine learning, classification

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Authors: Ghada S. Moussa

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Vehicle classification is an important task in traffic surveillance and intelligent transportation systems. Classification of vehicle images is facing several problems such as: high intra-class vehicle variations, occlusion, shadow, illumination. These problems and others must be considered to develop a reliable vehicle classification system. In this study, a reliable multi-type vehicle classification system based on Bag-of-Words (BoW) paradigm is developed. Our proposed system used and compared four well-known classifiers; Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Decision Tree to classify vehicles into four categories: motorcycles, small, medium and large. Experiments on a large dataset show that our approach is efficient and reliable in classifying vehicles with accuracy of 95.7%. The SVM outperforms other classification algorithms in terms of both accuracy and robustness alongside considerable reduction in execution time. The innovativeness of developed system is it can serve as a framework for many vehicle classification systems.

Keywords: vehicle classification, bag-of-words technique, SVM classifier, LDA classifier, KNN classifier, decision tree classifier, SIFT algorithm

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Authors: Hyun-Woo Cho

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Continuous monitoring of industrial plants is one of necessary tasks when it comes to ensuring high-quality final products. In terms of monitoring and diagnosis, it is quite critical and important to detect some incipient abnormal events of manufacturing processes in order to improve safety and reliability of operations involved and to reduce related losses. In this work a new multivariate statistical online diagnostic method is presented using a case study. For building some reference models an empirical discriminant model is constructed based on various past operation runs. When a fault is detected on-line, an on-line diagnostic module is initiated. Finally, the status of the current operating conditions is compared with the reference model to make a diagnostic decision. The performance of the presented framework is evaluated using a dataset from complex industrial processes. It has been shown that the proposed diagnostic method outperforms other techniques especially in terms of incipient detection of any faults occurred.

Keywords: data mining, empirical model, on-line diagnostics, process fault, process monitoring

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Authors: Navid Ebrahmi Madiseh, Sina Esfandiarpour-Broujeni, Rahil Razeghi

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Introduction: Quantitative analysis of game-related statistical parameters is widely used to evaluate basketball performance at both individual and team levels. Non-free throw shooting plays a crucial role as the primary scoring method, holding significant importance in the game's technical aspect. It has been explored the predictive value of game-related statistics in relation to various contextual and situational variables. Many similarities and differences also have been found between different age groups and levels of competition. For instance, in the World Basketball Championships after the 2010 rule change, 2-point field goals distinguished winners from losers in women's games but not in men's games, and the impact of successful 3-point field goals on women's games was minimal. The study aimed to identify and compare discriminant shooting-related statistics between winning and losing teams in men’s and women’s FIBA-U19-Basketball-World-Cup-2023 tournaments. Method: Data from 112 observations (2 per game) of 16 teams (for each gender) in the FIBA-U19-Basketball-World-Cup-2023 were selected as samples. The data were obtained from the official FIBA website using Python. Specific information was extracted, organized into a DataFrame, and consisted of twelve variables, including shooting percentages, attempts, and scoring ratio for 3-pointers, mid-range shots, paint shots, and free throws. Made% = scoring type successful attempts/scoring type total attempts¬ (1)Free-throw-pts% (free throw score ratio) = (free throw score/total score) ×100 (2)Mid-pts% (mid-range score ratio) = (mid-range score/total score) ×100 (3) Paint-pts% (paint score ratio) = (Paint score/total score) ×100 (4) 3p_pts% (three-point score ratio) = (three-point score/total score) ×100 (5) Independent t-tests were used to examine significant differences in shooting-related statistical parameters between winning and losing teams for both genders. Statistical significance was p < 0.05. All statistical analyses were completed with SPSS, Version 18. Results: The results showed that 3p-made%, mid-pts%, paint-made%, paint-pts%, mid-attempts, and paint-attempts were significantly different between winners and losers in men (t=-3.465, P<0.05; t=3.681, P<0.05; t=-5.884, P<0.05; t=-3.007, P<0.05; t=2.549, p<0.05; t=-3.921, P<0.05). For women, significant differences between winners and losers were found for 3p-made%, 3p-pts%, paint-made%, and paint-attempt (t=-6.429, P<0.05; t=-1.993, P<0.05; t=-1.993, P<0.05; t=-4.115, P<0.05; t=02.451, P<0.05). Discussion: The research aimed to compare shooting-related statistics between winners and losers in men's and women's teams at the FIBA-U19-Basketball-World-Cup-2023. Results indicated that men's winners excelled in 3p-made%, paint-made%, paint-pts%, paint-attempts, and mid-attempt, consistent with previous studies. This study found that losers in men’s teams had higher mid-pts% than winners, which was inconsistent with previous findings. It has been indicated that winners tend to prioritize statistically efficient shots while forcing the opponent to take mid-range shots. In women's games, significant differences in 3p-made%, 3p-pts%, paint-made%, and paint-attempts were observed, indicating that winners relied on riskier outside scoring strategies. Overall, winners exhibited higher accuracy in paint and 3P shooting than losers, but they also relied more on outside offensive strategies. Additionally, winners acquired a higher ratio of their points from 3P shots, which demonstrates their confidence in their skills and willingness to take risks at this competitive level.

Keywords: gender, losers, shoot-statistic, U19, winners

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Authors: Rani Abro, Ali Ata Moazzami, Jan Erik Lindberg, Torbjörn Lundh

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The effect of dietary starch level on liver metabolism in Arctic charr (Salvelinus alpinus) and tilapia (Oreochromis mossambicus) was studied using 1H-NMR based metabolomics. Fingerlings were fed iso-nitrogenous diets containing 0, 10 and 20 % starch for two months before liver samples were collected for metabolite analysis. Metabolite profiling was performed using 600 MHz NMR Chenomx software. In total, 48 metabolites were profiled in liver extracts from both fish species. Following the profiling, principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLC-DA) were performed. These revealed that differences in the concentration of significant metabolites were correlated to the dietary starch level in both species. The most prominent difference in metabolic response to starch feeding between the omnivorous tilapia and the carnivorous Arctic charr was an indication of higher anaerobic metabolism in Arctic charr. The data also indicated that amino acid and pyrimidine metabolism was higher in Artic charr than in tilapia.

Keywords: arctic charr, metabolomics, starch, tilapia

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Authors: A. Deswal, N. S. Deora, H. N. Mishra

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The aim of the present study was to develop a rapid method for electronic nose for online quality control of oat milk. Analysis by electronic nose and bacteriological measurements were performed to analyse spoilage kinetics of oat milk samples stored at room temperature and refrigerated conditions for up to 15 days. Principal component analysis (PCA), discriminant factorial analysis (DFA) and soft independent modelling by class analogy (SIMCA) classification techniques were used to differentiate the samples of oat milk at different days. The total plate count (bacteriological method) was selected as the reference method to consistently train the electronic nose system. The e-nose was able to differentiate between the oat milk samples of varying microbial load. The results obtained by the bacteria total viable counts showed that the shelf-life of oat milk stored at room temperature and refrigerated conditions were 20 hours and 13 days, respectively. The models built classified oat milk samples based on the total microbial population into “unspoiled” and “spoiled”.

Keywords: electronic-nose, bacteriological, shelf-life, classification

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Authors: Jamshaid ul Rahman, Akhter Ali, Adnan Manzoor

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Learning-based systems have received increasing interest in recent years; recognition structures, including end-to-end speak recognition, are one of the hot topics in this area. A famous work on end-to-end speaker verification by using Angular Softmax Loss gained significant importance and is considered useful to directly trains a discriminative model instead of the traditional adopted i-vector approach. The margin-based strategy in angular softmax is beneficial to learn discriminative speaker embeddings where the random selection of margin values is a big issue in additive angular margin and multiplicative angular margin. As a better solution in this matter, we present an alternative approach by introducing a bit similar form of an additive parameter that was originally introduced for face recognition, and it has a capacity to adjust automatically with the corresponding margin values and is applicable to learn more discriminative features than the Softmax. Experiments are conducted on the part of Fisher dataset, where it observed that the additive parameter with angular softmax to train the front-end and probabilistic linear discriminant analysis (PLDA) in the back-end boosts the performance of the structure.

Keywords: additive parameter, angular softmax, speaker verification, PLDA

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Authors: Ahmed Kamil Hasan Al-Ali, Bouchra Senadji, Ganesh Naik

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We propose a system to real environmental noise and channel mismatch for forensic speaker verification systems. This method is based on suppressing various types of real environmental noise by using independent component analysis (ICA) algorithm. The enhanced speech signal is applied to mel frequency cepstral coefficients (MFCC) or MFCC feature warping to extract the essential characteristics of the speech signal. Channel effects are reduced using an intermediate vector (i-vector) and probabilistic linear discriminant analysis (PLDA) approach for classification. The proposed algorithm is evaluated by using an Australian forensic voice comparison database, combined with car, street and home noises from QUT-NOISE at a signal to noise ratio (SNR) ranging from -10 dB to 10 dB. Experimental results indicate that the MFCC feature warping-ICA achieves a reduction in equal error rate about (48.22%, 44.66%, and 50.07%) over using MFCC feature warping when the test speech signals are corrupted with random sessions of street, car, and home noises at -10 dB SNR.

Keywords: noisy forensic speaker verification, ICA algorithm, MFCC, MFCC feature warping

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Authors: Jadisha Cornejo, Helio Pedrini

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Recognition of emotions based on facial expressions has received increasing attention from the scientific community over the last years. Several fields of applications can benefit from facial emotion recognition, such as behavior prediction, interpersonal relations, human-computer interactions, recommendation systems. In this work, we develop and analyze an emotion recognition framework based on facial expressions robust to occlusions through the Weber Local Descriptor (WLD). Initially, the occluded facial expressions are reconstructed following an extension approach of Robust Principal Component Analysis (RPCA). Then, WLD features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Experimental results on three public datasets demonstrated that the WLD representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other approaches available in the literature.

Keywords: emotion recognition, facial expression, occlusion, fiducial landmarks

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Authors: Jorn Meyer, Stefan Sturmer

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The assessment of attachment patterns in toddlers and adults has been well researched, and valid diagnostic methods (e.g., Strange Situation Test, Adult Attachment Interview) are applicable. For middle and late childhood, on the other hand, there are only few validated methods available so far. For the Child Attachment Interview (CAI) promising validation studies from English-speaking countries are available, but so far a comprehensive study on the validity of a German sample is lacking. Within the scope of a longitudinal project, the results of the first point of measurement are reported in this study. A German-language version of the CAI was carried out with 111 primary school children (56% female; age: M = 8.34, SD = 0.49). In relation to psychometric quality criteria, parameters on interrater reliability, construct validity and discriminant, and convergent validity are reported. Analyses of the correlations between attachment patterns and internalizing and externalizing behavior problems from parent and teacher reports are presented. The implications for the German-language assessment of attachment in middle and late childhood in research and individual case diagnostics, e.g., in the context of conducting expert evaluation reports for family courts, are discussed.

Keywords: attachment, attachment assessment, developmental psychology, longitudinal study

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Authors: Fadwa Alhalaiqa, Othman Alfuqaha, Anas H. Khalifeh, Mahmoud Alsaraireh, Rami Masa’Deh, Natija S Manaa

Abstract:

Background: Post-traumatic stress disorders (PTSD) usually occur after traumatic occurrences that exceed the range of common human experience. This study aimed to test the psychometric properties of PCL-5 checklist for the 20 PTSD symptoms from DSM-5 among Lebanese population and to identify the prevalence of PTSD. Methods: A cross sectional survey of PCL5 among 950 Lebanese using the online survey platform by Google form was conducted. Snowball recruitment was used to identify participants for the survey. STROBE guideline was used in reporting the current study. Results: Face content, construct, discriminant, and convergent validity had been accomplished of PCL-5. The reliability by Cronbach alpha, composite, and average variance extracted were set superior. We found also that more than half of the participants (55.6%) scored 33 or above, which is the cutoff score for a likely diagnosis of PTSD. Conclusion: The current study provides further support for the Arabic version PCL-5 validity and reliability among non-Western populations. This support using this tool in the screening of PTSD.

Keywords: post traumatic stress disorder, psychometric properties, stress, adult population

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Authors: Leonardo C. Pacheco-Londoño, Nataly J. Galan-Freyle, Lisandro Pacheco-Lugo, Antonio Acosta, Elkin Navarro, Gustavo Aroca-Martínez, Karin Rondón-Payares, Samuel P. Hernández-Rivera

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In our Life Science Research Center of the University Simon Bolivar (LSRC), one of the focuses is the diagnosis and prognosis of different diseases; we have been implementing the use of gold nanoparticles (Au-NPs) for various biomedical applications. In this case, Au-NPs were used for Surface-Enhanced Raman Spectroscopy (SERS) in different diseases' diagnostics, such as Lupus Nephritis (LN), hypertension (H), preeclampsia (PC), and others. This methodology is proposed for the diagnosis of each disease. First, good signals of the different metabolites by SERS were obtained through a mixture of urine samples and Au-NPs. Second, PLS-DA models based on SERS spectra to discriminate each disease were able to differentiate between sick and healthy patients with different diseases. Finally, the sensibility and specificity for the different models were determined in the order of 0.9. On the other hand, a second methodology was developed using machine learning models from all data of the different diseases, and, as a result, a discriminant spectral map of the diseases was generated. These studies were possible thanks to joint research between two university research centers and two health sector entities, and the patient samples were treated with ethical rigor and their consent.

Keywords: SERS, Raman, PLS-DA, diseases

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Authors: Yanick Blaise Ketchaya, Taofa Zhou

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The type of primary source of gold deposit can be explored by using the study of trace element analysis of placer gold which is a valuable exploration tool. Au-bearing deposits are investigated through the placer gold, which is an important indicator mineral. The hydrothermal fluid interacting with diverse geological settings exerts an important function on the chemical composition of gold. Consequently, alluvial gold particles from the placer deposits within the Gamba district in northern Cameroon were examined by an electron probe microanalyzer (EPMA) to show discriminant chemical signatures. The gold grains from a different locality show the same trace element composition, which appears to be in a solid solution in Au. These trace element compositions, contained in gold grains, indicate a homogeneous source. The placer gold particles have significant chemical characteristics (low Ag content), consistent with a mesothermal source. The gold particle signatures in the Gamba district, with high Te and Bi contents, reflect the chemical characteristics of the felsic host rock superimposed on the chemical signature of the hydrothermal fluid.

Keywords: hypogene source, Northern Cameroon, placer gold, trace element

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Authors: Hatem A. El-Mezayen, Ahmed Abdelmajeed, Fatehya Metwally, Usama Elsaly, Salwa Atef

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Tumor metastasis involves the dissemination of malignant cells into the basement membrane and vascular system contributes to the circulating pool of these markers. In this context our aim has been focused on development of a non-invasive. Circulating tumor cells (CTCs) represent a unique liquid biopsy carrying comprehensive biological information of the primary tumor. Herein, we sought to develop a novel score based on the combination of the most significant CTCs biomarkers with and routine laboratory tests for accurate detection of metastatic breast cancer. Methods: Cytokeratin 18 (CK18), Cytokeratin 19 (CK19), and CA15.3 were assayed in metastatic breast cancer (MBC) patients (75), non-MBC patients (50) and healthy control (20). Results: Areas under receiving operating curve (AUCs) were calculated and used for construction on novel score. A novel score named MBC-CTCs = CA15.3 (U/L) × 0.08 + CK 18 % × 2.9 + CK19 × 3.1– 510. That function correctly classified 87% of metastatic breast cancer at cut-off value = 0.55. (i.e great than 0.55 indicates patients with metastatic breast cancer and less than 0.55 indicates patients with non-metastatic breast cancer). Conclusion: MBC-CTCs is a novel, non-invasive and simple can applied to discriminate patients with metastatic breast cancer.

Keywords: metastatic breast cancer, circulating tumor cells, cytokeratin, EpiCam

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Authors: Lavanya Srinivasan

Abstract:

Biometric identification is to identify unique features in a person like fingerprints, iris, ear, and voice recognition that need the subject's permission and physical contact. Gait biometric is used to identify the unique gait of the person by extracting moving features. The main advantage of gait biometric to identify the gait of a person at a distance, without any physical contact. In this work, the gait biometric is used for person re-identification. The person walking naturally compared with the same person walking with bag, coat, and case recorded using longwave infrared, short wave infrared, medium wave infrared, and visible cameras. The videos are recorded in rural and in urban environments. The pre-processing technique includes human identified using YOLO, background subtraction, silhouettes extraction, and synthesis Gait Entropy Image by averaging the silhouettes. The moving features are extracted from the Gait Entropy Energy Image. The extracted features are dimensionality reduced by the principal component analysis and recognised using different classifiers. The comparative results with the different classifier show that linear discriminant analysis outperforms other classifiers with 95.8% for visible in the rural dataset and 94.8% for longwave infrared in the urban dataset.

Keywords: biometric, gait, silhouettes, YOLO

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Authors: M. Bodner, M. Scampicchio

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Fourier Transform Infrared (FT-IR) spectroscopy coupled with chemometrics was used to distinguish between butter samples and non-butter samples. Further, quantification of the content of margarine in adulterated butter samples was investigated. Fingerprinting region (1400-800 cm^–1) was used to develop unsupervised pattern recognition (Principal Component Analysis, PCA), supervised modeling (Soft Independent Modelling by Class Analogy, SIMCA), classification (Partial Least Squares Discriminant Analysis, PLS-DA) and regression (Partial Least Squares Regression, PLS-R) models. PCA of the fingerprinting region shows a clustering of the two sample types. All samples were classified in their rightful class by SIMCA approach; however, nine adulterated samples (between 1% and 30% w/w of margarine) were classified as belonging both at the butter class and at the non-butter one. In the two-class PLS-DA model’s (R² = 0.73, RMSEP, Root Mean Square Error of Prediction = 0.26% w/w) sensitivity was 71.4% and Positive Predictive Value (PPV) 100%. Its threshold was calculated at 7% w/w of margarine in adulterated butter samples. Finally, PLS-R model (R² = 0.84, RMSEP = 16.54%) was developed. PLS-DA was a suitable classification tool and PLS-R a proper quantification approach. Results demonstrate that FT-IR spectroscopy combined with PLS-R can be used as a rapid, simple and safe method to identify pure butter samples from adulterated ones and to determine the grade of adulteration of margarine in butter samples.

Keywords: adulterated butter, margarine, PCA, PLS-DA, PLS-R, SIMCA

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Authors: I. Chebbi, L. Boudaya, L. Neifar

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The bogue, Boops boops is an economically important fishery resource and commonly captured in the Mediterranean, and its diversity in parasites has been used as a tool to differentiate between stocks along with Tunisia since it is widely acceptable in fisheries management. In this study, a total of 90 fish are investigated from three localities off Tunisia, including Kelibia, Mahdia, and Zarzis. Fifteen species of parasites totaling 1270 individuals were harvested from B. boops, whereas ten parasites were used as biological tags. Based on Mahalanobis distance, each parasite species shows a great importance in the discrimination between groups. Tetraphyllidea larvae are the most influential parasites in determining the position of samples belonging to Kelibia. Monogenean species and Hysterothylacium sp. are the most important species for determining the position of samples from Mahdia. Specimens from Zarzis are characterized by the absence of the four Monogenean species and the Tetraphyllidea larvae. Parasites allocate B. boops population correctly to their origin communities with an accuracy of 83.3%. These results were corroborated by the discriminant analyses, highlighted the presence of three stocks, and improved that the parasitological method can be considered as a reliable key to provide imperative information for discriminating among B. boops stocks in Tunisian waters.

Keywords: biological marker, Boops boops, parasite, population structure

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Authors: Jun Wang, Ge Zhang

Abstract:

Machine learning has been used in many areas today. It thrives at reviewing large volumes of data and identifying patterns and trends that might not be apparent to a human. Given the huge potential benefit and the amount of data available in the financial market, it is not surprising to see machine learning applied to various financial products. While future prices of financial securities are extremely difficult to forecast, we study them from a different angle. Instead of trying to forecast future prices, we apply machine learning algorithms to predict the direction of future price movement, in particular, whether a sector Exchange Traded Fund (ETF) would outperform or underperform the market in the next week or in the next month. We apply several machine learning algorithms for this prediction. The algorithms are Linear Discriminant Analysis (LDA), k-Nearest Neighbors (KNN), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Neural Networks (NN). We show that these machine learning algorithms, most notably GNB and NN, have some predictive power in forecasting out-performance and under-performance out of sample. We also try to explore whether it is possible to utilize the predictions from these algorithms to outperform the buy-and-hold strategy of the S&P 500 index. The trading strategy to explore out-performance predictions does not perform very well, but the trading strategy to explore under-performance predictions can earn higher returns than simply holding the S&P 500 index out of sample.

Keywords: machine learning, ETF prediction, dynamic trading, asset allocation

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Authors: Yun-Ru Tsai, Jo-Hui Lin

Abstract:

The cultural interactions between tourists and destination communities have received increased attention. Tourists play an important role in constructing a rewarding intercultural experience and cultural understanding. Cultural literacy is the ability for tourists to negotiate different cultures, this research aimed to develop a measurement of Tourist Cultural Literacy (TCL), the result provides a theoretical framework to assess how tourists interact with different cultural destinations. A pilot qualitative research was conducted in order to generate the initial items. In this study, the procedure of developing the TCL scale was divided into two parts. First, an exploratory factor analysis was conducted, a 25-item TCL scale was developed and six factors were identified: cultural sensitivity, appreciation of the culture, respect for the culture, knowledge of the culture, participate in the culture, and empathy for the culture. Second, confirmatory factor analyses and structural equation modeling were employed, the six-factor model was verified, and was proven to have good fit, reliability, convergent validity, discriminant validity, and criterion-related validity. The study provides managerial implications for tourist management and education, the popularization of TCL might increase the respect and understanding between tourists and local societies as well as decrease the cultural shocks and negative social-cultural impacts derived from tourism activities, thereby reducing the maintenance cost of management and allowing tourists to obtain a better cultural experience. Future research suggestions are also provided.

Keywords: cultural literacy, cultural tourism, scale development, tourism contact

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