Search results for: classification tree

Authors: Rattanathip Rattanachai, Kunyanuth Kularbphettong

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This paper describes the development of a Vegetation Searching System based on Web Application in case of Suan Sunandha Rajabhat University. The model was developed by PHP, JavaScript, and MySQL database system and it was designed to support searching endemic and rare species of tree on web site. We describe the design methods and functional components of this prototype. To evaluate the system performance, questionnaires for system usability and Black Box Testing were used to measure expert and user satisfaction. The results were satisfactory as followed: Means for experts and users were 4.3 and 4.5, and standard deviation for experts and users were 0.61 and 0.73 respectively. Further analysis showed that the quality of plant searching web site was also at a good level as well.

Keywords: endemic species, vegetation, web-based system, black box testing, Thailand

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Authors: M. Govindarajan

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One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

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Authors: Parisa Mansour

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Cystic fibrosis (CF) is one of the most common autosomal recessive diseases among whites. It mostly affects the lungs, causing infections and inflammation that account for 90% of deaths in CF patients. Because of this high variability in clinical presentation and organ involvement, investigating treatment responses and evaluating lung changes over time is critical to preventing CF progression. High-resolution computed tomography (HRCT) greatly facilitates the assessment of lung disease progression in CF patients. Recently, artificial intelligence was used to analyze chest CT scans of CF patients. In this paper, we propose a convolutional neural network (CNN) approach to classify CF lung patterns in HRCT images. The proposed network consists of two convolutional layers with 3 × 3 kernels and maximally connected in each layer, followed by two dense layers with 1024 and 10 neurons, respectively. The softmax layer prepares a predicted output probability distribution between classes. This layer has three exits corresponding to the categories of normal (healthy), bronchitis and inflammation. To train and evaluate the network, we constructed a patch-based dataset extracted from more than 1100 lung HRCT slices obtained from 45 CF patients. Comparative evaluation showed the effectiveness of the proposed CNN compared to its close peers. Classification accuracy, average sensitivity and specificity of 93.64%, 93.47% and 96.61% were achieved, indicating the potential of CNNs in analyzing lung CF patterns and monitoring lung health. In addition, the visual features extracted by our proposed method can be useful for automatic measurement and finally evaluation of the severity of CF patterns in lung HRCT images.

Keywords: HRCT, CF, cystic fibrosis, chest CT, artificial intelligence

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Authors: Jeremy Jenrette, Z. Y. C. Liu, Pranav Chimote, Edward Fox, Trevor Hastie, Francesco Ferretti

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Suitable shark conservation depends on well-informed population assessments. Direct methods such as scientific surveys and fisheries monitoring are adequate for defining population statuses, but species-specific indices of abundance and distribution coming from these sources are rare for most shark species. We can rapidly fill these information gaps by boosting media-based remote monitoring efforts with machine learning and automation. We created a database of shark images by sourcing 24,546 images covering 219 species of sharks from the web application spark pulse and the social network Instagram. We used object detection to extract shark features and inflate this database to 53,345 images. We packaged object-detection and image classification models into a Shark Detector bundle. We developed the Shark Detector to recognize and classify sharks from videos and images using transfer learning and convolutional neural networks (CNNs). We applied these models to common data-generation approaches of sharks: boosting training datasets, processing baited remote camera footage and online videos, and data-mining Instagram. We examined the accuracy of each model and tested genus and species prediction correctness as a result of training data quantity. The Shark Detector located sharks in baited remote footage and YouTube videos with an average accuracy of 89\%, and classified located subjects to the species level with 69\% accuracy (n =\ eight species). The Shark Detector sorted heterogeneous datasets of images sourced from Instagram with 91\% accuracy and classified species with 70\% accuracy (n =\ 17 species). Data-mining Instagram can inflate training datasets and increase the Shark Detector’s accuracy as well as facilitate archiving of historical and novel shark observations. Base accuracy of genus prediction was 68\% across 25 genera. The average base accuracy of species prediction within each genus class was 85\%. The Shark Detector can classify 45 species. All data-generation methods were processed without manual interaction. As media-based remote monitoring strives to dominate methods for observing sharks in nature, we developed an open-source Shark Detector to facilitate common identification applications. Prediction accuracy of the software pipeline increases as more images are added to the training dataset. We provide public access to the software on our GitHub page.

Keywords: classification, data mining, Instagram, remote monitoring, sharks

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Authors: G. Martino, F. Silva, E. Marchal

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The control over delivered iron ore blend characteristics is one of the most important aspects of the mining business. The iron ore price is a function of its composition, which is the outcome of the beneficiation process. So, end-to-end integrated planning of mine operations can reduce risks of penalties on the iron ore price. In a standard iron mining company, the production chain is composed of mining, ore beneficiation, and client supply. When mine planning and client supply decisions are made uncoordinated, the beneficiation plant struggles to deliver the best blend possible. Technological improvements in several fields allowed bridging the gap between departments and boosting integrated decision-making processes. Clusterization and classification algorithms over historical production data generate reasonable previsions for quality and volume of iron ore produced for each pile of run-of-mine (ROM) processed. Mathematical modeling can use those deterministic relations to propose iron ore blends that better-fit specifications within a delivery schedule. Additionally, a model capable of representing the whole production chain can clearly compare the overall impact of different decisions in the process. This study shows how flexibilization combined with a planning optimization model between the mine and the ore beneficiation processes can reduce risks of out of specification deliveries. The model capabilities are illustrated on a hypothetical iron ore mine with magnetic separation process. Finally, this study shows ways of cost reduction or profit increase by optimizing process indicators across the production chain and integrating the different plannings with the sales decisions.

Keywords: clusterization and classification algorithms, integrated planning, mathematical modeling, optimization, penalty minimization

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Authors: Ingy Ibrahim El-Darwish, Neveen Youssef Azmy

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The endowment of a healthy environment can be implemented by endorsing sustainable fundamentals. Design of sustainable buildings through recycling of waste, can reduce health problems, provide good environments and contribute to the aesthetically pleasing entourage. Such environments can help in providing energy-saving alternatives to consolidate the principles of sustainability. The poor community awareness and the absence of laws and legislation in Egypt for waste management specifically in residential areas have led to an inability to provide an integrated system for waste management in urban and rural areas. Many problems and environmental challenges face the Egyptian urban environments. From these problems, is the lack of a cohesive vision for waste collection and recycling for energy-saving. The second problem is the lack public awareness of the short term and long term vision of waste management. Bad practices have adversely affected the efficiency of environmental management systems due to lack of urban legislations that codify collection and recycling of residential communities in Egyptian urban environments. Hence, this research tries to address residents on waste management matters to facilitate legislative process on waste collection and classification within residential units and outside them in a preparation phase for recycling in the Egyptian urban environments. In order to achieve this goal, one of the Egyptian communities has been addressed, analyzed and studied. Waste collection, classification, separation and access to recycling places in the urban city are proposed in preparation for a legislation ruling and regulating the process. Hence, sustainable principles are to be achieved.

Keywords: recycling, residential buildings, sustainability, waste

Procedia PDF Downloads 317

Authors: Marija Zulić, Vanda Hájková, Nina Brkić-Jovanović, Linda Rathousová, Sanja Tomić

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Cerebral palsy is primarily reflected in the disorder of the development of movement and posture, which may be accompanied by sensory disturbances, disturbances of perception, cognition and communication, behavioural disorders and epilepsy. According to current inclusive attitudes towards people with disabilities implies that full social participation of children with cerebral palsy means inclusion in all activities in family, peer, school and leisure environments in the same scope and to the same extent as is the case with the children of proper development and without physical difficulties. Due to the fact that it has been established that the quality of children's participation in primary school is directly related to their social inclusion in future life, the aim of the paper is to identify predictors of social participation, respectively, and in particular, factors that could to improve the quality of social participation of children with cerebral palsy, in the primary school environment in Czech Republic. The study includes children with cerebral palsy (n = 75) in the Czech Republic, aged between six and 12 years who attend mainstream or special primary schools to the sixth grade. The main instrument used was the first and third part of the School function assessment questionnaire. It will also take into account the type of damage assessed according to a scale the Gross motor function classification system, five–level classification system for cerebral palsy. The research results will provide detailed insight into the degree of social participation of children with cerebral palsy and the factors that would be a potential cause of their levels of participation, in regular and special primary schools, in different socioeconomic environments in Czech Republic.

Keywords: cerebral palsy, Czech republic, social participation, the school function assessment

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Authors: S. Mamoucha, J. Rahul, N. Christodoulakis

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Introduction: Nature has been the source of medicinal agents since the dawn of the human existence on Earth. Currently, millions of people, in the developing world, rely on medicinal plants for primary health care, income generation and lifespan improvement. In Greece, more than 5500 plant taxa are reported while about 250 of them are considered to be of great pharmaceutical importance. Among the plants used for medical purposes, Vitex agnus-castus L. (Verbenaceae) is known since ancient times. It is a small tree or shrub, widely distributed in the Mediterranean basin up to the Central Asia. It is also known as chaste tree or monks pepper. Theophrastus mentioned the shrub several times, as ‘agnos’ in his ‘Enquiry into Plants’. Dioscorides mentioned the use of V. agnus-castus for the stimulation of lactation in nursing mothers and the treatment of several female disorders. The plant has important medicinal properties and a long tradition in folk medicine as an antimicrobial, diuretic, digestive and insecticidal agent. Materials and methods: Leaves were cleaned, detached, fixed, sectioned and investigated with light and Scanning Electron Microscopy (SEM). Histochemical tests were executed as well. Specific histochemical reagents (osmium tetroxide, H2SO4, vanillin/HCl, antimony trichloride, Wagner’ s reagent, Dittmar’ s reagent, potassium bichromate, nitroso reaction, ferric chloride and di methoxy benzaldehyde) were used for the sub cellular localization of secondary metabolites. Results: Light microscopical investigations of the elongated leaves of V. agnus-castus revealed three layers of palisade parenchyma, just below the single layered adaxial epidermis. The spongy parenchyma is rather loose. Adaxial epidermal cells are larger in magnitude, compared to those of the abaxial epidermis. Four different types of capitate, secreting trichomes, were localized among the abaxial epidermal cells. Stomata were observed at the abaxial epidermis as well. SEM revealed the interesting arrangement of trichomes. Histochemical treatment on fresh and plastic embedded tissue sections revealed the nature and the sites of secondary metabolites accumulation (flavonoids, steroids, terpenes). Acknowledgment: This work was supported by IKY - State Scholarship Foundation, Athens, Greece.

Keywords: Vitex agnus-castus, leaf anatomy, histochemical reagents, secondary metabolites

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Authors: Sagheer Ahmad

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Teaching is a sacred profession whereby students are developed in their mental abilities to cope with the challenges of the remote world. Thinkers have developed plenty of interesting ways to make the learning process quick and absorbing for the students. However, third world countries are still lacking these remote facilities in the institutions, and therefore, teaching is totally dependent upon the skills of the teachers. Skillful teachers use self-devised and stimulating ideas to grab the attention of their students. Most of the time their ideas are based on local grounds with which the students are already familiar. This self-explanatory characteristic is the base of several local ideologies to disseminate scientific knowledge to new generations. Biology is such a subject which largely bases upon hypotheses, and teaching it in an interesting way is needful to create a friendly relationship between teacher and student, and to make a fantastic learning environment. Taxonomic classification if presented as it is, may not be attractive for the secondary school students who just start learning about biology at elementary levels. Presenting this hierarchy by exemplifying Kingdom, Phylum, Class, Order, family, genus and Species as comparatives of our division into continents, countries, cities, towns, villages, homes and finally individuals could be an attention-grabbing approach to make this concept get into bones of students. Similarly, many other interesting approaches have also been adopted to teach students in a fascinating way so that learning science subjects may not be boring for them. Discussing these appealing ways of teaching students can be a valuable stimulus to refine teaching methodologies about science, thereby promoting the concept of friendly learning.

Keywords: biology, innovative approaches, taxonomic classification, teaching

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Authors: Sirilak Areerachakul, Nat Ployong, Supayothin Na Songkla

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This research’s objective is to select the model with most accurate value by using Neural Network Technique as a way to filter potential students who enroll in IT course by electronic learning at Suan Suanadha Rajabhat University. It is designed to help students selecting the appropriate courses by themselves. The result showed that the most accurate model was 100 Folds Cross-validation which had 73.58% points of accuracy.

Keywords: artificial neural network, classification, students, e-learning

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Authors: Ana Staneva, Vessela Stoimenova

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A parametric estimation of the MBP with Power Series offspring distribution family is considered in this paper. The MLE for the parameters is obtained in the case when the observable data are incomplete and consist only with the generation sizes of the family tree of MBP. The parameter estimation is calculated by using the Monte Carlo EM algorithm. The estimation for the posterior distribution and for the offspring distribution parameters are calculated by using the Bayesian approach and the Gibbs sampler. The article proposes various examples with bivariate branching processes together with computational results, simulation and an implementation using R.

Keywords: Bayesian, branching processes, EM algorithm, Gibbs sampler, Monte Carlo methods, statistical estimation

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Authors: Maximilian Elsen, Frank Tietze

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The development and diffusion of green technologies are crucial for achieving our ambitious climate targets. The Paris Agreement commits its members to develop strategies for achieving net zero greenhouse gas emissions by the second half of the century. Governments, executives, and academics are working on net-zero strategies and the business of rating organisations on their environmental, social and governance (ESG) performance has grown tremendously in its public interest. ESG data is now commonly integrated into traditional investment analysis and an important factor in investment decisions. Creating these metrics, however, is inherently challenging as environmental and social impacts are hard to measure and uniform requirements on ESG reporting are lacking. ESG metrics are often incomplete and inconsistent as they lack fully accepted reporting standards and are often of qualitative nature. This study explores the use of patent data for assessing the environmental performance of companies by focusing on their patented inventions in the space of climate change mitigation and adaptation technologies (CCMAT). The present study builds on the successful identification of CCMAT patents. In this context, the study adopts the Y02 patent classification, a fully cross-sectional tagging scheme that is fully incorporated in the Cooperative Patent Classification (CPC), to identify Climate Change Adaptation Technologies. The Y02 classification was jointly developed by the European Patent Office (EPO) and the United States Patent and Trademark Office (USPTO) and provides means to examine technologies in the field of mitigation and adaptation to climate change across relevant technologies. This paper develops sustainability-related metrics for firm-level patent portfolios. We do so by adopting a three-step approach. First, we identify relevant CCMAT patents based on their classification as Y02 CPC patents. Second, we examine the technological strength of the identified CCMAT patents by including more traditional metrics from the field of patent analytics while considering their relevance in the space of CCMAT. Such metrics include, among others, the number of forward citations a patent receives, as well as the backward citations and the size of the focal patent family. Third, we conduct our analysis on a firm level by sector for a sample of companies from different industries and compare the derived sustainability performance metrics with the firms’ environmental and financial performance based on carbon emissions and revenue data. The main outcome of this research is the development of sustainability-related metrics for firm-level environmental performance based on patent data. This research has the potential to complement existing ESG metrics from an innovation perspective by focusing on the environmental performance of companies and putting them into perspective to conventional financial performance metrics. We further provide insights into the environmental performance of companies on a sector level. This study has implications of both academic and practical nature. Academically, it contributes to the research on eco-innovation and the literature on innovation and intellectual property (IP). Practically, the study has implications for policymakers by deriving meaningful insights into the environmental performance from an innovation and IP perspective. Such metrics are further relevant for investors and potentially complement existing ESG data.

Keywords: climate change mitigation, innovation, patent portfolios, sustainability

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Authors: Néstor Iván Rojas, Wilson Medina Sierra

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Managing spatial information is described through a Geographic Information System (GIS), for some neighborhoods in the city of Tunja, in relation to the structural typology of the buildings. The use of GIS provides tools that facilitate the capture, processing, analysis and dissemination of cartographic information, product quality evaluation of the classification of buildings. Allows the development of a method that unifies and standardizes processes information. The project aims to generate a geographic database that is useful to the entities responsible for planning and disaster prevention and care for vulnerable populations, also seeks to be a basis for seismic vulnerability studies that can contribute in a study of urban seismic microzonation. The methodology consists in capturing the plat including road naming, neighborhoods, blocks and buildings, to which were added as attributes, the product of the evaluation of each of the housing data such as the number of inhabitants and classification, year of construction, the predominant structural systems, the type of mezzanine board and state of favorability, the presence of geo-technical problems, the type of cover, the use of each building, damage to structural and non-structural elements . The above data are tabulated in a spreadsheet that includes cadastral number, through which are systematically included in the respective building that also has that attribute. Geo-referenced data base is obtained, from which graphical outputs are generated, producing thematic maps for each evaluated data, which clearly show the spatial distribution of the information obtained. Using GIS offers important advantages for spatial information management and facilitates consultation and update. Usefulness of the project is recognized as a basis for studies on issues of planning and prevention.

Keywords: microzonation, buildings, geo-processing, cadastral number

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Authors: Usman Ahmed Khan

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Urbanization directly affects the existing infrastructure, landscape modification, environmental contamination, and traffic pollution, especially if there is a lack of urban planning. Recently, the rapid urban sprawl has resulted in less developed green areas and has devastating environmental consequences. This study was aimed to study the past urban expansion rates and measure LST from satellite data. The land use land cover (LULC) maps of years 1996, 2010, 2013, and 2017 were generated using landsat satellite images. Four main classes, i.e., water, urban, bare land, and vegetation, were identified using unsupervised classification with iterative self-organizing data analysis (isodata) technique. The LST from satellite thermal data can be derived from different procedures: atmospheric, radiometric calibrations and surface emissivity corrections, classification of spatial changeability in land-cover. Different methods and formulas were used in the algorithm that successfully retrieves the land surface temperature to help us study the thermal environment of the ground surface. To verify the algorithm, the land surface temperature and the near-air temperature were compared. The results showed that, From 1996-2017, urban areas increased to about a considerable increase of about 48%. Few areas of the city also shown in a reduction in LST from the year 1996-2017 that actually began their transitional phase from rural to urban LULC. The mean temperature of the city increased averagely about 1ºC each year in the month of October. The green and vegetative areas witnessed a decrease in the area while a higher number of pixels increased in urban class.

Keywords: LST, LULC, isodata, urbanization

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Authors: Chih Jou Hsiao, Chung Ming Lo, Li Chun Hsieh

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Brain tumor is not the cancer having high incidence rate, but its high mortality rate and poor prognosis still make it as a big concern. On clinical examination, the grading of brain tumors depends on pathological features. However, there are some weak points of histopathological analysis which can cause misgrading. For example, the interpretations can be various without a well-known definition. Furthermore, the heterogeneity of malignant tumors is a challenge to extract meaningful tissues under surgical biopsy. With the development of magnetic resonance imaging (MRI), tumor grading can be accomplished by a noninvasive procedure. To improve the diagnostic accuracy further, this study proposed a computer-aided diagnosis (CAD) system based on MRI features to provide suggestions of tumor grading. Gliomas are the most common type of malignant brain tumors (about 70%). This study collected 34 glioblastomas (GBMs) and 73 lower-grade gliomas (LGGs) from The Cancer Imaging Archive. After defining the region-of-interests in MRI images, multiple quantitative morphological features such as region perimeter, region area, compactness, the mean and standard deviation of the normalized radial length, and moment features were extracted from the tumors for classification. As results, two of five morphological features and three of four image moment features achieved p values of <0.001, and the remaining moment feature had p value <0.05. Performance of the CAD system using the combination of all features achieved the accuracy of 83.18% in classifying the gliomas into LGG and GBM. The sensitivity is 70.59% and the specificity is 89.04%. The proposed system can become a second viewer on clinical examinations for radiologists.

Keywords: brain tumor, computer-aided diagnosis, gliomas, magnetic resonance imaging

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Authors: Ibrahim A. Adeniran, Rui F. Zhu, Man S. Wong

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Owing to the insufficiency in the spatial representativeness and continuity of in situ temperature measurements from weather stations (WS), the use of temperature measurement from WS for large-range diurnal analysis in heterogenous landscapes has been limited. This has made the accurate estimation of land surface temperature (LST) from remotely sensed data more crucial. Moreover, the study of dynamic interaction between the atmosphere and the physical surface of the Earth could be enhanced at both annual and diurnal scales by using optimal LST data derived from satellite sensors. The tradeoff between the spatial and temporal resolution of LSTs from satellite’s thermal infrared sensors (TIRS) has, however, been a major challenge, especially when high spatiotemporal LST data are recommended. It is well-known from existing literature that polar satellites have the advantage of high spatial resolution, while geostationary satellites have a high temporal resolution. Hence, this study is aimed at designing a framework for the cross-comparison of LST data from polar and geostationary satellites in a heterogeneous landscape. This could help to understand the relationship between the LST estimates from the two satellites and, consequently, their integration in diurnal LST analysis. Landsat-8 satellite data will be used as the representative of the polar satellite due to the availability of its long-term series, while the Himawari-8 satellite will be used as the data source for the geostationary satellite because of its improved TIRS. For the study area, Hong Kong Special Administrative Region (HK SAR) will be selected; this is due to the heterogeneity in the landscape of the region. LST data will be retrieved from both satellites using the Split window algorithm (SWA), and the resulting data will be validated by comparing satellite-derived LST data with temperature data from automatic WS in HK SAR. The LST data from the satellite data will then be separated based on the land use classification in HK SAR using the Global Land Cover by National Mapping Organization version3 (GLCNMO 2013) data. The relationship between LST data from Landsat-8 and Himawari-8 will then be investigated based on the land-use class and over different seasons of the year in order to account for seasonal variation in their relationship. The resulting relationship will be spatially and statistically analyzed and graphically visualized for detailed interpretation. Findings from this study will reveal the relationship between the two satellite data based on the land use classification within the study area and the seasons of the year. While the information provided by this study will help in the optimal combination of LST data from Polar (Landsat-8) and geostationary (Himawari-8) satellites, it will also serve as a roadmap in the annual and diurnal urban heat (UHI) analysis in Hong Kong SAR.

Keywords: automatic weather station, Himawari-8, Landsat-8, land surface temperature, land use classification, split window algorithm, urban heat island

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Authors: Ian Omung'a

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Breast cancer remains to be one of the deadliest cancers for women worldwide, with the risk of developing tumors being as high as 50 percent in Sub-Saharan African countries like Kenya. With as many as 42 percent of these cases set to be diagnosed late when cancer has metastasized and or the prognosis has become terminal, Full Field Digital [FFD] Mammography remains an effective screening technique that leads to early detection where in most cases, successful interventions can be made to control or eliminate the tumors altogether. FFD Mammograms have been proven to multiply more effective when used together with Computer-Aided Detection and Diagnosis [CADe] systems, relying on algorithmic implementations of Deep Learning techniques in Computer Vision to carry out deep pattern recognition that is comparable to the level of a human radiologist and decipher whether specific areas of interest in the mammogram scan image portray abnormalities if any and whether these abnormalities are indicative of a benign or malignant tumor. Within this paper, we review emergent Deep Learning techniques that will prove relevant to the development of State-of-The-Art FFD Mammogram CADe systems. These techniques will span self-supervised learning for context-encoded occlusion, self-supervised learning for pre-processing and labeling automation, as well as the creation of a standardized large-scale mammography dataset as a benchmark for CADe systems' evaluation. Finally, comparisons are drawn between existing practices that pre-date these techniques and how the development of CADe systems that incorporate them will be different.

Keywords: breast cancer diagnosis, computer aided detection and diagnosis, deep learning, whole mammogram classfication, ultrasound classification, computer vision

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Authors: Marija Zulić, Vanda Hájková, Nina Brkić–Jovanović, Srećko Potić, Sanja Tomić

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The name cerebral palsy comes from the word cerebrum, which means the brain and the word palsy, which means seizure, and essentially refers to the movement disorder. In the clinical picture of cerebral palsy, basic neuromotor disorders are associated with other various disorders: behavioural, intellectual, speech, sensory, epileptic seizures, and bone and joint deformities. Motor speech disorders are among the most common difficulties present in people with cerebral palsy. Social participation represents an interaction between an individual and their social environment. Quality of social participation of the students with cerebral palsy at school is an important indicator of their successful participation in adulthood. One of the most important skills for the undisturbed social participation is ability of good communication. The aim of the study was to determine relation between social participation of students with cerebral palsy and presence of their speech impairment in primary schools in the Czech Republic. The study was performed in the Czech Republic in mainstream schools and schools established for the pupils with special education needs. We analysed 75 children with cerebral palsy aged between six and twelve years attending up to sixth grade by using the first and the third part of the school function assessment questionnaire as the main instrument. The other instrument we used in the research is the Gross motor function classification system–five–level classification system, which measures degree of motor functions of children and youth with cerebral palsy. Funding for this study was provided by the Grant Agency of Charles University in Prague.

Keywords: cerebral palsy, social participation, speech disorders, The Czech Republic, the school function assessment

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Authors: Rahul Patel

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Ahmedabad, a city located in western India, is experiencing rapid growth due to urbanization and industrialization. It is projected to become a metropolitan city in the near future, resulting in various construction activities. Soil testing is necessary before construction can commence, requiring construction companies and contractors to periodically conduct soil testing. The focus of this study is on the process of creating a spatial database that is digitally formatted and integrated with geotechnical data and a Geographic Information System (GIS). Building a comprehensive geotechnical (Geo)-database involves three steps: collecting borehole data from reputable sources, verifying the accuracy and redundancy of the data, and standardizing and organizing the geotechnical information for integration into the database. Once the database is complete, it is integrated with GIS, allowing users to visualize, analyze, and interpret geotechnical information spatially. Using a Topographic to Raster interpolation process in GIS, estimated values are assigned to all locations based on sampled geotechnical data values. The study area was contoured for SPT N-Values, Soil Classification, Φ-Values, and Bearing Capacity (T/m2). Various interpolation techniques were cross-validated to ensure information accuracy. This GIS map enables the calculation of SPT N-Values, Φ-Values, and bearing capacities for different footing widths and various depths. This study highlights the potential of GIS in providing an efficient solution to complex phenomena that would otherwise be tedious to achieve through other means. Not only does GIS offer greater accuracy, but it also generates valuable information that can be used as input for correlation analysis. Furthermore, this system serves as a decision support tool for geotechnical engineers.

Keywords: ArcGIS, borehole data, geographic information system, geo-database, interpolation, SPT N-value, soil classification, Φ-Value, bearing capacity

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Authors: Sedef Şahin, Meral Huri

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Cerebral Palsy (CP) represents the most frequent cause of physical disability in children with a rate of 2,9 per 1000 live births. The activity-focused intervention is known to improve function and reduce activity limitations and barriers to participation of children with disabilities. The aim of the study was to assess the effects of occupational therapy on level of fatigue, activity performance and satisfaction in children with Unilateral Cerebral Palsy. Twenty-two children with hemiparetic cerebral palsy (mean age: 9,3 ± 2.1years; Gross Motor Function Classification System ( GMFCS) level from I to V (I = 54%, II = 23%, III = 14%, IV= 9%, V= 0%), Manual Ability Classification System (MACS) level from I to V (I = 40%, II = 32%, III = 14%, IV= 10%, V= 4%), were assigned to occupational therapy program for 6 weeks.Visual Analogue Scale (VAS) was used for intensity of the fatigue they experienced at the time on a 10 point Likert scale (1-10).Activity performance and satisfaction were measured with Canadian Occupational Performance Measure (COPM).A client-centered occupational therapy intervention was designed according to results of COPM. The results were compared with nonparametric Wilcoxon test before and after the intervention. Thirteen of the children were right-handed, whereas nine of the children were left handed.Six weeks of intervention showed statistically significant differences in level of fatigue, compared to first assessment(p<0,05). The mean score of first and the second activity performance scores were 4.51 ± 1.70 and 7.35 ± 2.51 respectively. Statistically significant difference between performance scores were found (p<0.01). The mean scores of first and second activity satisfaction scores were of 2.30± 1.05 and 5.51 ± 2.26 respectively. Statistically significant difference between satisfaction assessments were found (p<0.01). Occupational therapy is an evidence-based approach and occupational therapy interventions implemented by therapists were clinically effective on severity of fatigue, activity performance and satisfaction if implemented individually during 6 weeks.

Keywords: activity performance, cerebral palsy, fatigue, occupational therapy

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Authors: Wei Ji

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This study was conducted in the Kansas City metropolitan area of the United States, which has experienced significant urban sprawling in recent decades. The remote sensing of land cover changes in this area spanned over four decades from 1972 through 2010. The project was implemented in two stages: the first stage focused on detection of long-term trends of urban land cover change, while the second one examined how to detect the coupled effects of human impact and climate change on urban landscapes. For the first-stage study, six Landsat images were used with a time interval of about five years for the period from 1972 through 2001. Four major land cover types, built-up land, forestland, non-forest vegetation land, and surface water, were mapped using supervised image classification techniques. The study found that over the three decades the built-up lands in the study area were more than doubled, which was mainly at the expense of non-forest vegetation lands. Surprisingly and interestingly, the area also saw a significant gain in surface water coverage. This observation raised questions: How have human activities and precipitation variation jointly impacted surface water cover during recent decades? How can we detect such coupled impacts through remote sensing analysis? These questions led to the second stage of the study, in which we designed and developed approaches to detecting fine-scale surface waters and analyzing coupled effects of human impact and precipitation variation on the waters. To effectively detect urban landscape changes that might be jointly shaped by precipitation variation, our study proposed “urban wetscapes” (loosely-defined urban wetlands) as a new indicator for remote sensing detection. The study examined whether urban wetscape dynamics was a sensitive indicator of the coupled effects of the two driving forces. To better detect this indicator, a rule-based classification algorithm was developed to identify fine-scale, hidden wetlands that could not be appropriately detected based on their spectral differentiability by a traditional image classification. Three SPOT images for years 1992, 2008, and 2010, respectively were classified with this technique to generate the four types of land cover as described above. The spatial analyses of remotely-sensed wetscape changes were implemented at the scales of metropolitan, watershed, and sub-watershed, as well as based on the size of surface water bodies in order to accurately reveal urban wetscape change trends in relation to the driving forces. The study identified that urban wetscape dynamics varied in trend and magnitude from the metropolitan, watersheds, to sub-watersheds in response to human impacts at different scales. The study also found that increased precipitation in the region in the past decades swelled larger wetlands in particular while generally smaller wetlands decreased mainly due to human development activities. These results confirm that wetscape dynamics can effectively reveal the coupled effects of human impact and climate change on urban landscapes. As such, remote sensing of this indicator provides new insights into the relationships between urban land cover changes and driving forces.

Keywords: urban land cover, human impact, climate change, rule-based classification, across-scale analysis

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Authors: Gideon C. Joseph

Abstract:

The ability to localize mobile devices is quite important, as some applications may require location information of these devices to operate or deliver better services to the users. Although there are several ways of acquiring location data of mobile devices, the WLAN fingerprinting approach has been considered in this work. This approach uses the Received Signal Strength Indicator (RSSI) measurement as a function of the position of the mobile device. RSSI is a quantitative technique of describing the radio frequency power carried by a signal. RSSI may be used to determine RF link quality and is very useful in dense traffic scenarios where interference is of major concern, for example, indoor environments. This research aims to design a system that can predict the location of a mobile device, when supplied with the mobile’s RSSIs. The developed system takes as input the RSSIs relating to the mobile device, and outputs parameters that describe the location of the device such as the longitude, latitude, floor, and building. The relationship between the Received Signal Strengths (RSSs) of mobile devices and their corresponding locations is meant to be modelled; hence, subsequent locations of mobile devices can be predicted using the developed model. It is obvious that describing mathematical relationships between the RSSIs measurements and localization parameters is one option to modelling the problem, but the complexity of such an approach is a serious turn-off. In contrast, we propose an intelligent system that can learn the mapping of such RSSIs measurements to the localization parameters to be predicted. The system is capable of upgrading its performance as more experiential knowledge is acquired. The most appealing consideration to using such a system for this task is that complicated mathematical analysis and theoretical frameworks are excluded or not needed; the intelligent system on its own learns the underlying relationship in the supplied data (RSSI levels) that corresponds to the localization parameters. These localization parameters to be predicted are of two different tasks: Longitude and latitude of mobile devices are real values (regression problem), while the floor and building of the mobile devices are of integer values or categorical (classification problem). This research work presents artificial neural network based intelligent systems to model the relationship between the RSSIs predictors and the mobile device localization parameters. The designed systems were trained and validated on the collected WLAN fingerprint database. The trained networks were then tested with another supplied database to obtain the performance of trained systems on achieved Mean Absolute Error (MAE) and error rates for the regression and classification tasks involved therein.

Keywords: indoor localization, WLAN fingerprinting, neural networks, classification, regression

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Authors: Yassine Bouzouita, Oumaima Belghith, Cyrine Zitoun, Charles Bonneau

Abstract:

Performance measurement framework (PMF) is an essential tool in any organization to assess the performance of its processes. It guides businesses to stay on track with their objectives and benchmark themselves from the market. With the growing trend of the digital transformation of business processes, led by innovations in artificial intelligence (AI) & Big Data applications, developing a mature system capable of capturing the impact of digital solutions across different industries became a necessity. Based on the conducted research, no such system has been developed in academia nor the industry. In this context, this paper covers a variety of methodologies on performance measurement, overviews the major AI and big data applications in the banking sector, and covers an exhaustive list of relevant metrics. Consequently, this paper is of interest to both researchers and practitioners. From an academic perspective, it offers a comparative analysis of the reviewed performance measurement frameworks. From an industry perspective, it offers exhaustive research, from market leaders, of the major applications of AI and Big Data technologies, across the different departments of an organization. Moreover, it suggests a standardized classification model with a well-defined structure of intelligent digital solutions. The aforementioned classification is mapped to a centralized library that contains an indexed collection of potential metrics for each application. This library is arranged in a manner that facilitates the rapid search and retrieval of relevant metrics. This proposed framework is meant to guide professionals in identifying the most appropriate AI and big data applications that should be adopted. Furthermore, it will help them meet their business objectives through understanding the potential impact of such solutions on the entire organization.

Keywords: AI and Big Data applications, impact assessment, metrics, performance measurement

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Authors: Riona Dighe

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This research attempts to identify an alternative approach to the measurement of Gross National Happiness (GNH). It uses artificial intelligence (AI), incorporating natural language processing (NLP) and sentiment analysis to measure GNH. We use ‘off the shelf’ NLP models responsible for the sentiment analysis of a sentence as a building block for this research. We constructed an algorithm using NLP models to derive a sentiment analysis score against sentences. This was then tested against a sample of 20 respondents to derive a sentiment analysis score. The scores generated resembled human responses. By utilising the MLP classifier, decision tree, linear model, and K-nearest neighbors, we were able to obtain a test accuracy of 89.97%, 54.63%, 52.13%, and 47.9%, respectively. This gave us the confidence to use the NLP models against sentences in websites to measure the GNH of a country.

Keywords: artificial intelligence, NLP, sentiment analysis, gross national happiness

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Authors: Noureddine Mahmoudi

Abstract:

The use of natural fibers in the development of composite materials is a sector in full expansion. These fibers were used for their low cost, their availability and their renewable character. The fibers of the palm (palm tree) were used as reinforcement in polypropylene (PP). The date palm fibers have some potential because of their ecological and economic interest. Both unmodified and compatibilized fibers are used. Compatibilization was carried out with the use of maleic anhydride copolymers. The morphology and mechanical properties were characterized by electron microscopy scanning (SEM) and tensile tests. The influence of fiber content on mechanical properties of composite PP / date palm has been evaluated and demonstrated, that the maximum stress and elongation decreases with increasing fiber volume rate. On the other hand, an increase of the tensile modulus has been noticed, but after the fibers improvement, the maximum stress increases significantly up to 25% weight.

Keywords: plant fiber, palm, SEM, compatibilizer

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Authors: C. R. Ríos-Soberanis, V. M. Moo-Huchin, R. J. Estrada-Leon, E. Perez-Pacheco

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Polymers from renewable resources have attracted an increasing amount of attention over the last two decades, predominantly due to two major reasons: firstly environmental concerns, and secondly the realization that our petroleum resources are finite. Finding new uses for agricultural commodities is also an important area of research. Therefore, it is crucial to get new sources of natural materials that can be used in different applications. Ramon tree (Brosimum alicastrum) is a tropical plant that grows freely in Yucatan countryside. This paper focuses on the seeds recollection, processing and starch extraction and characterization in order to find out about its suitability as biomaterial. Results demonstrated that it has a high content of qualities to be used not only as comestible but also as an important component in polymeric blends.

Keywords: biomaterials, characterization techniques, natural resource, starch

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Authors: Marutani M, Kawajiri H, Usui C, Takai Y, Kawaguchi T

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Background: To build the inclusive society, the Japanese government provides “transition support for employment system” for Persons with Disabilities (PWDs). It is, however, difficult to provide appropriate accommodations due to their changeable health conditions. Mobile phone applications (App) are useful to monitor their health conditions and their environments, and effective to improve reasonable accommodations for PWDs. Purpose: This study aimed to develop an App that PWDs input their self-assessment and make their health conditions and environment conditions visible. To attain the goal, we investigated the items of the App for the first step. Methods: Qualitative and descriptive design was used for this study. Study participants were recruited by snowball sampling in July and August 2023. They had to have had minimum of five-years of experience to support PWDs’ employment. Semi-structured interviews were conducted on their assessment regarding PWDs’ conditions of daily activities, their health conditions, and living and working environment. Verbatim transcript was created from each interview content. We extracted the following items in tree groups from each verbatim transcript: daily activities, health conditions, and living and working. Results: Fourteen participants were involved (average years of experience: 10.6 years). Based on the interviews, tree item groups were enriched. The items of daily activities were divided into fifty-five. The example items were as follows: “have meals on one’s style” “feel like slept well” “wake-up time, bedtime, and mealtime are usually fixed.” “commute to the office and work without barriers.” Thirteen items of health conditions were obtained like “feel no anxiety” “relieve stress” “focus on work and training” “have no pain” “have the physical strength to work for one day.” The items of categories of living and working environments were divided into fifteen-two. The example items were as follows: “have no barrier in home” “have supportive family members” “have time to take medication on time while at work” “commute time is just right” “people at the work understand the symptoms” “room temperature and humidity are just right” “get along well with friends in my own way.” The participants also mentioned the styles to input self-assessment like that a face scale would be preferred to number scale. Conclusion: The items were enriched existent paper-based assessment items in terms of living and working environment because those were obtained from the perspective of PWDs. We have to create the app and examine its usefulness with PWDs toward inclusive society.

Keywords: occupational health, innovatiove tool, people with disability, employment

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Authors: Oyebade K. Oyedotun

Abstract:

The cardiotocogram is a technical recording of the heartbeat rate and uterine contractions of a fetus during pregnancy. During pregnancy, several complications can occur to both the mother and the fetus; hence it is very crucial that medical experts are able to find technical means to check the healthiness of the mother and especially the fetus. It is very important that the fetus develops as expected in stages during the pregnancy period; however, the task of monitoring the health status of the fetus is not that which is easily achieved as the fetus is not wholly physically available to medical experts for inspection. Hence, doctors have to resort to some other tests that can give an indication of the status of the fetus. One of such diagnostic test is to obtain cardiotocograms of the fetus. From the analysis of the cardiotocograms, medical experts can determine the status of the fetus, and therefore necessary medical interventions. Generally, medical experts classify examined cardiotocograms into ‘normal’, ‘suspect’, or ‘pathological’. This work presents an artificial neural network based decision support system which can filter cardiotocograms data, producing the corresponding statuses of the fetuses. The capability of artificial neural network to explore the cardiotocogram data and learn features that distinguish one class from the others has been exploited in this research. In this research, feedforward and radial basis neural networks were trained on a publicly available database to classify the processed cardiotocogram data into one of the three classes: ‘normal’, ‘suspect’, or ‘pathological’. Classification accuracies of 87.8% and 89.2% were achieved during the test phase of the trained network for the feedforward and radial basis neural networks respectively. It is the hope that while the system described in this work may not be a complete replacement for a medical expert in fetus status evaluation, it can significantly reinforce the confidence in medical diagnosis reached by experts.

Keywords: decision support, cardiotocogram, classification, neural networks

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Authors: Emmanuel Barrantes Chaves, Ernesto Rivera Alvarado

Abstract:

This paper presents a method to enable agents for the Terraria game to evaluate algorithms commonly used in general video game artificial intelligence competitions. The usage of the ‘You Only Look Once’ model in the first layer of the process obtains information from the screen, translating this information into a video game description language known as “Video Game Description Language”; the agents take that as input to make decisions. For this, the state-of-the-art algorithms were tested and compared; Monte Carlo Tree Search and Rolling Horizon Evolutionary; in this case, Rolling Horizon Evolutionary shows a better performance. This approach’s main advantage is that a VGDL beforehand is unnecessary. It will be built on the fly and opens the road for using more games as a framework for AI.

Keywords: AI, MCTS, RHEA, Terraria, VGDL, YOLOv5

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Authors: Vedat Kurt, Tansel Koyunoglu, Gamze Kurt, Ozgen Aras

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Low back pain is one of the most common health problem in public. Common symptoms that can be associated with low back pain include; pain, functional disability, gait disturbances. The aim of the study was to investigate the differences between disability scores and gait parameters in subjects with low back pain. Sixty participants are included in our study, (35 men, 25 women, mean age: 37.65±10.02 years). Demographic characteristics of participants were recorded. Pain (visual analog scale) and disability level (Oswestry Disability Index(ODI)) were evaluated. Gait parameters were measured with Zebris-FDM-2 platform. Independent samples t-test was used to analyse the differences between subjects with under 40 points (n=31, mean age:35.8±11.3) and above 40 points (n=29, mean age:39.6±8.1) of ODI scores. Significant level in statistical analysis was accepted as 0.05. There was no significant difference between the ODI scores and groups’ ages. Statistically significant differences were found in step width between subjects with under 40 points of ODI and above 40 points of ODI score(p < 0.05). But there were non-significant differences with other gait parameters (p > 0.05). The differences between gait parameters and pain scores were not statistically significant (p > 0.05). Researchers generally agree that individuals with LBP walk slower and take shorter steps and have asymmetric step lengths when compared with than their age-matched pain-free counterparts. Also perceived general disability may have moderate correlation with walking performance. In the current study, the patients classified as minimal/moderate and severe disability level by using ODI scores. As a result, a patient with LBP who have higher disability level tends to increase support surface. On the other hand, we did not find any relation between pain intensity and gait parameters. It may be caused by the classification system of pain scores. Additional research is needed to investigate the effects of functionality level and pain intensity on gait in subjects with low back pain under different classification types.

Keywords: functionality, low back pain, gait, pain

Procedia PDF Downloads 282