Search results for: feature selection methods

Authors: Sophia Hayes, Kenny L. Liang, Sahil Sharma, Austin Shema, Mahmoud Bader, Mohamed Elbarkouky

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Despite being one of the most significant economic contributors of the country, Canada’s construction industry is lagging behind other sectors when it comes to labor productivity improvements. The construction industry is very collaborative as a general contractor, will hire trade contractors to perform most of a project’s work; meaning low productivity from one contractor can have a domino effect on the shared success of a project. To address this issue and encourage trade contractors to improve their productivity tracking methods, an investigative study was done on the productivity views and tracking methods of various trade contractors. Additionally, an in-depth review was done on four standard tracking methods used in the construction industry: cost codes, benchmarking, the job productivity measurement (JPM) standard, and WorkFace Planning (WFP). The four tracking methods were used as a baseline in comparing the trade contractors’ responses, determining gaps within their current tracking methods, and for making improvement recommendations. 15 interviews were conducted with different trades to analyze how contractors value productivity. The results of these analyses indicated that there seem to be gaps within the construction industry when it comes to an understanding of the purpose and value in productivity tracking. The trade contractors also shared their current productivity tracking systems; which were then compared to the four standard tracking methods used in the construction industry. Gaps were identified in their various tracking methods and using a framework; recommendations were made based on the type of trade on how to improve how they track productivity.

Keywords: labor productivity, productivity tracking methods, trade contractors, construction

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

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Authors: Sajjad Ali

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The Quranic pedagogical methods don't imply that the Quran explicitly prescribes teaching methods. Instead, it acknowledges the inherent ways of learning and teaching that align with human nature, offering guidance in this direction. Qur'an briefly describes different angles of acquiring knowledge. Narrative, interrogative, question, analytical, poetic, comparative and critical methods of teaching are briefly described in the Holy Quran. The Muslim Ummah has a firm belief that the Qur'an is a comprehensive book which mentions every dry and wet, but this does not mean that the Qur'an is a manual book. This means that the Qur'an contains symbols and hints about everything. The fact that everything is mentioned in the Qur'an means that the Qur'an only provides guidance, while its interpretation requires contemplation.

Keywords: hadith, knowledge, reality, understanding

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Authors: Vramori Mitra, Bornali Sarma, Arun K. Sarma

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Recurrence is a ubiquitous feature of any real dynamical system. The states in phase space trajectory of a system have an inherent tendency to return to the same state or its close state after certain time laps. Recurrence quantification analysis technique, based on this fundamental feature of a dynamical system, detects evaluation of state under variation of control parameter of the system. The paper presents the investigation of nonlinear dynamical behavior of plasma floating potential fluctuations obtained by using a Langmuir probe in different magnetic field under the variation of discharge voltages. The main measures of recurrence quantification analysis are considered as determinism, linemax and entropy. The increment of the DET and linemax variables asserts that the predictability and periodicity of the system is increasing. The variable linemax indicates that the chaoticity is being diminished with the slump of magnetic field while increase of magnetic field enhancing the chaotic behavior. Fractal property of the plasma time series estimated by DFA technique (Detrended fluctuation analysis) reflects that long-range correlation of plasma fluctuations is decreasing while fractal dimension is increasing with the enhancement of magnetic field which corroborates the RQA analysis.

Keywords: detrended fluctuation analysis, chaos, phase space, recurrence

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Authors: Zhengkun Yi

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Surface texture discrimination using artificial tactile sensors has attracted increasing attentions in the past decade as it can endow technical and robot systems with a key missing ability. However, as a major component of texture, roughness has rarely been explored. This paper presents an approach for tactile surface roughness discrimination, which includes two parts: (1) design and fabrication of a bioinspired artificial fingertip, and (2) tactile signal processing for tactile surface roughness discrimination. The bioinspired fingertip is comprised of two polydimethylsiloxane (PDMS) layers, a polymethyl methacrylate (PMMA) bar, and two perpendicular polyvinylidene difluoride (PVDF) film sensors. This artificial fingertip mimics human fingertips in three aspects: (1) Elastic properties of epidermis and dermis in human skin are replicated by the two PDMS layers with different stiffness, (2) The PMMA bar serves the role analogous to that of a bone, and (3) PVDF film sensors emulate Meissner’s corpuscles in terms of both location and response to the vibratory stimuli. Various extracted features and classification algorithms including support vector machines (SVM) and k-nearest neighbors (kNN) are examined for tactile surface roughness discrimination. Eight standard rough surfaces with roughness values (Ra) of 50 μm, 25 μm, 12.5 μm, 6.3 μm 3.2 μm, 1.6 μm, 0.8 μm, and 0.4 μm are explored. The highest classification accuracy of (82.6 ± 10.8) % can be achieved using solely one PVDF film sensor with kNN (k = 9) classifier and the standard deviation feature.

Keywords: bioinspired fingertip, classifier, feature extraction, roughness discrimination

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Authors: Tigabu Dagne Akal

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Electronic health records (EHRs) can help to store, maintain, and make the appropriate handling of patient histories for proper treatment and decision. Merging the EHRs with big data analytics (BDA) capabilities enable healthcare stakeholders to provide effective and efficient treatments for chronic diseases. Though there are huge opportunities and efforts that exist in the deployment of EMRs and the development of BDA, there are challenges in addressing resources and organizational capabilities that are required to achieve the competitive advantage and sustainability of EHRs and BDA. The resource-based view (RBV), information system (IS), and non- IS theories should be extended to examine organizational capabilities and resources which are required for successful data analytics in the healthcare industries. The main purpose of this study is to develop a conceptual framework for the development of healthcare BDA capabilities based on past works so that researchers can extend. The research question was formulated for the search strategy as a research methodology. The study selection was made at the end. Based on the study selection, the conceptual framework for the development of BDA capabilities in the healthcare settings was formulated.

Keywords: EHR, EMR, Big data, Big data analytics, resource-based view

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Authors: M. V. A. Raju Bahubalendruni, Bibhuti Bhusan Biswal, B. B. V. L. Deepak

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Achieving an appropriate assembly sequence needs deep verification for its physical feasibility. For this purpose, industrial engineers use several assembly predicates; namely, liaison, geometric feasibility, stability and mechanical feasibility. However, testing an assembly sequence for these predicates requires huge assembly information. Extracting such assembly information from an assembled product is a time consuming and highly skillful task with complex reasoning methods. In this paper, computer aided methods are proposed to extract all the necessary assembly information from computer aided design (CAD) environment in order to perform the assembly sequence planning efficiently. These methods use preliminary capabilities of three-dimensional solid modelling and assembly modelling methods used in CAD software considering equilibrium laws of physical bodies.

Keywords: assembly automation, assembly attributes, assembly, CAD

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Authors: Ahmet Karagoz, Irfan Karagoz

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Synthetic Aperture Radar (SAR) is a radar mechanism that can be integrated into manned and unmanned aerial vehicles to create high-resolution images in all weather conditions, regardless of day and night. In this study, SAR images of military vehicles with different azimuth and descent angles are pre-processed at the first stage. The main purpose here is to reduce the high speckle noise found in SAR images. For this, the Wiener adaptive filter, the mean filter, and the median filters are used to reduce the amount of speckle noise in the images without causing loss of data. During the image segmentation phase, pixel values are ordered so that the target vehicle region is separated from other regions containing unnecessary information. The target image is parsed with the brightest 20% pixel value of 255 and the other pixel values of 0. In addition, by using appropriate parameters of statistical region merging algorithm, segmentation comparison is performed. In the step of feature extraction, the feature vectors belonging to the vehicles are obtained by using Gabor filters with different orientation, frequency and angle values. A number of Gabor filters are created by changing the orientation, frequency and angle parameters of the Gabor filters to extract important features of the images that form the distinctive parts. Finally, images are classified by sparse representation method. In the study, l₁ norm analysis of sparse representation is used. A joint database of the feature vectors generated by the target images of military vehicle types is obtained side by side and this database is transformed into the matrix form. In order to classify the vehicles in a similar way, the test images of each vehicle is converted to the vector form and l₁ norm analysis of the sparse representation method is applied through the existing database matrix form. As a result, correct recognition has been performed by matching the target images of military vehicles with the test images by means of the sparse representation method. 97% classification success of SAR images of different military vehicle types is obtained.

Keywords: automatic target recognition, sparse representation, image classification, SAR images

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Authors: Kalangi Rodrigo

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The term Neuroecology elucidates the study of customizable variation in cognition and the brain. Subject marked the birth since 1980s, when researches began to apply methods of comparative evolutionary biology to cognitive processes and the underlying neural mechanisms of cognition. In Archaeology and Anthropology, we observe behaviors such as social learning skills, innovative feeding and foraging, tool use and social manipulation to determine the cognitive processes of ancient mankind. Depending on the brainstem size was used as a control variable, and phylogeny was controlled using independent contrasts. Both disciplines need to enriched with comparative literature and neurological experimental, behavioral studies among tribal peoples as well as primate groups which will lead the research to a potential end. Neuroecology examines the relations between ecological selection pressure and mankind or sex differences in cognition and the brain. The goal of neuroecology is to understand how natural law acts on perception and its neural apparatus. Furthermore, neuroecology will eventually lead both principal disciplines to Ethology, where human behaviors and social management studies from a biological perspective. It can be either ethnoarchaeological or prehistoric. Archaeology should adopt general approach of neuroecology, phylogenetic comparative methods can be used in the field, and new findings on the cognitive mechanisms and brain structures involved mating systems, social organization, communication and foraging. The contribution of neuroecology to archaeology and anthropology is the information it provides on the selective pressures that have influenced the evolution of cognition and brain structure of the mankind. It will shed a new light to the path of evolutionary studies including behavioral ecology, primate archaeology and cognitive archaeology.

Keywords: Neuroecology, Archaeology, Brain Evolution, Cognitive Archaeology

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Authors: Muzaffar Shaikh, Shoaib Shaikh, Mark Moyou, Gaby Hawat

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This paper presents the use of Analytic Hierarchy Process (AHP) in evaluating the site selection of a new plant by a corporation. Due to intense competition at a global level, multinational corporations are continuously striving to minimize production and shipping costs of their products. One key factor that plays significant role in cost minimization is where the production plant is located. In the U.S. for example, labor and land costs continue to be very high while they are much cheaper in countries such as India, China, Indonesia, etc. This is why many multinational U.S. corporations (e.g. General Electric, Caterpillar Inc., Ford, General Motors, etc.), have shifted their manufacturing plants outside. The continued expansion of the Internet and its availability along with technological advances in computer hardware and software all around the globe have facilitated U.S. corporations to expand abroad as they seek to reduce production cost. In particular, management of multinational corporations is constantly engaged in concentrating on countries at a broad level, or cities within specific countries where certain or all parts of their end products or the end products themselves can be manufactured cheaper than in the U.S. AHP is based on preference ratings of a specific decision maker who can be the Chief Operating Officer of a company or his/her designated data analytics engineer. It serves as a tool to first evaluate the plant site selection criteria and second, alternate plant sites themselves against these criteria in a systematic manner. Examples of site selection criteria are: Transportation Modes, Taxes, Energy Modes, Labor Force Availability, Labor Rates, Raw Material Availability, Political Stability, Land Costs, etc. As a necessary first step under AHP, evaluation criteria and alternate plant site countries are identified. Depending upon the fidelity of analysis, specific cities within a country can also be chosen as alternative facility locations. AHP experience in this type of analysis indicates that the initial analysis can be performed at the Country-level. Once a specific country is chosen via AHP, secondary analyses can be performed by selecting specific cities or counties within a country. AHP analysis is usually based on preferred ratings of a decision-maker (e.g., 1 to 5, 1 to 7, or 1 to 9, etc., where 1 means least preferred and a 5 means most preferred). The decision-maker assigns preferred ratings first, criterion vs. criterion and creates a Criteria Matrix. Next, he/she assigns preference ratings by alternative vs. alternative against each criterion. Once this data is collected, AHP is applied to first get the rank-ordering of criteria. Next, rank-ordering of alternatives is done against each criterion resulting in an Alternative Matrix. Finally, overall rank ordering of alternative facility locations is obtained by matrix multiplication of Alternative Matrix and Criteria Matrix. The most practical aspect of AHP is the ‘what if’ analysis that the decision-maker can conduct after the initial results to provide valuable sensitivity information of specific criteria to other criteria and alternatives.

Keywords: analytic hierarchy process, multinational corporations, plant site selection, preference ratings

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Authors: Rujia Chen, Ajit Narayanan

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Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

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Authors: Rachelle McFarlane

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The Second Decade of Action Road Safety has begun with increased focus on countries who are disproportionately affected by road fatalities. Researchers highlight the low effectiveness of road safety campaigns in Latin America and the Caribbean (LAC) still reporting approximately 130,000 deaths and six million injuries annually. The regional fatality rate 19.2 per 100,000 with heightened concern for persons 15 to 44 years. In 2021, 483 Jamaicans died in 435 crashes, with 33% of these fatalities occurring during Covid-19 curfew hours. The study objective is to conduct a systemic safety review of Jamaican road crashes and provide a framework for its use in complementing traditional methods. The methodology involves the use of the FHWA Systemic Safety Project Selection Tool for analysis. This tool reviews systemwide data in order to identify risk factors across the network associated with severe and fatal crashes, rather that only hotspots. A total of 10,379 crashes with 745 fatalities and serious injuries were reviewed. Of the focus crash types listed, 50% of ‘Pedestrian Accidents’ resulted in fatalities and serious injuries, followed by 32% ‘Bicycle’, 24% ‘Single’ and 12% of ‘Head-on’. This study seeks to understand the associated risk factors with these priority crash types across the network and recommend cost-effective countermeasures across common sites. As we press towards Vision Zero, the inclusion of the systemic safety review method, complementing traditional methods, may create a wider impact in reducing road fatalities and serious injury by targeting issues across network with similarities; focus crash types and contributing factors.

Keywords: systemic safety review, risk factors, road crashes, crash types

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Authors: Stepan Papacek, Jiri Jablonsky, Radek Kana, Ctirad Matonoha, Stefan Kindermann

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FRAP (Fluorescence Recovery After Photobleaching) is a widely used measurement technique to determine the mobility of fluorescent molecules within living cells. While the experimental setup and protocol for FRAP experiments are usually fixed, data processing part is still under development. In this paper, we formulate and solve the problem of data selection which enhances the processing of FRAP images. We introduce the concept of the irrelevant data set, i.e., the data which are almost not reducing the confidence interval of the estimated parameters and thus could be neglected. Based on sensitivity analysis, we both solve the problem of the optimal data space selection and we find specific conditions for optimizing an important experimental design factor, e.g., the radius of bleach spot. Finally, a theorem announcing less precision of the integrated data approach compared to the full data case is proven; i.e., we claim that the data set represented by the FRAP recovery curve lead to a larger confidence interval compared to the spatio-temporal (full) data.

Keywords: FRAP, inverse problem, parameter identification, sensitivity analysis, optimal experimental design

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Authors: Priya Arora, Ashutosh Mishra

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Gene identification, towards the pursuit of mutated genes, leading to Parkinson’s disease, puts forward a challenge towards proactive cure of the disorder itself. Computational analysis is an effective technique for exploring genes in the form of protein sequences, as the theoretical and manual analysis is infeasible. The limitations and effectiveness of a particular computational method are entirely dependent on the previous data that is available for disease identification. The article presents a sequence-based classification method for the identification of genes responsible for Parkinson’s disease. During the initiation phase, the physicochemical properties of amino acids transform protein sequences into a feature vector. The second phase of the method employs Jaccard distances to select negative genes from the candidate population. The third phase involves artificial neural networks for making final predictions. The proposed approach is compared with the state of art methods on the basis of F-measure. The results confirm and estimate the efficiency of the method.

Keywords: disease gene identification, Parkinson’s disease, physicochemical properties of amino acid, protein sequences

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Authors: Naci Büyükkaracığan

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Today, the need for water sources is swiftly increasing due to population growth. At the same time, it is known that some regions will face with shortage of water and drought because of the global warming and climate change. In this context, evaluation and analysis of hydrological data such as the observed trends, drought and flood prediction of short term flow has great deal of importance. The most accurate selection probability distribution is important to describe the low flow statistics for the studies related to drought analysis. As in many basins In Turkey, Gediz River basin will be affected enough by the drought and will decrease the amount of used water. The aim of this study is to derive appropriate probability distributions for frequency analysis of annual minimum flows at 6 gauging stations of the Gediz Basin. After applying 10 different probability distributions, six different parameter estimation methods and 3 fitness test, the Pearson 3 distribution and general extreme values distributions were found to give optimal results.

Keywords: Gediz Basin, goodness-of-fit tests, minimum flows, probability distribution

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Authors: Aleksandra Zysk, Pawel Badura

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Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: classification, singing, spectral analysis, vocal emission, vocal register

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Authors: Salim Omar Raoof

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This study was conducted in a private local sheep herd at Gabaraka village-Kirkuk-Iraq. Analysis of 77 ewes recorded and 7 Rams of local sheep presented in Gabaraka village farm plain, the age of ewes ranged between (2-4) years. The aim of this study is to investigate the genetic and non-genetic factors (type of birth, sex, and age of dam) affecting daily milk yield (DMY), birth weight (BW), weaning weight (WW) and Gain characteristics of local sheep raised under Iraq conditions, and it also aims at estimating heritability’s, BLUP. The overall mean of daily milk yield, (BW), (WW), and gain. Was 444.15gm,4.92kg,43.08kg, and 38.16kg, respectively. The results showed there was a significant effect of the type of birth and sex on (BW) and (WW). Also, the age of the dam had a significant effect on daily milk yield (BW), (WW), and gain. Generally, the estimate of heritability of DMP, BWT, WWT, and Gain tend to be 0.22, 0.17, 0.27, and 0.22, respectively. The breeding value (BLUP) for rams ranged between (-0.1684 to 0.188), (-0.205 to 0.310), and ( -0.0171 to 0.029) according to growth traits of Lambs BW, WW, and Gain, respectively. It concluded that the selection of ewes and rams at the population level in planned selection schemes is based on BLUP value and heritability.

Keywords: locally sheep, milk yield, Genetic parameters, BLUP value

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Authors: Alexander Hudson, Shaogang Gong

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Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Keywords: transfer learning, protein distance maps, protein structure classification, neural networks

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Authors: Robert Cazar, Nathaly Maza

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Energy drinks and sports drinks are quite popular among young adults and teenagers worldwide. Some concerns regarding their health effects – particularly those of the energy drinks - have been raised based on scientific findings. Differentiating between these two types of drinks by means of their chemical properties seems to be an instructive task. Chemometrics provides the most appropriate strategy to do so. In this study, a discrimination analysis of the energy and sports drinks has been carried out applying chemometric methods. A set of eleven samples of available commercial brands of drinks – seven energy drinks and four sports drinks – were collected. Each sample was characterized by eight chemical variables (carbohydrates, energy, sugar, sodium, pH, degrees Brix, density, and citric acid). The data set was standardized and examined by exploratory chemometric techniques such as clustering and principal component analysis. As a preliminary step, a variable selection was carried out by inspecting the variable correlation matrix. It was detected that some variables are redundant, so they can be safely removed, leaving only five variables that are sufficient for this analysis. They are sugar, sodium, pH, density, and citric acid. Then, a hierarchical clustering `employing the average – linkage criterion and using the Euclidian distance metrics was performed. It perfectly separates the two types of drinks since the resultant dendogram, cut at the 25% similarity level, assorts the samples in two well defined groups, one of them containing the energy drinks and the other one the sports drinks. Further assurance of the complete discrimination is provided by the principal component analysis. The projection of the data set on the first two principal components – which retain the 71% of the data information – permits to visualize the distribution of the samples in the two groups identified in the clustering stage. Since the first principal component is the discriminating one, the inspection of its loadings consents to characterize such groups. The energy drinks group possesses medium to high values of density, citric acid, and sugar. The sports drinks group, on the other hand, exhibits low values of those variables. In conclusion, the application of chemometric methods on a data set that features some chemical properties of a number of energy and sports drinks provides an accurate, dependable way to discriminate between these two types of beverages.

Keywords: chemometrics, clustering, energy drinks, principal component analysis, sports drinks

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Authors: Dechrit Maneetham

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The aim of this paper was to design a smart electric power wheelchair (SEPW) with a novel control system for quadriplegics with head and neck mobility. Head movement has been used as a control interface for people with motor impairments in a range of applications. Acquiring measurements from the module is simplified through a synchronous a motor. Axis measures the two directions namely X ,Y and Z. The model of a DC motor is considered as a speed control by selection of a PID parameters using genetic algorithm. An experimental set-up constructed, which consists of micro controller Arduino ATmega32u4 as controllers, a DC motor driven SEPW and feedback elements. And this paper is tuning methods of parameter for a pulse width modulation (PWM) control system. A speed controller has been designed successfully for closed loop of the DC motor so that the motor runs very closed to the reference speed and angle. SEPW controller can be used to ensure the person’s head is attending the direction of travel asserted by a conventional, direction and speed control.

Keywords: wheelchair, quadriplegia, rehabilitation, medical devices, speed control

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Authors: Richard Krijnen, Alan Wang

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As the world is aiming to reach net zero around 2050, structural engineers must begin finding solutions to contribute to this global initiative. Approximately 40% of global energy-related emissions are due to buildings and construction, and a building’s structure accounts for 50% of its embodied carbon, which indicates that structural engineers are key contributors to finding solutions to reach carbon neutrality. However, this task presents a multifaceted challenge as structural engineers must navigate technical, safety and economic considerations while striving to reduce emissions. This study reviews several options and considerations to reduce carbon emissions that structural engineers can use in their future designs without compromising the structural integrity of their proposed design. Low-carbon structures should adhere to several guiding principles. Firstly, prioritize the selection of materials with low carbon footprints, such as recyclable or alternative materials. Optimization of design and engineering methods is crucial to minimize material usage. Encouraging the use of recyclable and renewable materials reduces dependency on natural resources. Energy efficiency is another key consideration involving the design of structures to minimize energy consumption across various systems. Choosing local materials and minimizing transportation distances help in reducing carbon emissions during transport. Innovation, such as pre-fabrication and modular design or low-carbon concrete, can further cut down carbon emissions during manufacturing and construction. Collaboration among stakeholders and sharing experiences and resources are essential for advancing the development and application of low-carbon structures. This paper identifies current available tools and solutions to reduce embodied carbon in structures, which can be used as part of daily structural engineering practice.

Keywords: efficient structural design, embodied carbon, low-carbon material, sustainable structural design

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Authors: Yash Bingi, Yiqiao Yin

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Reduction of child mortality is an ongoing struggle and a commonly used factor in determining progress in the medical field. The under-5 mortality number is around 5 million around the world, with many of the deaths being preventable. In light of this issue, Cardiotocograms (CTGs) have emerged as a leading tool to determine fetal health. By using ultrasound pulses and reading the responses, CTGs help healthcare professionals assess the overall health of the fetus to determine the risk of child mortality. However, interpreting the results of the CTGs is time-consuming and inefficient, especially in underdeveloped areas where an expert obstetrician is hard to come by. Using a support vector machine (SVM) and oversampling, this paper proposed a model that classifies fetal health with an accuracy of 99.59%. To further explain the CTG measurements, an algorithm based on Randomized Input Sampling for Explanation ((RISE) of Black-box Models was created, called Feature Alteration for explanation of Black Box Models (FAB), and compared the findings to Shapley Additive Explanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME). This allows doctors and medical professionals to classify fetal health with high accuracy and determine which features were most influential in the process.

Keywords: machine learning, fetal health, gradient boosting, support vector machine, Shapley values, local interpretable model agnostic explanations

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Authors: Sam Teek Ling, Norhashidah Hj. Mohd. Ali

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We investigate the formulation and implementation of new explicit group iterative methods in solving the two-dimensional Poisson equation with Dirichlet boundary conditions. The methods are derived from a fourth order compact nine point finite difference discretization. The methods are compared with the existing second order standard five point formula to show the dramatic improvement in computed accuracy. Numerical experiments are presented to illustrate the effectiveness of the proposed methods.

Keywords: explicit group iterative method, finite difference, fourth order compact, Poisson equation

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Authors: H. K. Shivanand, Ranjith R. Hombal, Paraveej Shirahatti, Gujjalla Anil Babu, S. ShivaPrakash

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When it comes to the selection of materials the knowledge of materials science plays a vital role in selection and enhancements of materials properties. In the world of material science a composite material has the significant role based on its application. The composite materials are those in which two or more components having different physical and chemical properties are combined to create a new enhanced property substance. In this study three different materials (Kenaf, Kevlar and Graphene) been chosen based on their properties and a composite material is developed with help of vacuum bagging process. The fibers (Kenaf and Kevlar) and Resin(vinyl ester) ratio was maintained at 70:30 during the process and 0.5% 1% and 1.5% of Graphene was added during fabrication process. The material was machined to thedimension ofASTM standards(300×300mm and thickness 3mm)with help of water jet cutting machine. The composite materials were tested for Mechanical properties such as Interlaminar shear strength(ILSS) and Flexural strength. It is found that there is significant increase in material properties in the developed composite material.

Keywords: Kevlar, Kenaf, graphene, vacuum bagging process, Interlaminar shear strength test, flexural test

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Authors: Haonan Hu, Shuge Lei, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Jijun Tang

Abstract:

This paper focuses on the classification task of breast ultrasound images and conducts research on the reliability measurement of classification results. A dual-channel evaluation framework was developed based on the proposed inference reliability and predictive reliability scores. For the inference reliability evaluation, human-aligned and doctor-agreed inference rationals based on the improved feature attribution algorithm SP-RISA are gracefully applied. Uncertainty quantification is used to evaluate the predictive reliability via the test time enhancement. The effectiveness of this reliability evaluation framework has been verified on the breast ultrasound clinical dataset YBUS, and its robustness is verified on the public dataset BUSI. The expected calibration errors on both datasets are significantly lower than traditional evaluation methods, which proves the effectiveness of the proposed reliability measurement.

Keywords: medical imaging, ultrasound imaging, XAI, uncertainty measurement, trustworthy AI

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Authors: Phuc D. Nguyen, Kristy L. Hansen, Branko Zajamsek

Abstract:

The existence of special characteristics of wind farm noise such as amplitude modulation (AM) contributes significantly to annoyance, which could ultimately result in sleep disturbance and other adverse health effects for residents living near wind farms. In order to detect and quantify this phenomenon, several methods have been developed which can be separated into three types: time-domain, frequency-domain and hybrid methods. However, due to a lack of systematic validation of these methods, it is still difficult to select the best method for identifying AM. Furthermore, previous comparisons between AM methods have been predominantly qualitative or based on synthesised signals, which are not representative of the actual noise. In this study, a comparison between methods for detecting and quantifying AM has been carried out. The results are based on analysis of real noise data which were measured at a wind farm in South Australia. In order to evaluate the performance of these methods in terms of detecting AM, an approach has been developed to select the most successful method of AM detection. This approach uses a receiver operating characteristic (ROC) curve which is based on detection of AM in audio files by experts.

Keywords: amplitude modulation, wind farm noise, ROC curve

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Authors: D. I. George Amalarethinam, A. Emima

Abstract:

Educational Data Mining (EDM) is one of the newest topics to emerge in recent years, and it is concerned with developing methods for analyzing various types of data gathered from the educational circle. EDM methods and techniques with machine learning algorithms are used to extract meaningful and usable information from huge databases. For scientists and researchers, realistic applications of Machine Learning in the EDM sectors offer new frontiers and present new problems. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed to forecast students' performance, which aids the tutor, institution to boost the level of student’s performance. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.

Keywords: classification technique, data mining, EDM methods, prediction methods

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Authors: Riccardo Zanni, Maria Galvez-Llompart, Ramon Garcia-Domenech, Jorge Galvez

Abstract:

Biopesticides, such as naturally occurring chemicals, pheromones, fungi, bacteria and insect predators are often a winning choice in crop protection because of their environmental friendly profile. They are considered to have lower toxicity than traditional pesticides. After almost a century of pesticides use, resistances to traditional insecticides are wide spread, while those to bioinsecticides have raised less attention, and resistance management is frequently neglected. This seems to be a crucial mistake since resistances have already occurred for many marketed biopesticides. With an eye to the future, we present here a selection of new natural occurring chemicals as potential bioinsecticides. The molecules were selected using a consolidated mathematical paradigm called molecular topology. Several QSAR equations were depicted and subsequently applied for the virtual screening of hundred thousands molecules of natural origin, which resulted in the selection of new potential bioinsecticides. The most innovative aspect of this work does not only reside in the importance of the identification of new molecules overcoming biopesticides’ resistances, but on the possibility to promote shared knowledge in the field of green chemistry through this unique in silico discipline named molecular topology.

Keywords: green chemistry, QSAR, molecular topology, biopesticide

Procedia PDF Downloads 301

Authors: Aliaksandr Huminski

Abstract:

Semantic Role Labeling (SRL) as shallow semantic parsing approach includes recognition and labeling arguments of a verb in a sentence. Verb participants are linked with specific semantic roles (Agent, Patient, Instrument, Location, etc.). Thus, SRL can answer on key questions such as ‘Who’, ‘When’, ‘What’, ‘Where’ in a text and it is widely applied in dialog systems, question-answering, named entity recognition, information retrieval, and other fields of NLP. However, SRL has the following flaw: Two sentences with identical (or almost identical) meaning can have different semantic role structures. Let consider 2 sentences: (1) John put butter on the bread. (2) John buttered the bread. SRL for (1) and (2) will be significantly different. For the verb put in (1) it is [Agent + Patient + Goal], but for the verb butter in (2) it is [Agent + Goal]. It happens because of one of the most interesting and intriguing features of a verb: Its ability to capture participants as in the case of the verb butter, or their features as, say, in the case of the verb drink where the participant’s feature being liquid is shared with the verb. This capture looks like a total fusion of meaning and cannot be decomposed in direct way (in comparison with compound verbs like babysit or breastfeed). From this perspective, SRL looks really shallow to represent semantic structure. If the key point in semantic representation is an opportunity to use it for making inferences and finding hidden reasons, it assumes by default that two different but semantically identical sentences must have the same semantic structure. Otherwise we will have different inferences from the same meaning. To overcome the above-mentioned flaw, the following approach is suggested. Assume that: P is a participant of relation; F is a feature of a participant; Vcp is a verb that captures a participant; Vcf is a verb that captures a feature of a participant; Vpr is a primitive verb or a verb that does not capture any participant and represents only a relation. In another word, a primitive verb is a verb whose meaning does not include meanings from its surroundings. Then Vcp and Vcf can be decomposed as: Vcp = Vpr +P; Vcf = Vpr +F. If all Vcp and Vcf will be represented this way, then primitive verbs Vpr can be considered as a canonical form for SRL. As a result of that, there will be no hidden participants caught by a verb since all participants will be explicitly unfolded. An obvious example of Vpr is the verb go, which represents pure movement. In this case the verb drink can be represented as man-made movement of liquid into specific direction. Extraction and using primitive verbs for SRL create a canonical representation unique for semantically identical sentences. It leads to the unification of semantic representation. In this case, the critical flaw related to SRL will be resolved.

Keywords: decomposition, labeling, primitive verbs, semantic roles

Procedia PDF Downloads 354