Scholarly Research Excellence

Digital Open Science Index

Commenced in January 2007 Frequency: Monthly Edition: International Paper Count: 688

688
10009593
Classification of Computer Generated Images from Photographic Images Using Convolutional Neural Networks
Abstract:
This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.
687
10009605
Reverse Twin Block with Expansion Screw for Treatment of Skeletal Class III Malocclusion in Growing Patient: Case Report
Abstract:

Class III malocclusion shows both skeletal and dentoalveolar component. Sketal Class III malocclusion can have variants in different region, maxilla or mandibular. Skeletal Class III malocclusion during growth period is considered to treat to prevent its severity in adulthood. Orthopedics treatment of skeletal Class III malocclusion in growing patient can be treated by using reverse twin block with expansion screw to modify the growth pattern. The objective of this case report was to describe the functional correction of skeletal Class III maloclussion using reverse twin block with expansion screw in growing patient. A patient with concave profile came with a chief complaint of aesthetic problems. The cephalometric analysis showed that patient had skeletal Class III malocclusion (ANB -50, SNA 75º, Wits appraisal -3 mm) with anterior cross bite and deep bite (overjet -3 mm, overbite 6 mm). In this case report, the patient was treated with reverse twin block appliance with expansion screw. After three months of treatment, the skeletal problems have been corrected (ANB -1°), overjet, overbite and aesthetic were improved. Reverse twin block appliance with expansion screw can be used as orthopedics treatment for skeletal Class III malocclusion in growing patient and can improve the aesthetic with great satisfaction which was the main complaint in this patient.

686
10009659
Evaluation of the Performance of ACTIFLO® Clarifier in the Treatment of Mining Wastewaters: Case Study of Costerfield Mining Operations, Victoria, Australia
Abstract:

A pre-treatment stage prior to reverse osmosis (RO) is very important to ensure the long-term performance of the RO membranes in any wastewater treatment using RO. This study aims to evaluate the application of the Actiflo® clarifier as part of a pre-treatment unit in mining operations. It involves performing analytical testing on RO feed water before and after installation of Actiflo® unit. Water samples prior to RO plant stage were obtained on different dates from Costerfield mining operations in Victoria, Australia. Tests were conducted in an independent laboratory to determine the concentration of various compounds in RO feed water before and after installation of Actiflo® unit during the entire evaluated period from December 2015 to June 2018. Water quality analysis shows that the quality of RO feed water has remarkably improved since installation of Actiflo® clarifier. Suspended solids (SS) and turbidity removal efficiencies has been improved by 91 and 85 percent respectively in pre-treatment system since the installation of Actiflo®. The Actiflo® clarifier proved to be a valuable part of pre-treatment system prior to RO. It has the potential to conveniently condition the mining wastewater prior to RO unit, and reduce the risk of RO physical failure and irreversible fouling. Consequently, reliable and durable operation of RO unit with minimum requirement for RO membrane replacement is expected with Actiflo® in use.

685
10009666
Emotional Analysis for Text Search Queries on Internet
Abstract:
The goal of this study is to analyze if search queries carried out in search engines such as Google, can offer emotional information about the user that performs them. Knowing the emotional state in which the Internet user is located can be a key to achieve the maximum personalization of content and the detection of worrying behaviors. For this, two studies were carried out using tools with advanced natural language processing techniques. The first study determines if a query can be classified as positive, negative or neutral, while the second study extracts emotional content from words and applies the categorical and dimensional models for the representation of emotions. In addition, we use search queries in Spanish and English to establish similarities and differences between two languages. The results revealed that text search queries performed by users on the Internet can be classified emotionally. This allows us to better understand the emotional state of the user at the time of the search, which could involve adapting the technology and personalizing the responses to different emotional states.
684
10009715
Shaping of World-Class Delhi: Politics of Marginalization and Inclusion
Abstract:

In the context of the government's vision of turning Delhi into a green, privatized and slum free city, giving it a world-class image at par with the global cities of the world, this paper investigates into the various processes and politics of things that went behind defining spaces in the city and attributing an aesthetic image to it. The paper will explore two cases that were forged primarily through the forces of one particular type of power relation. One would be to look at the modernist movement adopted by the Nehruvian government post-independence and the next case will look at special periods like Emergency and Commonwealth games. The study of these cases will help understand the ambivalence embedded in the different rationales of the Government and different powerful agencies adopted in order to build world-classness. Through the study, it will be easier to discern how city spaces were reconfigured in the name of 'good governance'. In this process, it also became important to analyze the double nature of law, both as a protector of people’s rights and as a threat to people. What was interesting to note through the study was that in the process of nation building and creating an image for the city, the government’s policies and programs were mostly aimed at the richer sections of the society and the poorer sections and people from lower income groups kept getting marginalized, subdued, and pushed further away (These marginalized people were pushed away even geographically!). The reconfiguration of city space and attributing an aesthetic character to it, led to an alteration not only in the way in which citizens perceived and engaged with these spaces, but also brought about changes in the way they envisioned their place in the city. Ironically, it was found that every attempt to build any kind of facility for the city’s elite in turn led to an inevitable removal of the marginalized sections of the society as a necessary step to achieve a clean, green and world-class city. The paper questions the claim made by the government for creating a just, equitable city and granting rights to all. An argument is put forth that in the politics of redistribution of space, the city that has been designed is meant for the aspirational middle-class and elite only, who are ideally primed to live in world-class cities. Thus, the aim is to study city spaces, urban form, the associated politics and power plays involved within and understand whether segmented cities are being built in the name of creating sensible, inclusive cities.

683
10009491
Synthetic Aperture Radar Remote Sensing Classification Using the Bag of Visual Words Model to Land Cover Studies
Abstract:

Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.

682
10009526
Clash of Civilizations without Civilizational Groups: Revisiting Samuel P. Huntington´s Clash of Civilizations Theory
Authors:
Abstract:

This paper is largely a response/critique of Samuel P. Huntington´s Clash of Civilizations thesis. The overriding argument is that Huntington´s thesis is characterized by failure to distinguish between ´groups´ and ´categories´. Multinational civilizations overcoming their internal collective action problems, which would enable them to pursue a unified strategy vis-à-vis the West, is a rather foundational assumption in his theory. Without assigning sufficient intellectual attention to the processes through which multinational civilizations may gain capacity for concerted action i.e. become a group, he contended that the post-cold-war world would be shaped in large measure by interactions among seven or eight major civilizations. Thus, failure in providing a convincing analysis of multi-national civilizations´ transition from categories to groups is a significant weakness in Huntington´s clash theory. It is also suggested that so-called Islamic terrorism and the war on terror is not to be taken as an expression of presence of clash between a Western and an Islamic civilization, as terrorist organizations would be superfluous in a world characterized by clash of civilizations. Consequences of multinational civilizations becoming a group are discussed in relation to contemporary Western superiority.

681
10009563
Suitability of Class F Flyash for Construction Industry: An Indian Scenario
Abstract:

The present study evaluates the properties of class F fly ash as a replacement of natural materials in civil engineering construction industry. The low-lime flash similar to class F is the prime variety generated in India, although it has significantly smaller volumes of high-lime fly ash as compared to class C. The chemical and physical characterization of the sample is carried out with the number of experimental approaches in order to investigate all relevant features present in the samples. For chemical analysis, elementary quantitative results from point analysis and scanning electron microscopy (SEM)/dispersive spectroscopy (EDS) techniques were used to identify the element images of different fractions. The physical properties found very close to the range of common soils. Furthermore, the fly ash-based bricks were prepared by the same sample of class F fly ash and the results of compressive strength similar to that of Standard Clay Brick Grade 1 available in the local market of India.

680
10009302
Effect of Plastic Fines on Undrained Behavior of Clayey Sands
Abstract:

In recent years, the occurrence of several liquefactions in sandy soils containing various values of clay content has shown that in addition to silty sands, clayey sands are also susceptible to liquefaction. Therefore, it is necessary to investigate the properties of these soil compositions and their behavioral characteristics. This paper presents the effect of clay fines on the undrained shear strength of sands at various confining pressures. For this purpose, a series of unconsolidated undrained triaxial shear tests were carried out on clean sand and sand mixed with 5, 10, 15, 20, and 30 percent of clay fines. It was found that the presence of clay particle in sandy specimens change the dilative behavior to contraction. The result also showed that increasing the clay fines up to 10 percent causes to increase the potential for liquefaction, and decreases it at higher values fine content. These results reveal the important role of clay particles in changing the undrained strength of the sandy soil.

679
10009310
Automated Heart Sound Classification from Unsegmented Phonocardiogram Signals Using Time Frequency Features
Abstract:
Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.
678
10009357
A Conversation about Inclusive Education: Revelations from Namibian Primary School Teachers
Abstract:

Inclusive education stems from a philosophy and vision, which argues that all children should learn together at school. It is not only about treating all pupils in the same way. It is also about allowing all children to attend school without any restrictions. Ten primary school teachers in a circuit in Namibia volunteered to participate in face-to-face interviews about inclusive education. The teachers responded to three questions about their (i) understanding of inclusive education; (ii) whether inclusive education was implemented in primary schools; and (iii) whether they were able to work with learners with special needs. Findings indicated that teachers understood what inclusive education entailed; felt that inclusive education was not implemented in their primary schools, and they were unable to work with learners with special needs in their classrooms. Further, the teachers identified training and resources as important components of inclusive education. It is recommended that education authorities should perhaps verify the findings reported here as well as ensure that the concerns raised by the teachers are addressed.

677
10009269
Meta-Learning for Hierarchical Classification and Applications in Bioinformatics
Abstract:
Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation.
676
10009283
Knowledge Representation and Inconsistency Reasoning of Class Diagram Maintenance in Big Data
Authors:
Abstract:

Requirements modeling and analysis are important in successful information systems' maintenance. Unified Modeling Language (UML) class diagrams are useful standards for modeling information systems. To our best knowledge, there is a lack of a systems development methodology described by the organism metaphor. The core concept of this metaphor is adaptation. Using the knowledge representation and reasoning approach and ontologies to adopt new requirements are emergent in recent years. This paper proposes an organic methodology which is based on constructivism theory. This methodology is a knowledge representation and reasoning approach to analyze new requirements in the class diagrams maintenance. The process and rules in the proposed methodology automatically analyze inconsistencies in the class diagram. In the big data era, developing an automatic tool based on the proposed methodology to analyze large amounts of class diagram data is an important research topic in the future.

675
10009108
A World Map of Seabed Sediment Based on 50 Years of Knowledge
Abstract:

Production of a global sedimentological seabed map has been initiated in 1995 to provide the necessary tool for searches of aircraft and boats lost at sea, to give sedimentary information for nautical charts, and to provide input data for acoustic propagation modelling. This original approach had already been initiated one century ago when the French hydrographic service and the University of Nancy had produced maps of the distribution of marine sediments of the French coasts and then sediment maps of the continental shelves of Europe and North America. The current map of the sediment of oceans presented was initiated with a UNESCO's general map of the deep ocean floor. This map was adapted using a unique sediment classification to present all types of sediments: from beaches to the deep seabed and from glacial deposits to tropical sediments. In order to allow good visualization and to be adapted to the different applications, only the granularity of sediments is represented. The published seabed maps are studied, if they present an interest, the nature of the seabed is extracted from them, the sediment classification is transcribed and the resulted map is integrated in the world map. Data come also from interpretations of Multibeam Echo Sounder (MES) imagery of large hydrographic surveys of deep-ocean. These allow a very high-quality mapping of areas that until then were represented as homogeneous. The third and principal source of data comes from the integration of regional maps produced specifically for this project. These regional maps are carried out using all the bathymetric and sedimentary data of a region. This step makes it possible to produce a regional synthesis map, with the realization of generalizations in the case of over-precise data. 86 regional maps of the Atlantic Ocean, the Mediterranean Sea, and the Indian Ocean have been produced and integrated into the world sedimentary map. This work is permanent and permits a digital version every two years, with the integration of some new maps. This article describes the choices made in terms of sediment classification, the scale of source data and the zonation of the variability of the quality. This map is the final step in a system comprising the Shom Sedimentary Database, enriched by more than one million punctual and surface items of data, and four series of coastal seabed maps at 1:10,000, 1:50,000, 1:200,000 and 1:1,000,000. This step by step approach makes it possible to take into account the progresses in knowledge made in the field of seabed characterization during the last decades. Thus, the arrival of new classification systems for seafloor has improved the recent seabed maps, and the compilation of these new maps with those previously published allows a gradual enrichment of the world sedimentary map. But there is still a lot of work to enhance some regions, which are still based on data acquired more than half a century ago.

674
10009133
The Role of Blended Modality in Enhancing Active Learning Strategies in Higher Education: A Case Study of a Hybrid Course of Oral Production and Listening of French
Abstract:

Learning oral skills in an Arabic speaking environment is challenging. A blended course (material, activities, and individual/ group work tasks …) was implemented in a module of level B1 for undergraduate students of French as a foreign language in order to increase their opportunities to practice listening and speaking skills. This research investigates the influence of this modality on enhancing active learning and examines the effectiveness of provided strategies. Moreover, it aims at discovering how it allows teacher to flip the traditional classroom and create a learner-centered framework. Which approaches were integrated to motivate students and urge them to search, analyze, criticize, create and accomplish projects? What was the perception of students? This paper is based on the qualitative findings of a questionnaire and a focus group interview with learners. Despite the doubled time and effort both “teacher” and “student” needed, results revealed that the NTIC allowed a shift into a learning paradigm where learners were the “chiefs” of the process. Tasks and collaborative projects required higher intellectual capacities from them. Learners appreciated this experience and developed new life-long learning competencies at many levels: social, affective, ethical and cognitive. To conclude, they defined themselves as motivated young researchers, motivators and critical thinkers.

673
10009134
A Survey of Online User Perspectives and Age Profile in an Undergraduate Fundamental Business Technology Course
Abstract:

Over the past few decades, more and more students choose to enroll in online classes instead of attending in-class lectures. While past studies consider students’ attitudes towards online education and how their grades differed from in-class lectures, the profile of the online student remains a blur. To shed light on this, an online survey was administered to about 1,500 students enrolled in an undergraduate Fundamental Business Technology course at a Canadian University. The survey was comprised of questions on students’ demographics, their reasons for choosing online courses, their expectations towards the course, the communication channels they use for the course with fellow students and with the instructor. This paper focused on the research question: Do the perspectives of online students concerning the online experience, in general, and in the course in particular, differ according to age profile? After several statistical analyses, it was found that age does have an impact on the reasons why students select online classes instead of in-class. For example, it was found that the perception that an online course might be easier than in-class delivery was a more important reason for younger students than for older ones. Similarly, the influence of friends is much more important for younger students, than for older students. Similar results were found when analyzing students’ expectation about the online course and their use of communication tools. Overall, the age profile of online users had an impact on reasons, expectations and means of communication in an undergraduate Fundamental Business Technology course. It is left to be seen if this holds true across other courses, graduate and undergraduate.

672
10009165
Classifying and Predicting Efficiencies Using Interval DEA Grid Setting
Abstract:
The classification and the prediction of efficiencies in Data Envelopment Analysis (DEA) is an important issue, especially in large scale problems or when new units frequently enter the under-assessment set. In this paper, we contribute to the subject by proposing a grid structure based on interval segmentations of the range of values for the inputs and outputs. Such intervals combined, define hyper-rectangles that partition the space of the problem. This structure, exploited by Interval DEA models and a dominance relation, acts as a DEA pre-processor, enabling the classification and prediction of efficiency scores, without applying any DEA models.
671
10009169
A Dataset of Program Educational Objectives Mapped to ABET Outcomes: Data Cleansing, Exploratory Data Analysis and Modeling
Abstract:

Datasets or collections are becoming important assets by themselves and now they can be accepted as a primary intellectual output of a research. The quality and usage of the datasets depend mainly on the context under which they have been collected, processed, analyzed, validated, and interpreted. This paper aims to present a collection of program educational objectives mapped to student’s outcomes collected from self-study reports prepared by 32 engineering programs accredited by ABET. The manual mapping (classification) of this data is a notoriously tedious, time consuming process. In addition, it requires experts in the area, which are mostly not available. It has been shown the operational settings under which the collection has been produced. The collection has been cleansed, preprocessed, some features have been selected and preliminary exploratory data analysis has been performed so as to illustrate the properties and usefulness of the collection. At the end, the collection has been benchmarked using nine of the most widely used supervised multiclass classification techniques (Binary Relevance, Label Powerset, Classifier Chains, Pruned Sets, Random k-label sets, Ensemble of Classifier Chains, Ensemble of Pruned Sets, Multi-Label k-Nearest Neighbors and Back-Propagation Multi-Label Learning). The techniques have been compared to each other using five well-known measurements (Accuracy, Hamming Loss, Micro-F, Macro-F, and Macro-F). The Ensemble of Classifier Chains and Ensemble of Pruned Sets have achieved encouraging performance compared to other experimented multi-label classification methods. The Classifier Chains method has shown the worst performance. To recap, the benchmark has achieved promising results by utilizing preliminary exploratory data analysis performed on the collection, proposing new trends for research and providing a baseline for future studies.

670
10009213
Clay Palm Press: A Technique of Hand Building in Ceramics for Developing Conceptual Forms
Abstract:

There are several techniques of production in the field of ceramics. These different techniques overtime have been categorised under three methods of production which includes; casting, throwing and hand building. Hand building method of production is further broken down into other techniques and they include coiling, slabbing and pinching. Ceramic artists find the different hand building techniques to be very interesting, practicable and rewarding. This has encouraged ceramic artist in their various studios at different levels to experiment for further hand building techniques that could be unique and unusual. The art of “Clay Palm Press” is a development from studio experiment in a quest for uniqueness in conceptual ceramic practise. Clay palm press is a technique that requires no formal tutelage but at the same time, it is not easily comprehensible when viewed. It is a practice of putting semi-solid clay in the palm and inserting a closed fist pressure so as to take the imprint of the human palm. This clay production from the palm when dried, fired and explored into an art, work reveals an absolute awesomeness of what the palm imprint could result in.

669
10009086
Mean-Variance Optimization of Portfolios with Return of Premium Clauses in a DC Pension Plan with Multiple Contributors under Constant Elasticity of Variance Model
Abstract:

In this paper, mean-variance optimization of portfolios with the return of premium clauses in a defined contribution (DC) pension plan with multiple contributors under constant elasticity of variance (CEV) model is studied. The return clauses which permit death members to claim their accumulated wealth are considered, the remaining wealth is not equally distributed by the remaining members as in literature. We assume that before investment, the surplus which includes funds of members who died after retirement adds to the total wealth. Next, we consider investments in a risk-free asset and a risky asset to meet up the expected returns of the remaining members and obtain an optimized problem with the help of extended Hamilton Jacobi Bellman equation. We obtained the optimal investment strategies for the two assets and the efficient frontier of the members by using a stochastic optimal control technique. Furthermore, we studied the effect of the various parameters of the optimal investment strategies and the effect of the risk-averse level on the efficient frontier. We observed that the optimal investment strategy is the same as in literature, secondly, we observed that the surplus decreases the proportion of the wealth invested in the risky asset.

668
10009366
Inequalities in Higher Education and Students’ Perceptions of Factors Influencing Academic Performance
Abstract:

This qualitative study aims to answer the following research questions: i) What are the factors that students perceive as relevant to a) promoting and b) preventing good grades? ii) How does socio-economic status (SES) feature in those beliefs? We conducted in-depth interviews with 19 first- and second-year undergraduates of varying SES at a research-intensive university in the UK. The interviews yielded eight factors that students perceived as promoting and six perceived as preventing good grades. The findings suggested one significant difference between the beliefs of low and high SES students in that low SES students perceive themselves to be at a greater disadvantage to their peers while high SES students do not have such beliefs. This could have knock-on effects on their performance.

667
10008750
Surveillance for African Swine Fever and Classical Swine Fever in Benue State, Nigeria
Abstract:
A serosurveillance study was conducted to detect the presence of antibodies to African swine fever virus (ASFV) and Classical swine fever virus in pigs sampled from piggeries and Makurdi central slaughter slab in Benue State, Nigeria. 416 pigs from 74 piggeries across 12 LGAs and 44 pigs at the Makurdi central slaughter slab were sampled for serum. The sera collected were analysed using Indirect Enzyme Linked Immunosorbent Assay (ELISA) test kit to test for antibodies to ASFV, while competitive ELISA test kit was used to test for antibodies to CSFV. Of the 416 pigs from piggeries and 44 pigs sampled from the slaughter slab, seven (1.7%) and six (13.6%), respectively, tested positive to ASFV antibodies and was significantly associated (p < 0.0001). Out of the 12 LGAs sampled, Obi LGA had the highest ASFV antibody detection rate of (4.8%) and was significantly associated (p < 0.0001). None of the samples tested positive to CSFV antibodies. The study concluded that antibodies to CSFV were absent in the sampled pigs in piggeries and at the Makurdi central slaughter slab in Benue State, while antibodies to ASFV were present in both locations; hence, the need to keep an eye open for CSF too since both diseases may pose great risk in the study area. Further studies to characterise the ASFV circulating in Benue State and investigate the possible sources is recommended. Routine surveillance to provide a comprehensive and readily accessible data base to plan for the prevention of any fulminating outbreak is also recommended.
666
10008804
Road Vehicle Recognition Using Magnetic Sensing Feature Extraction and Classification
Abstract:

This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

665
10008680
Performance Assessment of Multi-Level Ensemble for Multi-Class Problems
Abstract:
Many supervised machine learning tasks require decision making across numerous different classes. Multi-class classification has several applications, such as face recognition, text recognition and medical diagnostics. The objective of this article is to analyze an adapted method of Stacking in multi-class problems, which combines ensembles within the ensemble itself. For this purpose, a training similar to Stacking was used, but with three levels, where the final decision-maker (level 2) performs its training by combining outputs from the tree-based pair of meta-classifiers (level 1) from Bayesian families. These are in turn trained by pairs of base classifiers (level 0) of the same family. This strategy seeks to promote diversity among the ensembles forming the meta-classifier level 2. Three performance measures were used: (1) accuracy, (2) area under the ROC curve, and (3) time for three factors: (a) datasets, (b) experiments and (c) levels. To compare the factors, ANOVA three-way test was executed for each performance measure, considering 5 datasets by 25 experiments by 3 levels. A triple interaction between factors was observed only in time. The accuracy and area under the ROC curve presented similar results, showing a double interaction between level and experiment, as well as for the dataset factor. It was concluded that level 2 had an average performance above the other levels and that the proposed method is especially efficient for multi-class problems when compared to binary problems.
664
10008694
Hybrid Reliability-Similarity-Based Approach for Supervised Machine Learning
Authors:
Abstract:

Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

663
10008724
Long Short-Term Memory Based Model for Modeling Nicotine Consumption Using an Electronic Cigarette and Internet of Things Devices
Abstract:

In this paper, we want to determine whether the accurate prediction of nicotine concentration can be obtained by using a network of smart objects and an e-cigarette. The approach consists of, first, the recognition of factors influencing smoking cessation such as physical activity recognition and participant’s behaviors (using both smartphone and smartwatch), then the prediction of the configuration of the e-cigarette (in terms of nicotine concentration, power, and resistance of e-cigarette). The study uses a network of commonly connected objects; a smartwatch, a smartphone, and an e-cigarette transported by the participants during an uncontrolled experiment. The data obtained from sensors carried in the three devices were trained by a Long short-term memory algorithm (LSTM). Results show that our LSTM-based model allows predicting the configuration of the e-cigarette in terms of nicotine concentration, power, and resistance with a root mean square error percentage of 12.9%, 9.15%, and 11.84%, respectively. This study can help to better control consumption of nicotine and offer an intelligent configuration of the e-cigarette to users.

662
10008991
A Silicon Controlled Rectifier-Based ESD Protection Circuit with High Holding Voltage and High Robustness Characteristics
Abstract:
In this paper, a Silicon Controlled Rectifier (SCR)-based Electrostatic Discharge (ESD) protection circuit with high holding voltage and high robustness characteristics is proposed. Unlike conventional SCR, the proposed circuit has low trigger voltage and high holding voltage and provides effective ESD protection with latch-up immunity. In addition, the TCAD simulation results show that the proposed circuit has better electrical characteristics than the conventional SCR. A stack technology was used for voltage-specific applications. Consequentially, the proposed circuit has a trigger voltage of 17.60 V and a holding voltage of 3.64 V.
661
10008539
Evaluating Machine Learning Techniques for Activity Classification in Smart Home Environments
Abstract:
With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.
660
10008834
Comparative Evaluation of Accuracy of Selected Machine Learning Classification Techniques for Diagnosis of Cancer: A Data Mining Approach
Abstract:
With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.
659
10008592
Experimental Determination of Shear Strength Properties of Lightweight Expanded Clay Aggregates Using Direct Shear and Triaxial Tests
Abstract:

Artificial lightweight aggregates have a wide range of applications in industry and engineering. Nowadays, the usage of this material in geotechnical activities, especially as backfill in retaining walls has been growing due to the specific characteristics which make it a competent alternative to the conventional geotechnical materials. In practice, a material with lower weight but higher shear strength parameters would be ideal as backfill behind retaining walls because of the important roles that these parameters play in decreasing the overall active lateral earth pressure. In this study, two types of Light Expanded Clay Aggregates (LECA) produced in the Leca factory are investigated. LECA is made in a rotary kiln by heating natural clay at different temperatures up to 1200 °C making quasi-spherical aggregates with different sizes ranged from 0 to 25 mm. The loose bulk density of these aggregates is between 300 and 700 kN/m3. The purpose of this research is to determine the stress-strain behavior, shear strength parameters, and the energy absorption of LECA materials. Direct shear tests were conducted at five normal stresses of 25, 50, 75, 100, and 200 kPa. In addition, conventional triaxial compression tests were operated at confining pressures of 50, 100, and 200 kPa to examine stress-strain behavior. The experimental results show a high internal angle of friction and even a considerable amount of nominal cohesion despite the granular structure of LECA. These desirable properties along with the intrinsic low density of these aggregates make LECA as a very proper material in geotechnical applications. Furthermore, the results demonstrate that lightweight aggregates may have high energy absorption that is excellent alternative material in seismic isolations.

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