Search results for: classification quality

Authors: Raghavi C. Janaswamy

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In the healthcare sector, heterogenous data elements such as patients, diagnosis, symptoms, conditions, observation text from physician notes, and prescriptions form the essentials of the Electronic Health Record (EHR). The data in the form of clear text and images are stored or processed in a relational format in most systems. However, the intrinsic structure restrictions and complex joins of relational databases limit the widespread utility. In this regard, the design and development of realistic mapping and deep connections as real-time objects offer unparallel advantages. Herein, a graph neural network-based classification of EHR data has been developed. The patient conditions have been predicted as a node classification task using a graph-based open source EHR data, Synthea Database, stored in Tigergraph. The Synthea DB dataset is leveraged due to its closer representation of the real-time data and being voluminous. The graph model is built from the EHR heterogeneous data using python modules, namely, pyTigerGraph to get nodes and edges from the Tigergraph database, PyTorch to tensorize the nodes and edges, PyTorch-Geometric (PyG) to train the Graph Neural Network (GNN) and adopt the self-supervised learning techniques with the AutoEncoders to generate the node embeddings and eventually perform the node classifications using the node embeddings. The model predicts patient conditions ranging from common to rare situations. The outcome is deemed to open up opportunities for data querying toward better predictions and accuracy.

Keywords: electronic health record, graph neural network, heterogeneous data, prediction

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Authors: Mohamed Mounir Tellache, Hiroyuki Kambara, Yasuharu Koike, Makoto Miyakoshi, Natsue Yoshimura

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This study explores the practicality of using electroencephalographic (EEG) independent components to predict eight-direction finger movements in pseudo-real-time. Six healthy participants with individual-head MRI images performed finger movements in eight directions with two different arm configurations. The analysis was performed in two stages. The first stage consisted of using independent component analysis (ICA) to separate the signals representing brain activity from non-brain activity signals and to obtain the unmixing matrix. The resulting independent components (ICs) were checked, and those reflecting brain-activity were selected. Finally, the time series of the selected ICs were used to predict eight finger-movement directions using Sparse Logistic Regression (SLR). The second stage consisted of using the previously obtained unmixing matrix, the selected ICs, and the model obtained by applying SLR to classify a different EEG dataset. This method was applied to two different settings, namely the single-participant level and the group-level. For the single-participant level, the EEG dataset used in the first stage and the EEG dataset used in the second stage originated from the same participant. For the group-level, the EEG datasets used in the first stage were constructed by temporally concatenating each combination without repetition of the EEG datasets of five participants out of six, whereas the EEG dataset used in the second stage originated from the remaining participants. The average test classification results across datasets (mean ± S.D.) were 38.62 ± 8.36% for the single-participant, which was significantly higher than the chance level (12.50 ± 0.01%), and 27.26 ± 4.39% for the group-level which was also significantly higher than the chance level (12.49% ± 0.01%). The classification accuracy within [–45°, 45°] of the true direction is 70.03 ± 8.14% for single-participant and 62.63 ± 6.07% for group-level which may be promising for some real-life applications. Clustering and contribution analyses further revealed the brain regions involved in finger movement and the temporal aspect of their contribution to the classification. These results showed the possibility of using the ICA-based method in combination with other methods to build a real-time system to control prostheses.

Keywords: brain-computer interface, electroencephalography, finger motion decoding, independent component analysis, pseudo real-time motion decoding

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Authors: L. S. Chathurika

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Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

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Authors: G. Hafner

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A method developed at the University of Stuttgart will be presented: ‘Analysis, Evaluation and Optimization of Food Management’. A major focus is represented by quantification of food losses and food waste as well as their classification and evaluation regarding a system optimization through waste prevention. For quantification and accounting of food, food losses and food waste along the food chain, a clear definition of core terms is required at the beginning. This includes their methodological classification and demarcation within sectors of the food value chain. The food chain is divided into agriculture, industry and crafts, trade and consumption (at home and out of home). For adjustment of core terms, the authors have cooperated with relevant stakeholders in Germany for achieving the goal of holistic and agreed definitions for the whole food chain. This includes modeling of sub systems within the food value chain, definition of terms, differentiation between food losses and food wastage as well as methodological approaches. ‘Food Losses’ and ‘Food Wastes’ are assigned to individual sectors of the food chain including a description of the respective methods. The method for analyzing, evaluation and optimization of food management systems consist of the following parts: Part I: Terms and Definitions. Part II: System Modeling. Part III: Procedure for Data Collection and Accounting Part. IV: Methodological Approaches for Classification and Evaluation of Results. Part V: Evaluation Parameters and Benchmarks. Part VI: Measures for Optimization. Part VII: Monitoring of Success The method will be demonstrated at the example of an invesigation of food losses and food wastage in the Federal State of Bavaria including an extrapolation of respective results to quantify food wastage in Germany.

Keywords: food losses, food waste, resource management, waste management, system analysis, waste minimization, resource efficiency

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Authors: M. Ghasemi-Varnamkhasti, S. H. Yoosefian. A. Mohammad- Razdari

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The effect of 1.0 kJ/m² Ultraviolet-C (UV-C) light on pH, weight loss, color, and firmness of button mushroom (Agaricus bisporus) tissues during 21-days storage at 4 ºC was studied. UV-C irradiation enhanced pH, weight, color parameters, and firmness of mushroom during storage compared to control treatment. However, application of 1.0 kJ/m² UV-C treatment could effectively induce the increase of weight loss, firmness, and pH to 14.53%, 49.82%, and 10.39%, respectively. These results suggest that the application of UV-C irradiation could be an effective method to maintain the postharvest quality of mushrooms.

Keywords: mushroom, polyethylene film, quality, UV-c irradiation

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Authors: Enes Furkan Erkan, Özer Uygun, Halil Ibrahim Demir, Zeynep Demir

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Companies require increase in quality perception level of their products due to competitive conditions. As a result, the tendency to quality and researches to develop the quality are increasing day by day. Cost and time constraints are the biggest problems that companies face in their quality improvement efforts. In this study, factors that affect the porosity of ceramic products are determined and analyzed in a factory producing ceramic tiles. Then, Taguchi method is used in the design phase in order to decrease the number of tests to be performed by means of orthogonal sequences. The most important factors affecting the porosity of ceramic tiles are determined using Taguchi and ANOVA analysis. Based on the analyses, the most affecting factors are determined to be used in the fuzzy implementation stage. Then, the fuzzy rules were established with the factors affecting porosity by the experts’ opinion. Thus, porosity result could be obtained not only for the specified factor levels but also for intermediate values. In this way, it has been provided convenience to the factory in terms of cost and quality improvement.

Keywords: fuzzy, porosity, Taguchi Method, Taguchi-Fuzzy

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Authors: Mohammed Riyas Pp

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This study aimed at investigating major issues in translating the Arabic Hadith terminologies into English, focusing on choosing the most appropriate translation for each, reviewing major Hadith works in English. This study is confined to twenty terminologies with regard to classification of Hadith based on authority, strength, number of transmitters and connections in Isnad. Almost all available translations are collected and analyzed to find the most proper translation based on linguistic and translational values. To the researcher, many translations lack precise understanding of either Hadith terminologies or English language and varieties of methodologies have influence on varieties of translations. This study provides a classification of translational and conceptual issues. Translational issues are related to translatability of these terminologies and their equivalence. Conceptual issues provide a list of misunderstandings due to wrong translations of terminologies. This study ends with a suggestion for unification in translating terminologies based on convention of Muslim scholars having good understanding of Hadith terminologies and English language.

Keywords: english language, hadith terminologies, equivalence in translation, problems in translation

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Authors: Oumaima Lahmar

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This paper aims to define a structured topography for finance researchers seeking to navigate the body of knowledge in their extrapolation of finance phenomena. To make sense of the body of knowledge in finance, a probabilistic topic modeling approach is applied on 6000 abstracts of academic articles published in three top journals in finance between 1976 and 2020. This approach combines both machine learning techniques and natural language processing to statistically identify the conjunctions between research articles and their shared topics described each by relevant keywords. The topic modeling analysis reveals 35 coherent topics that can well depict finance literature and provide a comprehensive structure for the ongoing research themes. Comparing the extracted topics to the Journal of Economic Literature (JEL) classification system, a significant similarity was highlighted between the characterizing keywords. On the other hand, we identify other topics that do not match the JEL classification despite being relevant in the finance literature.

Keywords: finance literature, textual analysis, topic modeling, perplexity

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Authors: Tasneem Alfalah, Salsabeel Alfalah, Jannat Alfalah

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The quality of higher education as a service is fundamental to a country’s development because universities prepare the professionals who will work as managers in companies and manage public and private resources and care for the health and education of new generations. Knowledge sharing involves the interaction of all activities between individuals. Thus, the higher education institutions are aiming to improve and assist their academics in generating new ideas by encouraging them to work as a team, to simplify the exchange of the new knowledge and to further improve the learning process and achieving institutional aims. Moreover, the sources of competitive advantage in universities derive from intellectual capital and innovations in which innovation comes through knowledge sharing. Using quality tools is to define the exact requirements needed to create the concept of knowledge sharing and what are the barriers to achieve this in universities. The purpose of this research is critically evaluating the role of using quality tools to facilitate the concept of knowledge sharing and improve the academic staff level in the Jordanian universities.

Keywords: higher education, knowledge sharing, quality, management tools

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Authors: Jamil Ahmed, Li P. Wong, Yan P. Chua

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The United Nation's sustainable development goals include the target to ensure access to water and sanitation for all; however, very few studies have assessed school-based drinking water in Pakistan. The purpose of this study was to characterize water quality in primary schools of Pakistan and to characterize how recent WASH interventions were associated with school water quality. We conducted a representative cross-sectional study of primary schools in the Sindh province of Pakistan. We used structured observations and structured interviews to ascertain the school’s WASH conditions. Our primary exposures of interest were the implementation of previous WASH interventions in the school and the water source type. Outcomes of interest included water quality (measured by various chemical and microbiological indicators) and water availability at the school’s primary drinking water source. We used log-binomial regression to characterize how WASH exposures were associated with water quality outcomes. We collected data from 256 schools. Groundwater was the primary drinking water source at most schools (87%). Water testing showed that 14% of the school’s water had arsenic above the WHO recommendations, and over 50% of the water samples exceeded recommendations for both lead and cadmium. A majority of the water sources (52%) had fecal coliform contamination. None of the schools had nitrate contamination (0%), and few had fluoride contamination (5%). Regression results indicated that having a recent WASH intervention at the school was not associated with either arsenic contamination (prevalence ratio=0.97; 95% CI: 0.46-2.1) or with fecal coliform contamination (PR=0.88; 95% CI: 0.67-1.17). Our assessment unveiled several water quality gaps that exist, including high heavy metal and fecal contamination. Our findings will help various stakeholders to take suitable action to improve water quality in Pakistani schools.

Keywords: WASH interventions, water quality, primary school children, heavy metals

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Authors: Morteza Raei Dehaghi, Seyed Mohammad Mirhashemi

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The current study explored the effect of economic development, financial development and institutional quality on environmental destruction in upper-middle income countries during the time period of 1999-2011. The dependent variable is logarithm of carbon dioxide emissions that can be considered as an index for destruction or quality of the environment given to its effects on the environment. Financial development and institutional development variables as well as some control variables were considered. In order to study cross-sectional correlation among the countries under study, Pesaran and Friz test was used. Since the results of both tests show cross-sectional correlation in the countries under study, seemingly unrelated regression method was utilized for model estimation. The results disclosed that Kuznets’ environmental curve hypothesis is confirmed in upper-middle income countries and also, financial development and institutional quality have a significant effect on environmental quality. The results of this study can be considered by policy makers in countries with different income groups to have access to a growth accompanied by improved environmental quality.

Keywords: economic development, environmental destruction, financial development, institutional development, seemingly unrelated regression

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Authors: Jinny Hong, Jeanne C. Watson

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Three types of autobiographical memory recall -specific, episodic, and generic- were examined in relation to the quality of descriptions and in-session levels of emotional arousal. Correlational analyses and general estimating equation were conducted to test the relationships between 1) quality of descriptions and type of memory, 2) type of memory and emotional arousal, and 3) quality of descriptions and emotional arousal. The data was transcripts drawn from an archival randomized-control study comparing cognitive-behavioral therapy and emotion-focused therapy in a 16-week treatment for depression. Autobiographical memory recall segments were identified and sorted into three categories: specific, episodic, and generic. Quality of descriptions of these segments was then operationalized and measured using the Referential Activity Scale, and each memory segment was rated on four dimensions: concreteness, specificity, clarity, and overall imagery. Clients’ level of emotional arousal for each recall was measured using the Client’s Expression Emotion Scale. Contrary to the predictions, generic memories are associated with higher emotional arousal ratings and descriptive language ratings compared to specific memories. However, a positive relationship emerged between the quality of descriptions and expressed emotional arousal, indicating that the quality of descriptions in which memories are described in sessions is more important than the type of memory recalled in predicting clients’ level of emotional arousal. The results from this study provide a clearer understanding of the role of memory recall types and use of language in activating emotional arousal in psychotherapy sessions in a depressed sample.

Keywords: autobiographical memory recall, emotional arousal, psychotherapy for depression, quality of descriptions, referential activity

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Authors: Dorota Zielińska, Monika Trząskowska, Anna Łepecka, Katarzyna Neffe-Skocińska, Beata Bilska, Marzena Tomaszewska, Danuta Kołożyn-Krajewska

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Minimum durability date (MDD) labeled food is known to have a long shelf life. A properly stored or transported food retains its physical, chemical, microbiological, and sensory properties up to MDD. The aim of the study was to assess the sensory quality and microbiological safety of selected thermally processed products,i.e., mayonnaise, jam, and canned tuna within and after MDD. The scope of the study was to determine the markers of microbiological quality, i.e., the total viable count (TVC), the Enterobacteriaceae count and the total yeast and mold (TYMC) count on the last day of MDD and after 1 and 3 months of storage, after the MDD expired. In addition, the presence of Salmonella and Listeria monocytogenes was examined on the last day of MDD. The sensory quality of products was assessed by quantitative descriptive analysis (QDA), the intensity of differentiators (quality features), and overall quality were defined and determined. It was found that during three months storage of tested food products, after the MDD expired, the microbiological quality slightly decreased, however, regardless of the tested sample, TVC was at the level of <3 log cfu/g, similarly, the Enterobacretiaceae, what indicates the good microbiological quality of the tested foods. The TYMC increased during storage but did not exceed 2 logs cfu/g of product. Salmonella and Listeria monocytogenes were not found in any of the tested food samples. The sensory quality of mayonnaise negatively changed during storage. After three months from the expiry of MDD, a decrease in the "fat" and "egg" taste and aroma intensity, as well as the "density" were found. The "sour" taste intensity of blueberry jam after three months of storage was slightly higher, compared to the jam tested on the last day of MDD, without affecting the overall quality. In the case of tuna samples, an increase in the "fishy" taste and aroma intensity was observed during storage, and the overall quality did not change. Tested thermally processed products (mayonnaise, jam, and canned tuna) were characterized by good microbiological and sensory quality on the last day of MDD, as well as after three months of storage under conditions recommended by the producer. These findings indicate the possibility of reducing food losses by extending or completely abolishing the MDD of selected thermal processed food products.

Keywords: food wastes, food quality and safety, mayonnaise, jam, tuna

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Authors: Debarati Bhaumik, Diptish Dey

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Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence.

Keywords: audit, multilevel model, model transparency, model explainability, discrimination, ethics

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Authors: Christoph Linse, Thomas Martinetz

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Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

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Authors: Velly Anatasia

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The worldwide annual expenditure for cosmetics is estimated at U.S. $18 billion and many players in the field are competing aggressively to capture more and more markets. Players in the cosmetics industry strive to be the foremost by establish customer loyalty. Furthermore, customer loyalty is portrayed by brand loyalty. Therefore, brand loyalty is the key determine of winning the competition in tight market. This study examines the influence of brand loyalty on cosmetics buying behavior of female consumers in Jakarta as capital of Indonesia. The seven factors of brand loyalty are brand name, Product quality, price, design, promotion, servicesquality and store environment. The paper adopted descriptive analysis, factor loading and multiple regression approach to test the hypotheses. The data has been collected by using questionnaires which were distributed and self-administered to 125female respondents accustomed using cosmetics. The findings of this study indicated that promotion has shown strong correlation with brand loyalty. The research results showed that there is positive and significant relationship between factors of brand loyalty (brand name, product quality, price, design, promotion, services quality and store environment) with cosmetics brand loyalty.

Keywords: brand loyalty, brand name, product quality, service quality, promotion

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Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

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The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial Neural network, Competitive dynamics, Logistic Regression, Text classification, Text mining

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Authors: Silvio Ricardo Rodrigues Sanches, Bianca Cogo Barbosa, Beatriz Regina Brum, Cléber Gimenez Corrêa

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To evaluate the quality of a segmentation algorithm, the authors use subjective or objective metrics. Although subjective metrics are more accurate than objective ones, objective metrics do not require user feedback to test an algorithm. Objective metrics require subjective experiments only during their development. Subjective experiments typically display to users some videos (generated from frames with segmentation errors) that simulate the environment of an application domain. This user feedback is crucial information for metric definition. In the subjective experiments applied to develop some state-of-the-art metrics used to test segmentation algorithms, the videos displayed during the experiments did not contain audio. Audio is an essential component in applications such as videoconference and augmented reality. If the audio influences the user’s perception, using only videos without audio in subjective experiments can compromise the efficiency of an objective metric generated using data from these experiments. This work aims to identify if the audio influences the user’s perception of segmentation quality in background substitution applications with audio. The proposed approach used a subjective method based on formal video quality assessment methods. The results showed that audio influences the quality of segmentation perceived by a user.

Keywords: background substitution, influence of audio, segmentation evaluation, segmentation quality

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Authors: Jeong-Hyun Park, Young-Sik Park, Hyo-Teag Jung

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This paper presents static analysis deployment model for code quality on R&D Projects of SW Development. The proposed model includes the scope of R&D projects and index for static analysis of source code, operation model and execution process, environments and infrastructure system for R&D projects of SW development. There is the static analysis result of pilot project as case study based on the proposed deployment model and environment, and strategic considerations for success operation of the proposed static analysis deployment model for R&D Projects of SW Development. The proposed static analysis deployment model in this paper will be adapted and improved continuously for quality upgrade of R&D projects, and customer satisfaction of developed source codes and products.

Keywords: static analysis, code quality, coding rules, automation tool

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Authors: Wenbo Du, Xiaomei Ma

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With the empowerment of Cognitive Diagnostic Assessment (CDA), various domains of language testing and assessment have been investigated to dig out more diagnostic information. What is noticeable is that most of the extant empirical CDA-based research puts much emphasis on individual-level diagnostic purpose with very few concerned about learners’ group-level performance. Even though the personalized diagnostic feedback is the unique feature that differentiates CDA from other assessment tools, group-level diagnostic information cannot be overlooked in that it might be more practical in classroom setting. Additionally, the group-level diagnostic information obtained via current CDA always results in a “flat pattern”, that is, the mastery/non-mastery of all tested skills accounts for the two highest proportion. In that case, the outcome does not bring too much benefits than the original total score. To address these issues, the present study attempts to apply cluster analysis for group classification and quantile regression analysis to pinpoint learners’ performance at different proficiency levels (beginner, intermediate and advanced) thus to enhance the interpretation of the CDA results extracted from a group of EFL learners’ reading performance on a diagnostic reading test designed by PELDiaG research team from a key university in China. The results show that EM method in cluster analysis yield more appropriate classification results than that of CDA, and quantile regression analysis does picture more insightful characteristics of learners with different reading proficiencies. The findings are helpful and practical for instructors to refine EFL reading curriculum and instructional plan tailored based on the group classification results and quantile regression analysis. Meanwhile, these innovative statistical methods could also make up the deficiencies of CDA and push forward the development of language testing and assessment in the future.

Keywords: cognitive diagnostic assessment, diagnostic feedback, EFL reading, quantile regression

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

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Authors: M. E. Khiav, D. J. Borah, H. T. S. Santos, V. T. Faria

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For automobile companies, it has become very important to ensure sound quality in manufacturing and assembling in order to prevent occurrence of defects and to reduce the amount of parts replacements to be done in the service centers during the warranty period. Statistical Process Control (SPC) is widely used as the tool to analyze the quality of such processes and plays a significant role in the improvement of the processes by identifying the patterns and the location of the defects. In this paper, a case study has been conducted on an Iranian automobile company. This paper performs a quality analysis of a particular component called “Internal Bearing for the Back Wheel” of a particular car model, manufactured by the company, based on the 10 million data received from its service centers located all over the country. By creating control charts including X bar–S charts and EWMA charts, it has been observed after the year 2009, the specific component underwent frequent failures and there has been a sharp dip in the average distance covered by the cars till the specific component requires replacement/maintenance. Correlation analysis was performed to find out the reasons that might have affected the quality of the specific component in all the cars produced by the company after the year 2009. Apart from manufacturing issues, some political and environmental factors have been identified to have a potential impact on the quality of the component. A maiden attempt has been made to analyze the quality issues within an Iranian automobile manufacturer; such issues often get neglected in developing countries. The paper also discusses the possibility of political scenario of Iran and the country’s environmental conditions affecting the quality of the end products, which not only strengthens the extant literature but also provides a new direction for future research.

Keywords: capability analysis, car manufacturing, statistical process control, quality control, quality tools

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Authors: Chad Goldsworthy, B. Rajeswari Matam

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The use of deep learning for species identification in camera trap images has revolutionised our ability to study, conserve and monitor species in a highly efficient and unobtrusive manner, with state-of-the-art models achieving accuracies surpassing the accuracy of manual human classification. The high imbalance of camera trap datasets, however, results in poor accuracies for minority (rare or endangered) species due to their relative insignificance to the overall model accuracy. This paper investigates the use of Focal Loss, in comparison to the traditional Cross Entropy Loss function, to improve the identification of minority species in the “255 Bird Species” dataset from Kaggle. The results show that, although Focal Loss slightly decreased the accuracy of the majority species, it was able to increase the F1-score by 0.06 and improve the identification of the bottom two, five and ten (minority) species by 37.5%, 15.7% and 10.8%, respectively, as well as resulting in an improved overall accuracy of 2.96%.

Keywords: convolutional neural networks, data imbalance, deep learning, focal loss, species classification, wildlife conservation

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Authors: S. A. Che Osmi, W. M. F. Wan Ishak, H. Kim, M. A. Azman, M. A. Ramli

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River is one of important water sources for many activities including industrial and domestic usage such as daily usage, transportation, power supply and recreational activities. However, increasing activities in a river has grown the sources of pollutant enters the water bodies, and degraded the water quality of the river. It becomes a challenge to develop an effective river management to ensure the water sources of the river are well managed and regulated. In Malaysia, several approaches for river management have been implemented such as Integrated River Basin Management (IRBM) program for coordinating the management of resources in a natural environment based on river basin to ensure their sustainability lead by Department of Drainage and Irrigation (DID), Malaysia. Nowadays, Total Maximum Daily Load (TMDL) is one of the best approaches for river management in Malaysia. TMDL implementation is regulated and implemented in the United States. A study on the development of TMDL in Malacca River has been carried out by doing water quality monitoring, the development of water quality model by using Environmental Fluid Dynamic Codes (EFDC), and TMDL implementation plan. The implementation of TMDL will help the stakeholders and regulators to control and improve the water quality of the river. It is one of the good approaches for river management in Malaysia.

Keywords: EFDC, river management, TMDL, water quality modelling

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Authors: Sihem Oujdi, Hafida Belbachir

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Existing methods of data mining cannot be applied on spatial data because they require spatial specificity consideration, as spatial relationships. This paper focuses on the classification with decision trees, which are one of the data mining techniques. We propose an extension of the C4.5 algorithm for spatial data, based on two different approaches Join materialization and Querying on the fly the different tables. Similar works have been done on these two main approaches, the first - Join materialization - favors the processing time in spite of memory space, whereas the second - Querying on the fly different tables- promotes memory space despite of the processing time. The modified C4.5 algorithm requires three entries tables: a target table, a neighbor table, and a spatial index join that contains the possible spatial relationship among the objects in the target table and those in the neighbor table. Thus, the proposed algorithms are applied to a spatial data pattern in the accidentology domain. A comparative study of our approach with other works of classification by spatial decision trees will be detailed.

Keywords: C4.5 algorithm, decision trees, S-CART, spatial data mining

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Authors: C. Cafaro, P. Ceci, L. De Giorgi

Abstract:

The aim of this paper is to give an assessment of environmental effects of IPPC permit conditions of installations that are in the specific territory with a high concentration of industrial activities. The IPPC permit is the permit that each operator should hold to operate the installation as stated by the directive 2010/75/UE on industrial emissions (integrated pollution prevention and control), known as IED (Industrial Emissions Directive). The IPPC permit includes all the measures necessary to achieve a high level of protection of the environment as a whole, also defining the monitoring requirements as measurement methodology, frequency, and evaluation procedure. The emissions monitoring of a specific plant may also give indications of the contribution of these emissions on the air quality of a definite area. So, it is clear that the IPPC permits are important tools both to improve the environmental framework and to achieve the air quality standards, assisting in assessing the possible industrial sources contributions to air pollution.

Keywords: IPPC, IED, emissions, permits, air quality, large combustion plants

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Authors: Alisa Salimova, Jiane Zuo, Christopher Homer

Abstract:

The objective of this study is to analyse the North Canal riparian zone land use with the help of remote sensing analysis in ArcGis using 30 cloudless Landsat8 open-source satellite images from May to August of 2013 and 2017. Land cover, urban construction, heat island effect, vegetation cover, and water system change were chosen as the main parameters and further analysed to evaluate its impact on the North Canal water quality. The methodology involved the following steps: firstly, 30 cloudless satellite images were collected from the Landsat TM image open-source database. The visual interpretation method was used to determine different land types in a catchment area. After primary and secondary classification, 28 land cover types in total were classified. Visual interpretation method was used with the help ArcGIS for the grassland monitoring, US Landsat TM remote sensing image processing with a resolution of 30 meters was used to analyse the vegetation cover. The water system was analysed using the visual interpretation method on the GIS software platform to decode the target area, water use and coverage. Monthly measurements of water temperature, pH, BOD, COD, ammonia nitrogen, total nitrogen and total phosphorus in 2013 and 2017 were taken from three locations of the North Canal in Tongzhou district. These parameters were used for water quality index calculation and compared to land-use changes. The results of this research were promising. The vegetation coverage of North Canal riparian zone in 2017 was higher than the vegetation coverage in 2013. The surface brightness temperature value was positively correlated with the vegetation coverage density and the distance from the surface of the water bodies. This indicates that the vegetation coverage and water system have a great effect on temperature regulation and urban heat island effect. Surface temperature in 2017 was higher than in 2013, indicating a global warming effect. The water volume in the river area has been partially reduced, indicating the potential water scarcity risk in North Canal watershed. Between 2013 and 2017, urban residential, industrial and mining storage land areas significantly increased compared to other land use types; however, water quality has significantly improved in 2017 compared to 2013. This observation indicates that the Tongzhou Water Restoration Plan showed positive results and water management of Tongzhou district had been improved.

Keywords: North Canal, land use, riparian vegetation, river ecology, remote sensing

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Authors: R. Periyasamy, Deepak Joshi, Sneh Anand

Abstract:

Foot posture assessment is important to evaluate foot type, causing gait and postural defects in all age groups. Although different methods are used for classification of foot arch type in clinical/research examination, there is no clear approach for selecting the most appropriate measurement system. Therefore, the aim of this study was to develop a system for evaluation of foot type as clinical decision-making aids for diagnosis of flat and normal arch based on the Arch Index (AI) and foot pressure distribution parameter - Power Ratio (PR) data. The accuracy of the system was evaluated for 27 subjects with age ranging from 24 to 65 years. Foot area measurements (hind foot, mid foot, and forefoot) were acquired simultaneously from foot pressure intensity image using portable PedoPowerGraph system and analysis of the image in frequency domain to obtain foot pressure distribution parameter - PR data. From our results, we obtain 100% classification accuracy of normal and flat foot by using the linear discriminant analysis method. We observe there is no misclassification of foot types because of incorporating foot pressure distribution data instead of only arch index (AI). We found that the mid-foot pressure distribution ratio data and arch index (AI) value are well correlated to foot arch type based on visual analysis. Therefore, this paper suggests that the proposed system is accurate and easy to determine foot arch type from arch index (AI), as well as incorporating mid-foot pressure distribution ratio data instead of physical area of contact. Hence, such computational tool based system can help the clinicians for assessment of foot structure and cross-check their diagnosis of flat foot from mid-foot pressure distribution.

Keywords: arch index, computational tool, static foot pressure intensity image, foot pressure distribution, linear discriminant analysis

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Authors: Mohammed Abdulridha Hamdan

Abstract:

To face the water crisis, understanding the role of human activities on nutrient concentrations in aquatic ecosystems needs more investigations to compare to extensively studies which have been carried out to understand these impacts on the water quality of different aquatic ecosystems. We hypothesized human activates on the catchments of Tigris river may change nutrient concentrations in water along the river. The results showed that phosphate concentration differed significantly among the studied sites due to distributed human activities, while nitrate concentration did not. Phosphate and nitrate concentrations were not affected by water temperature. We concluded that human activities on the surrounding landscapes could be more essential sources for nutrients of aquatic ecosystems than role of ongoing climate warming. Despite the role of warming in driving nutrients availability in aquatic ecosystems, our findings suggest to take the different activities on the surrounding catchments into account in the studies caring about the trophic status classification of aquatic ecosystems.

Keywords: nitrate, phosphate, anthropogenic, warming

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Authors: Kefaya Qaddoum

Abstract:

Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to model a simple but often satisfactory supervised classification method. The original naive Bayes have a serious weakness, which is producing redundant predictors. In this paper, utilized regularization technique was used to obtain a computationally efficient classifier based on naive Bayes. The suggested construction, utilized L1-penalty, is capable of clearing redundant predictors, where a modification of the LARS algorithm is devised to solve this problem, making this method applicable to a wide range of data. In the experimental section, a study conducted to examine the effect of redundant and irrelevant predictors, and test the method on WSG data set for tomato yields, where there are many more predictors than data, and the urge need to predict weekly yield is the goal of this approach. Finally, the modified approach is compared with several naive Bayes variants and other classification algorithms (SVM and kNN), and is shown to be fairly good.

Keywords: tomato yield prediction, naive Bayes, redundancy, WSG

Procedia PDF Downloads 216