Search results for: reading method classification

Authors: Abdulsalam Abiodun Salman, Abdulmumin Isah

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The study investigated the motivational factors that could enhance the teaching and understanding of ICT-driven cataloguing and classification (Cat and Class) practical classes among students of library and information science (LIS) in Kwara State Library Schools, Nigeria. It deployed a positivist research paradigm using a quantitative method by deploying the use of questionnaires for data collection. The population of the study is one thousand, one hundred and twenty-five (1,125) which was obtained from the department of each respective library school (the University of Ilorin, Ilorin (Unilorin); Federal Polytechnic Offa, (Fedpoffa); and Kwara State University (KWASU). The sample size was determined using the research advisor table. Hence, the study sample of one hundred and ten (110) was used. The findings revealed that LIS students were averagely motivated toward ICT-driven Cataloguing and Classification practical classes. The study recommended that modern cataloguing and classification tools for practical classes should be made available in the laboratories as motivational incentives for students. It was also recommended that library schools should motivate the students beyond the provision of these ICT-driven tools but also extend the practical class periods. Availability and access to medical treatment in case of injuries during the practical classes should be made available. Technologists/Tutors of Cat and Class practical classes should also be exposed to further training in modern trends, especially emerging digital knowledge and skills in cataloguing and classification. This will keep both the tutors and students abreast of the new development in the technological arena.

Keywords: cataloguing and classification, motivational factors, ICT-driven practical classes, LIS students, Nigeria

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Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

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Authors: Kemal Polat

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In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset.

Keywords: fuzzy C-means clustering, fuzzy C-means clustering based attribute weighting, Pima Indians diabetes, SVM

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Authors: Roza Dzierzak, Waldemar Wojcik, Piotr Kacejko

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Analysis of the texture of medical images consists of determining the parameters and characteristics of the examined tissue. The main goal is to assign the analyzed area to one of two basic groups: as a healthy tissue or a tissue with pathological changes. The CT images of the thoracic lumbar spine from 15 healthy patients and 15 with confirmed osteoporosis were used for the analysis. As a result, 120 samples with dimensions of 50x50 pixels were obtained. The set of features has been obtained based on the histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and Haar wavelet. As a result of the image analysis, 290 descriptors of textural features were obtained. The dimension of the space of features was reduced by the use of three selection methods: Fisher coefficient (FC), mutual information (MI), minimization of the classification error probability and average correlation coefficients between the chosen features minimization of classification error probability (POE) and average correlation coefficients (ACC). Each of them returned ten features occupying the initial place in the ranking devised according to its own coefficient. As a result of the Fisher coefficient and mutual information selections, the same features arranged in a different order were obtained. In both rankings, the 50% percentile (Perc.50%) was found in the first place. The next selected features come from the co-occurrence matrix. The sets of features selected in the selection process were evaluated using six classification tree methods. These were: decision stump (DS), Hoeffding tree (HT), logistic model trees (LMT), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). In order to assess the accuracy of classifiers, the following parameters were used: overall classification accuracy (ACC), true positive rate (TPR, classification sensitivity), true negative rate (TNR, classification specificity), positive predictive value (PPV) and negative predictive value (NPV). Taking into account the classification results, it should be stated that the best results were obtained for the Hoeffding tree and logistic model trees classifiers, using the set of features selected by the POE + ACC method. In the case of the Hoeffding tree classifier, the highest values of three parameters were obtained: ACC = 90%, TPR = 93.3% and PPV = 93.3%. Additionally, the values of the other two parameters, i.e., TNR = 86.7% and NPV = 86.6% were close to the maximum values obtained for the LMT classifier. In the case of logistic model trees classifier, the same ACC value was obtained ACC=90% and the highest values for TNR=88.3% and NPV= 88.3%. The values of the other two parameters remained at a level close to the highest TPR = 91.7% and PPV = 91.6%. The results obtained in the experiment show that the use of classification trees is an effective method of classification of texture features. This allows identifying the conditions of the spongy tissue for healthy cases and those with the porosis.

Keywords: classification, feature selection, texture analysis, tree algorithms

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Authors: Ali Ulvi Uzer

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Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

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Authors: M. Inbar Daeribi, Vara Leoni

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Street Library Movement has been established and started to grow in some cities in Indonesia as a social movement. In the beginning, this movement emerged as a response to Indonesian lack of reading culture. Nevertheless, this study found out that street library movement is not only a literacy movement for developing reading culture. Furthermore, this movement is also a resistance towards education system in Indonesia. Street library movement is a critical consciousness driven by autonomous working group (community) as counter-public form towards Indonesia’s education condition legitimated by the government. This study, conducted in qualitative method with street library movement in Sukabumi, West Java, Indonesia as the object of study, will examine resistance forms of this movement and its social impacts. By studying this paper, it can be explained how street library movement served as an engine for social development.

Keywords: street library movement, social movement, resistance, education system

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Authors: Katsumi Hirata

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Certain systems can function well only if they recognize the sound environment as humans do. In this research, we focus on sound classification by adopting a convolutional neural network and aim to develop a method that automatically classifies various environmental sounds. Although the neural network is a powerful technique, the performance depends on the type of input data. Therefore, we propose an approach via a slice bispectrogram, which is a third-order spectrogram and is a slice version of the amplitude for the short-time bispectrum. This paper explains the slice bispectrogram and discusses the effectiveness of the derived method by evaluating the experimental results using the ESC‑50 sound dataset. As a result, the proposed scheme gives high accuracy and stability. Furthermore, some relationship between the accuracy and non-Gaussianity of sound signals was confirmed.

Keywords: environmental sound, bispectrum, spectrogram, slice bispectrogram, convolutional neural network

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Authors: Shahabeddin Sotudian, M. H. Fazel Zarandi, I. B. Turksen

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Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty.

Keywords: hepatitis disease, medical diagnosis, type-I fuzzy logic, type-II fuzzy logic, feature selection

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Authors: Agung Rizki Perdana, Asrofi Mursalin, Adniwan Shubhi Banuzaki, M. Indra Novian

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The understanding about sediment-derived plastiglomerate has a wide-range of merit in the academic realm. It can cover discussions about the Anthropocene Epoch in the scope of geoscience knowledge to even provide a solution for the environmental problem of plastic waste. Albeit its importance, very few research has been done regarding this issue. This research aims to create a classification as a pioneer for the study of sediment-derived plastiglomerate. This research was done in Bantul Regency, Daerah Istimewa Yogyakarta Province as an analogue of plastic debris sedimentation process. Observation is carried out in five observation points that shows three different depositional environments, which are terrestrial, fluvial, and transitional environment. The resulting classification uses three parameters and forms in a taxonomical manner. These parameters are composition, degree of lithification, and abundance of matrix respectively in advancing order. There is also a compositional ternary diagram which should be followed before entering the plastiglomerate nomenclature classification.

Keywords: plastiglomerate, classification, sedimentary mechanism, microplastic

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Authors: Kwangmin Joo

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Automatic detection of lumbar vertebrae and classification method is proposed for evaluating the degree of disc narrowing. Prior to classification, deep learning based segmentation is applied to detect individual lumbar vertebra. M-net is applied to segment five lumbar vertebrae and fine-tuning segmentation is employed to improve the accuracy of segmentation. Using the features extracted from previous step, clustering technique, k-means clustering, is applied to estimate the degree of disc space narrowing under four grade scoring system. As preliminary study, techniques proposed in this research could help building an automatic scoring system to diagnose the severity of disc narrowing from X-ray images.

Keywords: Disc space narrowing, Degenerative disc disorders, Deep learning based segmentation, Clustering technique

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Authors: Rangika Perera, Shyamani Hettiarachchi, Fran Hagstrom

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Dyslexia is the most common reading reading-related difficulty among the school school-aged population and currently, 5-10% are showing the features of dyslexia in Sri Lanka. As there is an insufficient number of speech and language pathologists in the country and few speech and language pathologists working in government mainstream school settings, these children who are at risk of dyslexia are not receiving enough quality early intervention services to develop their reading skills. As teachers are the key professionals who are directly working with these children, using them as the primary facilitators to improve their reading skills will be the most effective approach. This study aimed to identify the efficacy of a two and half a day of intensive training provided to fifteen mainstream government school teachers of grade two classes. The goal of the training was to enhance their knowledge of dyslexia and provide full classroom skills training that could be used to support the development of the students’ reading competencies. A closed closed-ended multiple choice questionnaire was given to these teachers pre and -post-training to measure teachers’ knowledge of dyslexia, the areas in which these children needed additional support, and the best strategies to facilitate reading competencies. The data revealed that the teachers’ knowledge in all areas was significantly poorer prior to the training and that there was a clear improvement in all areas after the training. The gain in target areas of teaching skills selected to improve the reading skills of children was evaluated through peer feedback. Teachers were assigned to three groups and expected to model how they were going to introduce the skills in recommended areas using researcher developed, validated and reliability reliability-tested materials and the strategies which were introduced during the training within the given tasks. Peers and the primary investigator rated teachers’ performances and gave feedback on organizational skills, presentation skills of materials, clarity of instruction, and appropriateness of vocabulary. After modifying their skills according to the feedback the teachers received, they were expected to modify and represent the same tasks to the group the following day. Their skills were re-evaluated by the peers and primary investigator using the same rubrics to measure the improvement. The findings revealed a significant improvement in their teaching skills development. The data analysis of both knowledge and skills gains of the teachers was carried out using quantitative descriptive data analysis. The overall findings of the study yielded promising results that support intensive training as a method for improving teachers’ knowledge and teaching skill development for use with children in a whole class intervention setting who are at risk of dyslexia.

Keywords: Dyslexia, knowledge, teaching skills, training program

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Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Harbor protection systems are so important. The need for automatic protection systems has increased over the last years. Diver detection active sonar has great significance. It used to detect underwater threats such as divers and autonomous underwater vehicle. To automatically detect such threats the sonar image is processed by algorithms. These algorithms used to detect, track and classify of underwater objects. In this work, divers tracking and classification algorithm is improved be proposing a robust wake detection method. To detect objects the sonar images is normalized then segmented based on fixed threshold. Next, the centroids of the segments are found and clustered based on distance metric. Then to track the objects linear Kalman filter is applied. To reduce effect of noise and creation of false tracks, the Kalman tracker is fine tuned. The tuning is done based on our active sonar specifications. After the tracks are initialed and updated they are subjected to a filtering stage to eliminate the noisy and unstable tracks. Also to eliminate object with a speed out of the diver speed range such as buoys and fast boats. Afterwards the result tracks are subjected to a classification stage to deiced the type of the object been tracked. Here the classification stage is to deice wither if the tracked object is an open circuit diver or a close circuit diver. At the classification stage, a small area around the object is extracted and a novel wake detection method is applied. The morphological features of the object with his wake is extracted. We used support vector machine to find the best classifier. The sonar training images and the test images are collected by ARMELSAN Defense Technologies Company using the portable diver detection sonar ARAS-2023. After applying the algorithm to the test sonar data, we get fine and stable tracks of the divers. The total classification accuracy achieved with the diver type is 97%.

Keywords: harbor protection, diver detection, active sonar, wake detection, diver classification

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Authors: Emmanuel Uba, Oluwasegun Omidiora, Eugenia Abiodun-Eniayekan

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The role of phonology on reading comprehension is long established in the literature. However, the vast majority of studies on the relationship between phonology and reading or comprehension among adults involve investigating the role of intonation, stress, and segmental knowledge on understanding texts. Not much attention is paid to junctural observation and its effect on the interpretation of texts. This study, therefore, presents a preliminary case-study investigation of the effect of juncture on comprehension of texts among adult Nigerian learners of English. Eighty adult learners of English in Nigeria were presented with fifteen seemingly ambiguous sentences to interpret. The sentences were structured in a way that pausing at different points would produce different interpretations. The results reveal that wrong application of pause is capable of affecting comprehension even when other phonological factors such as stress and intonation are observed properly.

Keywords: comprehension, juncture, phonology, reading

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Authors: Mohammad Saberian, Reza Porhoseini, Mohammad Ali Rahgozar

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Peat is a partially carbonized vegetable tissue which is formed in wet conditions by decomposition of various plants, mosses and animal remains. This restricted definition, including only materials which are entirely of vegetative origin, conflicts with several established soil classification systems. Peat soils are usually defined as soils having more than 75 percent organic matter. Due to this composition, the structure of peat soil is highly different from the mineral soils such as silt, clay and sand. Peat has high compressibility, high moisture content, low shear strength and low bearing capacity, so it is considered to be in the category of problematic. Since this kind of soil is generally found in many countries and various zones, except for desert and polar zones, recognizing this soil is inevitably significant. The objective of this paper is to review the classification of peats based on various properties of peat soils such as organic contents, water content, color, odor, and decomposition, scholars offer various classification systems which Von Post classification system is one of the most well-known and efficient system.

Keywords: peat soil, degree of decomposition, organic content, water content, Von Post classification

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Authors: Ismail Elkhrachy

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Determination of land use changing is an important component of regional planning for applications ranging from urban fringe change detection to monitoring change detection of land use. This data are very useful for natural resources management.On the other hand, the technologies and methods of change detection also have evolved dramatically during past 20 years. So it has been well recognized that the change detection had become the best methods for researching dynamic change of land use by multi-temporal remotely-sensed data. The objective of this paper is to assess, evaluate and monitor land use change surrounding the area of Najran city, Kingdom of Saudi Arabia (KSA) using Landsat images (June 23, 2009) and ETM+ image(June. 21, 2014). The post-classification change detection technique was applied. At last,two-time subset images of Najran city are compared on a pixel-by-pixel basis using the post-classification comparison method and the from-to change matrix is produced, the land use change information obtained.Three classes were obtained, urban, bare land and agricultural land from unsupervised classification method by using Erdas Imagine and ArcGIS software. Accuracy assessment of classification has been performed before calculating change detection for study area. The obtained accuracy is between 61% to 87% percent for all the classes. Change detection analysis shows that rapid growth in urban area has been increased by 73.2%, the agricultural area has been decreased by 10.5 % and barren area reduced by 7% between 2009 and 2014. The quantitative study indicated that the area of urban class has unchanged by 58.2 km〗^2, gained 70.3 〖km〗^2 and lost 16 〖km〗^2. For bare land class 586.4〖km〗^2 has unchanged, 53.2〖km〗^2 has gained and 101.5〖km〗^2 has lost. While agriculture area class, 20.2〖km〗^2 has unchanged, 31.2〖km〗^2 has gained and 37.2〖km〗^2 has lost.

Keywords: land use, remote sensing, change detection, satellite images, image classification

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Authors: Zin Mar Lwin

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Brain Computer Interface (BCI) Systems have developed for people who suffer from severe motor disabilities and challenging to communicate with their environment. BCI allows them for communication by a non-muscular way. For communication between human and computer, BCI uses a type of signal called Electroencephalogram (EEG) signal which is recorded from the human„s brain by means of an electrode. The electroencephalogram (EEG) signal is an important information source for knowing brain processes for the non-invasive BCI. Translating human‟s thought, it needs to classify acquired EEG signal accurately. This paper proposed a typical EEG signal classification system which experiments the Dataset from “Purdue University.” Independent Component Analysis (ICA) method via EEGLab Tools for removing artifacts which are caused by eye blinks. For features extraction, the Time and Frequency features of non-stationary EEG signals are extracted by Matching Pursuit (MP) algorithm. The classification of one of five mental tasks is performed by Multi_Class Support Vector Machine (SVM). For SVMs, the comparisons have been carried out for both 1-against-1 and 1-against-all methods.

Keywords: BCI, EEG, ICA, SVM

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Authors: M. C. Paliwal, A. K. Jain, S. K. Katiyar

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Classification of satellite imagery is very important for the assessment of its accuracy. In order to determine the accuracy of the classified image, usually the assumed-true data are derived from ground truth data using Global Positioning System. The data collected from satellite imagery and ground truth data is then compared to find out the accuracy of data and error matrices are prepared. Overall and individual accuracies are calculated using different methods. The study illustrates advanced classification and accuracy assessment of land use/land cover mapping using satellite imagery. IRS-1C-LISS IV data were used for classification of satellite imagery. The satellite image was classified using the software in fourteen classes namely water bodies, agricultural fields, forest land, urban settlement, barren land and unclassified area etc. Classification of satellite imagery and calculation of accuracy was done by using ERDAS-Imagine software to find out the best method. This study is based on the data collected for Bhopal city boundaries of Madhya Pradesh State of India.

Keywords: resolution, accuracy assessment, land use mapping, satellite imagery, ground truth data, error matrices

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Authors: Magali Mari, Misha Muller

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The present study’s objectives were to determine how diverse implicit stereotypes affect the processing of written information and linguistic inferential processes, such as presupposition accommodation. When reading a text, one constructs a representation of the described situation, which is then updated, according to new outputs and based on stereotypes inscribed within society. If the new output contradicts stereotypical expectations, the representation must be corrected, resulting in longer reading times. A similar process occurs in cases of linguistic inferential processes like presupposition accommodation. Presupposition accommodation is traditionally regarded as fast, automatic processing of background information (e.g., ‘Mary stopped eating meat’ is quickly processed as Mary used to eat meat). However, very few accounts have investigated if this process is likely to be influenced by domains of social cognition, such as implicit stereotypes. To study the effects of implicit stereotypes on presupposition accommodation, adults were recorded while they read sentences in French, combining two methods, an eye-tracking task and a classic self-paced reading task (where participants read sentence segments at their own pace by pressing a computer key). In one condition, presuppositions were activated with the French definite articles ‘le/la/les,’ whereas in the other condition, the French indefinite articles ‘un/une/des’ was used, triggering no presupposition. Using a definite article presupposes that the object has already been uttered and is thus part of background information, whereas using an indefinite article is understood as the introduction of new information. Two types of stereotypes were under examination in order to enlarge the scope of stereotypes traditionally analyzed. Study 1 investigated gender stereotypes linked to professional occupations to replicate previous findings. Study 2 focused on nationality-related stereotypes (e.g. ‘the French are seducers’ versus ‘the Japanese are seducers’) to determine if the effects of implicit stereotypes on reading are generalizable to other types of implicit stereotypes. The results show that reading is influenced by the two types of implicit stereotypes; in the two studies, the reading pace slowed down when a counter-stereotype was presented. However, presupposition accommodation did not affect participants’ processing of information. Altogether these results show that (a) implicit stereotypes affect the processing of written information, regardless of the type of stereotypes presented, and (b) that implicit stereotypes prevail over the superficial linguistic treatment of presuppositions, which suggests faster processing for treating social information compared to linguistic information.

Keywords: eye-tracking, implicit stereotypes, reading, social cognition

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Authors: Younghoon Kim, Seoung Bum Kim

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One-class classification plays an important role in detecting outlier and abnormality from normal observations. In the previous research, several attempts were made to extend the scope of application of the one-class classification techniques to statistical process control problems. For most previous approaches, such as support vector data description (SVDD) control chart, the design of the control limits is commonly based on the assumption that the proportion of abnormal observations is approximately equal to an expected Type I error rate in Phase I process. Because of the limitation of the one-class classification techniques based on convex optimization, we cannot make the proportion of abnormal observations exactly equal to expected Type I error rate: controlling Type I error rate requires to optimize constraints with integer decision variables, but convex optimization cannot satisfy the requirement. This limitation would be undesirable in theoretical and practical perspective to construct effective control charts. In this work, to address the limitation of previous approaches, we propose the one-class classification algorithm based on the mixed integer programming technique, which can solve problems formulated with continuous and integer decision variables. The proposed method minimizes the radius of a spherically shaped boundary subject to the number of normal data to be equal to a constant value specified by users. By modifying this constant value, users can exactly control the proportion of normal data described by the spherically shaped boundary. Thus, the proportion of abnormal observations can be made theoretically equal to an expected Type I error rate in Phase I process. Moreover, analogous to SVDD, the boundary can be made to describe complex structures by using some kernel functions. New multivariate control chart applying the effectiveness of the algorithm is proposed. This chart uses a monitoring statistic to characterize the degree of being an abnormal point as obtained through the proposed one-class classification. The control limit of the proposed chart is established by the radius of the boundary. The usefulness of the proposed method was demonstrated through experiments with simulated and real process data from a thin film transistor-liquid crystal display.

Keywords: control chart, mixed integer programming, one-class classification, support vector data description

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

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Authors: Pi-Lan Yang

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It is controversial which type of gloss condition (i.e., gloss language or gloss position) is more effective in second or foreign language (L2) vocabulary learning. The present study compared the performance on learning ten English words in the conditions of L2 English reading with no glosses and with glosses of Chinese equivalents/translations and L2 English definitions at the side of a page and at an attached sheet for low-intermediate Chinese-speaking learners of English, who were technology university students in Taiwan. It is found first that the performances on the immediate posttest and the delayed posttest were overall better in the gloss condition than those in the no-gloss condition. Next, it is found that the glosses of Chinese translations were more effective and sustainable than those of L2 English definitions. Finally, the effects of L2 English glosses at the side of a page were observed to be less sustainable than those at an attached sheet. In addition, an opinion questionnaire used also showed a preference for the glosses of Chinese translations in L2 English reading. These results would be discussed in terms of automated lexical access, sentence processing mechanisms, and the trade-off nature of storage and processing functions in working memory system, proposed by the capacity theory of language comprehension.

Keywords: glosses of Chinese equivalents/translations, glosses of L2 English definitions, L2 vocabulary learning, L2 English reading

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Authors: Kavita Bodke

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Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception.

Keywords: SHM, CNN, deep learning, multi-class classification, multi-label classification

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Authors: Ren Moses

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The process of classifying vehicles on a highway is hereby viewed as a pattern recognition problem in which connectionist techniques such as artificial neural networks (ANN) can be used to assign vehicles to their correct classes and hence to establish optimum axle spacing thresholds. In the United States, vehicles are typically classified into 13 classes using a methodology commonly referred to as “Scheme F”. In this research, the ANN model was developed, trained, and applied to field data of vehicles. The data comprised of three vehicular features—axle spacing, number of axles per vehicle, and overall vehicle weight. The ANN reduced the classification error rate from 9.5 percent to 6.2 percent when compared to an existing classification algorithm that is not ANN-based and which uses two vehicular features for classification, that is, axle spacing and number of axles. The inclusion of overall vehicle weight as a third classification variable further reduced the error rate from 6.2 percent to only 3.0 percent. The promising results from the neural networks were used to set up new thresholds that reduce classification error rate.

Keywords: artificial neural networks, vehicle classification, traffic flow, traffic analysis, and highway opera-tions

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Authors: C. Mayr, J. Periya, A. Kariminezhad

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In this paper, we developed a learning framework for the classification of vulnerable road users (VRU) by their range-Doppler signatures. The frequency-modulated continuous-wave (FMCW) radar raw data is first pre-processed to obtain robust object range-Doppler maps per coherent time interval. The complex-valued range-Doppler maps captured from our outdoor measurements are further fed into a convolutional neural network (CNN) to learn the classification. This CNN has gone through a hyperparameter optimization process for improved learning. By learning VRU range-Doppler signatures, the three classes 'pedestrian', 'dog', and 'noise' are classified with an average accuracy of almost 95%. Interestingly, this classification accuracy holds for a combined longitudinal and lateral object trajectories.

Keywords: machine learning, radar, signal processing, autonomous driving

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Authors: Eugene Y. J. Aw, J. W. Koh, S. H. Chew, K. E. Chua, Lionel L. J. Ang, Algernon C. S. Hong, Danette S. E. Tan, Grace H. B. Foo, K. Q. Hong, L. M. Cheng, M. L. Leong

Abstract:

This paper presents a novel rapid soil classification technique that combines computer vision with four-probe soil electrical resistivity method and cone penetration test (CPT), to improve the accuracy and productivity of on-site classification of excavated soil. In Singapore, excavated soils from local construction projects are transported to Staging Grounds (SGs) to be reused as fill material for land reclamation. Excavated soils are mainly categorized into two groups (“Good Earth” and “Soft Clay”) based on particle size distribution (PSD) and water content (w) from soil investigation reports and on-site visual survey, such that proper treatment and usage can be exercised. However, this process is time-consuming and labour-intensive. Thus, a rapid classification method is needed at the SGs. Computer vision, four-probe soil electrical resistivity and CPT were combined into an innovative non-destructive and instantaneous classification method for this purpose. The computer vision technique comprises soil image acquisition using industrial grade camera; image processing and analysis via calculation of Grey Level Co-occurrence Matrix (GLCM) textural parameters; and decision-making using an Artificial Neural Network (ANN). Complementing the computer vision technique, the apparent electrical resistivity of soil (ρ) is measured using a set of four probes arranged in Wenner’s array. It was found from the previous study that the ANN model coupled with ρ can classify soils into “Good Earth” and “Soft Clay” in less than a minute, with an accuracy of 85% based on selected representative soil images. To further improve the technique, the soil strength is measured using a modified mini cone penetrometer, and w is measured using a set of time-domain reflectometry (TDR) probes. Laboratory proof-of-concept was conducted through a series of seven tests with three types of soils – “Good Earth”, “Soft Clay” and an even mix of the two. Validation was performed against the PSD and w of each soil type obtained from conventional laboratory tests. The results show that ρ, w and CPT measurements can be collectively analyzed to classify soils into “Good Earth” or “Soft Clay”. It is also found that these parameters can be integrated with the computer vision technique on-site to complete the rapid soil classification in less than three minutes.

Keywords: Computer vision technique, cone penetration test, electrical resistivity, rapid and non-destructive, soil classification

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

Abstract:

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

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

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Authors: Amal Abdelhadi

Abstract:

Consumer misbehavior evaluation can be markedly different based on a number of variables and different from one environment to another. Using three aberrant consumer behavior (ACB) scenarios (shoplifting, stealing from hotel rooms and software piracy) this study aimed to explore Libyan seller and consumers of ACB. Materials were collected by using a multi-method approach was employed (qualitative and quantitative approaches) in two fieldwork phases. In the phase stage, a qualitative data were collected from 26 Libyan sellers’ by face-to-face interviews. In the second stage, a consumer survey was used to collect quantitative data from 679 Libyan consumers. This study found that the consumer’s and seller’s evaluation of ACB are not always consistent. Further, ACB evaluations differed based on the form of ACB. Furthermore, the study found that not all consumer behaviors that were considered as bad behavior in other countries have the same evaluation in Libya; for example, software piracy. Therefore this study suggested a newly developed classification of ACB based on marketers’ and consumers’ views. This classification provides 9 ACB types within two dimensions (marketers’ and consumers’ views) and three degrees of behavior evaluation (good, acceptable and misbehavior).

Keywords: aberrant consumer behavior, Libya, multi-method approach, planned behavior theory

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Authors: Hrishi Rakshit, Pooneh Bagheri Zadeh

Abstract:

This paper investigates the performance of drone classification methods using conventional DCNN with different hyperparameters, when additional drone audio data is embedded in the dataset for training and further classification. In this paper, first a custom dataset is created using different images of drones from University of South California (USC) datasets and Leeds Beckett university datasets with embedded drone audio signal. The three well-known DCNN architectures namely, Resnet50, Darknet53 and Shufflenet are employed over the created dataset tuning their hyperparameters such as, learning rates, maximum epochs, Mini Batch size with different optimizers. Precision-Recall curves and F1 Scores-Threshold curves are used to evaluate the performance of the named classification algorithms. Experimental results show that Resnet50 has the highest efficiency compared to other DCNN methods.

Keywords: drone classifications, deep convolutional neural network, hyperparameters, drone audio signal

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Authors: Jalel Khelil

Abstract:

In order to improve reliability of Gas Turbine machine especially its generator equipment, a fault diagnosis system based on fuzzy approach is proposed. Three various methods namely K-NN (K-nearest neighbors), F-KNN (Fuzzy K-nearest neighbors) and FNM (Fuzzy nearest mean) are adopted to provide the measurement of relative strength of vibration defaults. Both applications consist of two major steps: Feature extraction and default classification. 09 statistical features are extracted from vibration signals. 03 different classes are used in this study which describes vibrations condition: Normal, unbalance defect, and misalignment defect. The use of the fuzzy approaches and the classification results are discussed. Results show that these approaches yield high successful rates of vibration default classification.

Keywords: fault diagnosis, fuzzy classification k-nearest neighbor, vibration

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Authors: Bhalke D. G., Bormane D. S., Kharate G. K.

Abstract:

This paper presents classification of musical instrument using machine learning techniques. The classification has been carried out using temporal, spectral, cepstral and wavelet features. Detail feature analysis is carried out using separate and combined features. Further, instrument model has been developed using K-Nearest Neighbor and Support Vector Machine (SVM). Benchmarked McGill university database has been used to test the performance of the system. Experimental result shows that SVM performs better as compared to KNN classifier.

Keywords: feature extraction, SVM, KNN, musical instruments

Procedia PDF Downloads 476