Search results for: hand gesture classification

Authors: Nassima Noufail, Sara Bouhali

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In this work, we develop a semi-supervised solution for the purpose of action detection in videos and propose an efficient algorithm for video segmentation. The approach is divided into video segmentation, feature extraction, and classification. In the first part, a video is segmented into clips, and we used the K-means algorithm for this segmentation; our goal is to find groups based on similarity in the video. The application of k-means clustering into all the frames is time-consuming; therefore, we started by the identification of transition frames where the scene in the video changes significantly, and then we applied K-means clustering into these transition frames. We used two image filters, the gaussian filter and the Laplacian of Gaussian. Each filter extracts a set of features from the frames. The Gaussian filter blurs the image and omits the higher frequencies, and the Laplacian of gaussian detects regions of rapid intensity changes; we then used this vector of filter responses as an input to our k-means algorithm. The output is a set of cluster centers. Each video frame pixel is then mapped to the nearest cluster center and painted with a corresponding color to form a visual map. The resulting visual map had similar pixels grouped. We then computed a cluster score indicating how clusters are near each other and plotted a signal representing frame number vs. clustering score. Our hypothesis was that the evolution of the signal would not change if semantically related events were happening in the scene. We marked the breakpoints at which the root mean square level of the signal changes significantly, and each breakpoint is an indication of the beginning of a new video segment. In the second part, for each segment from part 1, we randomly selected a 16-frame clip, then we extracted spatiotemporal features using convolutional 3D network C3D for every 16 frames using a pre-trained model. The C3D final output is a 512-feature vector dimension; hence we used principal component analysis (PCA) for dimensionality reduction. The final part is the classification. The C3D feature vectors are used as input to a multi-class linear support vector machine (SVM) for the training model, and we used a multi-classifier to detect the action. We evaluated our experiment on the UCF101 dataset, which consists of 101 human action categories, and we achieved an accuracy that outperforms the state of art by 1.2%.

Keywords: video segmentation, action detection, classification, Kmeans, C3D

Procedia PDF Downloads 77

Authors: K. Petcharaporn

Abstract:

The external characteristics of tomatoes, such as freshness, color and size are typically used in quality control processes for tomatoes sorting. However, the internal mold infection of intact tomato cannot be sorted based on a visible assessment and destructive method alone. In this study, a non-destructive technique was used to predict the internal mold infection of intact tomatoes by using transmittance visible and near infrared (VIS-NIR) spectroscopy. Spectra for 200 samples contained 100 samples for normal tomatoes and 100 samples for mold infected tomatoes were acquired in the wavelength range between 665-955 nm. This data was used in conjunction with partial least squares-discriminant analysis (PLS-DA) method to generate a classification model for tomato quality between groups of internal mold infection of intact tomato samples. For this task, the data was split into two groups, 140 samples were used for a training set and 60 samples were used for a test set. The spectra of both normal and internally mold infected tomatoes showed different features in the visible wavelength range. Combined spectral pretreatments of standard normal variate transformation (SNV) and smoothing (Savitzky-Golay) gave the optimal calibration model in training set, 85.0% (63 out of 71 for the normal samples and 56 out of 69 for the internal mold samples). The classification accuracy of the best model on the test set was 91.7% (29 out of 29 for the normal samples and 26 out of 31 for the internal mold tomato samples). The results from this experiment showed that transmittance VIS-NIR spectroscopy can be used as a non-destructive technique to predict the internal mold infection of intact tomatoes.

Keywords: tomato, mold, quality, prediction, transmittance

Procedia PDF Downloads 361

Authors: Olga A. Momot, Boris I. Synzynys, Alla A. Oudalova

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Obninsk is situated in the Kaluga region 100 km southwest of Moscow on the left bank of the Protva River. Several enterprises utilizing nuclear energy are operating in the town. A special attention in the region where radiation-hazardous facilities are located has traditionally been paid to radioactive gas and aerosol releases into the atmosphere; liquid waste discharges into the Protva river and groundwater pollution. Municipal intakes involve 34 wells arranged 15 km apart in a sequence north-south along the foot of the left slope of the Protva river valley. Northern and southern water intakes are upstream and downstream of the town, respectively. They belong to river valley intakes with mixed feeding, i.e. precipitation infiltration is responsible for a smaller part of groundwater, and a greater amount is being formed by overflowing from Protva. Water intakes are maintained by the Protva river runoff, the volume of which depends on the precipitation fallen out and watershed area. Groundwater contamination with tritium was first detected in a sanitary-protective zone of the Institute of Physics and Power Engineering (SRC-IPPE) by Roshydromet researchers when realizing the “Program of radiological monitoring in the territory of nuclear industry enterprises”. A comprehensive survey of the SRC-IPPE’s industrial site and adjacent territories has revealed that research nuclear reactors and accelerators where tritium targets are applied as well as radioactive waste storages could be considered as potential sources of technogenic tritium. All the above sources are located within the sanitary controlled area of intakes. Tritium activity in water of springs and wells near the SRC-IPPE is about 17.4 – 3200 Bq/l. The observed values of tritium activity are below the intervention levels (7600 Bq/l for inorganic compounds and 3300 Bq/l for organically bound tritium). The risk has being assessed to estimate possible effect of considered tritium concentrations on human health. Data on tritium concentrations in pipe-line drinking water were used for calculations. The activity of 3H amounted to 10.6 Bq/l and corresponded to the risk of such water consumption of ~ 3·10-7 year-1. The risk value given in magnitude is close to the individual annual death risk for population living near a NPP – 1.6·10-8 year-1 and at the same time corresponds to the level of tolerable risk (10-6) and falls within “risk optimization”, i.e. in the sphere for planning the economically sound measures on exposure risk reduction. To estimate the chemical risk, physical and chemical analysis was made of waters from all springs and wells near the SRC-IPPE. Chemical risk from groundwater contamination was estimated according to the EPA US guidance. The risk of carcinogenic diseases at a drinking water consumption amounts to 5·10-5. According to the classification accepted the health risk in case of spring water consumption is inadmissible. The compared assessments of risk associated with tritium exposure, on the one hand, and the dangerous chemical (e.g. heavy metals) contamination of Obninsk drinking water, on the other hand, have confirmed that just these chemical pollutants are responsible for health risk.

Keywords: radiation-hazardous facilities, water intakes, tritium, heavy metal, health risk

Procedia PDF Downloads 239

Authors: Somayeh Lotfi, Manuel Eggimann, Eckhard Wagner, Radosław Mróz, Jan Deja

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One of the main environmental challenges in the construction industry is a strong social force to decrease the bulk transport of the building materials in urban environments. Considering this fact, applying more in-situ recycling technologies for Construction and Demolition Waste (CDW) is an urgent need. The European C2CA project develops a novel concrete recycling technology that can be performed purely mechanically and in situ. The technology consists of a combination of smart demolition, gentle grinding of the crushed concrete in an autogenous mill, and a novel dry classification technology called ADR to remove the fines. The feasibility of this recycling process was examined in demonstration projects involving in total 20,000 tons of End of Life (EOL) concrete from two office towers in Groningen, The Netherlands. This paper concentrates on the second demonstration project of C2CA, where EOL concrete was recycled on an industrial site. After recycling, the properties of the produced Recycled Aggregate (RA) were investigated, and results are presented. An experimental study was carried out on mechanical and durability properties of produced Recycled Aggregate Concrete (RAC) compared to those of the Natural Aggregate Concrete (NAC). The aim was to understand the importance of RA substitution, w/c ratio and type of cement to the properties of RAC. In this regard, two series of reference concrete with strength classes of C25/30 and C45/55 were produced using natural coarse aggregates (rounded and crushed) and natural sand. The RAC series were created by replacing parts of the natural aggregate, resulting in series of concrete with 0%, 20%, 50% and 100% of RA. Results show that the concrete mix design and type of cement have a decisive effect on the properties of RAC. On the other hand, the substitution of RA even at a high percentage replacement level has a minor and manageable impact on the performance of RAC. This result is a good indication towards the feasibility of using RA in structural concrete by modifying the mix design and using a proper type of cement.

Keywords: C2CA, ADR, concrete recycling, recycled aggregate, durability

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Authors: Atefeh Heydari, Mohammadali Tavakoli, Naomie Salim

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In recent years, we have witnessed that online reviews are the most important source of customers’ opinion. They are progressively more used by individuals and organisations to make purchase and business decisions. Unfortunately, for the reason of profit or fame, frauds produce deceptive reviews to hoodwink potential customers. Their activities mislead not only potential customers to make appropriate purchasing decisions and organisations to reshape their business, but also opinion mining techniques by preventing them from reaching accurate results. Spam reviews could be divided into two main groups, i.e. multiple and singleton spam reviews. Detecting a singleton spam review that is the only review written by a user ID is extremely challenging due to lack of clue for detection purposes. Singleton spam reviews are very harmful and various features and proofs used in multiple spam reviews detection are not applicable in this case. Current research aims to propose a novel supervised technique to detect singleton spam reviews. To achieve this, various features are proposed in this study and are to be combined with the most appropriate features extracted from literature and employed in a classifier. In order to compare the performance of different classifiers, SVM and naive Bayes classification algorithms were used for model building. The results revealed that SVM was more accurate than naive Bayes and our proposed technique is capable to detect singleton spam reviews effectively.

Keywords: classification algorithms, Naïve Bayes, opinion review spam detection, singleton review spam detection, support vector machine

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Authors: Rezvan Hosseinian

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Aim: Systemic sclerosis (SSc) is a chronic connective tissue disease with the clinical hallmark of skin thickening and tethering. The correlation of musculoskeletal features with other parameters should be considered in SSc patients. Methods: We reviewed the records of all patients who had more than one visit and standard anteroposterior radiography of hand. We used univariate analysis, and factors with p<0.05 were included in logistic regression to find out dependent factors. Results: Overall, 180 SSc patients were enrolled in our study, 161 (89.4%) of whom were women. The median age (IQR) was 47.0 years (16), and 52% had a diffuse subtype of the disease. In multivariate analysis, tendon friction rubs (TFRs) were associated with the presence of calcinosis, muscle tenderness, and flexion contracture (FC) on physical examination (p<0.05). Arthritis showed no differences in the two subtypes of the disease (p=0.98), and in multivariate analysis, there were no correlations between radiographic arthritis and serological and clinical features. The radiographic results indicated that disease duration correlated with joint erosion, acro-osteolysis, resorption of the distal ulna, calcinosis and radiologic FC (p< 0.05). Acro-osteolysis was more frequent in the dcSSc subtype, TFRs, and anti-TOPO I antibody. Radiologic FC showed an association with skin score, calcinosis and haematocrit <30% (p<0.05). Joint flexion on radiography was associated with disease duration, modified Rodnan skin score, calcinosis, and low hematocrit (P<0.01). Conclusion: Disease duration was a main dependent factor for developing joint erosion, acro-osteolysis, bone resorption, calcinosis, and flexion contracture on hand radiography. Acro-osteolysis presented in the severe form of the disease. Acro-osteolysis was the only dependent variable associated with bone demineralization.

Keywords: disease subsets, hand radiography, joint erosion, sclerosis

Procedia PDF Downloads 88

Authors: Abdelmadjid Mammeri, Fatima Zohra Mahi, Luca Varani, H. Marinchoi

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We propose an analytical model for the admittance and the noise calculations of the InGaAs transistor and diode. The development of the small-signal admittance takes into account the longitudinal and transverse electric fields through a pseudo two-dimensional approximation of the Poisson equation. The frequency-dependent of the small-signal admittance response is determined by the total currents and the potentials matrix relation between the gate and the drain terminals. The noise is evaluated by using the real part of the transistor/diode admittance under a small-signal perturbation. The analytical results show that the admittance spectrum exhibits a series of resonant peaks corresponding to the excitation of plasma waves. The appearance of the resonance is discussed and analyzed as functions of the channel length and the temperature. The model can be used, on one hand; to control the appearance of the plasma resonances, and on other hand; can give significant information about the noise frequency dependence in the InGaAs transistor and diode.

Keywords: InGaAs transistors, InGaAs diode, admittance, resonant peaks, plasma waves, analytical model

Procedia PDF Downloads 314

Authors: David Percy

Abstract:

Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

Procedia PDF Downloads 64

Authors: K. Petcharaporn, N. Prathengjit

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The external characteristics of tomatoes, such as freshness, color and size are typically used in quality control processes for tomatoes sorting. However, the internal mold infection of intact tomato cannot be sorted based on a visible assessment and destructive method alone. In this study, a non-destructive technique was used to predict the internal mold infection of intact tomatoes by using transmittance visible and near infrared (VIS-NIR) spectroscopy. Spectra for 200 samples contained 100 samples for normal tomatoes and 100 samples for mold infected tomatoes were acquired in the wavelength range between 665-955 nm. This data was used in conjunction with partial least squares-discriminant analysis (PLS-DA) method to generate a classification model for tomato quality between groups of internal mold infection of intact tomato samples. For this task, the data was split into two groups, 140 samples were used for a training set and 60 samples were used for a test set. The spectra of both normal and internally mold infected tomatoes showed different features in the visible wavelength range. Combined spectral pretreatments of standard normal variate transformation (SNV) and smoothing (Savitzky-Golay) gave the optimal calibration model in training set, 85.0% (63 out of 71 for the normal samples and 56 out of 69 for the internal mold samples). The classification accuracy of the best model on the test set was 91.7% (29 out of 29 for the normal samples and 26 out of 31 for the internal mold tomato samples). The results from this experiment showed that transmittance VIS-NIR spectroscopy can be used as a non-destructive technique to predict the internal mold infection of intact tomatoes.

Keywords: tomato, mold, quality, prediction, transmittance

Procedia PDF Downloads 518

Authors: T. Garba, Y. Y. Sabo A. Babanyara, K. G. Ilellah, A. K. Mutari

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This study used the Normalized Difference Vegetation Index (NDVI) and maps compiled from the classification of Landsat TM and Landsat ETM images of 1986 and 1999 respectively and Nigeria sat 1 images of 2007 to quantify changes in land use and land cover in selected areas of Nigeria covering 143,609 hectares that are threatened by the encroaching Sahara desert. The results of this investigation revealed a decrease in natural vegetation over the three time slices (1986, 1999 and 2007) which was characterised by an increase in high positive pixel values from 0.04 in 1986 to 0.22 and 0.32 in 1999 and 2007 respectively and, a decrease in natural vegetation from 74,411.60ha in 1986 to 28,591.93ha and 21,819.19ha in 1999 and 2007 respectively. The same results also revealed a periodic trend in which there was progressive increase in the cultivated area from 60,191.87ha in 1986 to 104,376.07ha in 1999 and a terminal decrease to 88,868.31ha in 2007. These findings point to expansion of vegetated and cultivated areas in in the initial period between 1988 and 1996 and reversal of these increases in the terminal period between 1988 and 1996. The study also revealed progressive expansion of built-up areas from 1, 681.68ha in 1986 to 2,661.82ha in 1999 and to 3,765.35ha in 2007. These results argue for the urgent need to protect and conserve the depleting natural vegetation by adopting sustainable human resource use practices i.e. intensive farming in order to minimize persistent depletion of natural vegetation.

Keywords: changes, classification, desertification, vegetation changes

Procedia PDF Downloads 385

Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

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In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other. As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO

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Authors: Nikita Katyal, Shaurya Uppal

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This paper tackles the significant challenge of accurately classifying customers within a retailer’s customer base. Accurate classification is essential for developing targeted marketing strategies that effectively engage this important demographic. To address this issue, we propose a method that utilizes Large Language Models (LLMs). By employing LLMs, we analyze the metadata associated with product purchases derived from historical data to identify key product categories that act as distinguishing factors. These categories, such as baby food, eldercare products, or family-sized packages, offer valuable insights into the likely household composition of customers, including families with babies, families with kids/teenagers, families with pets, households caring for elders, or mixed households. We segment high-confidence customers into distinct categories by integrating historical purchase behavior with LLM-powered product classification. This paper asserts that life stage segmentation can significantly enhance e-commerce businesses’ ability to target the appropriate customers with tailored products and campaigns, thereby augmenting sales and improving customer retention. Additionally, the paper details the data sources, model architecture, and evaluation metrics employed for the segmentation task.

Keywords: LLMs, segmentation, product tags, fine-tuning, target segments, marketing communication

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Authors: Zahra Nouri Sangedehy

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There are two anthropologies considered in Protagoras. The first of them (Protagoras) considers the appearance of man, like all other beings, as the result of a natural evolution without a predetermined plan and aim. Not only the human's corporeal existence is the result of evolution and natural choices, but also his moral and social life can be explained in the light of this factor. In this anthropology, the moral and political laws derive from the contract and the people's majority agreement of society to survive. Society and socio-political institutions are the reason for the education and training (paidia) of virtues in general. The second anthropology is Socrates's, which is not clearly projected and is hidden behind his arguments. In this way, man's moral and social life is intrinsic. Man is intrinsically a moral and social being. Socrates intends to criticize the theory of the contractual nature of ethics by demonstrating the unity of virtues on the one hand and the identity of virtue and knowledge, and the problem of the teaching of virtues based on intrinsic and a priori knowledge of human beings, on the other hand, albeit with a new kind of education and training, which will replace the Sophists' education. Therefore, ethics will have undoubted foundations, and human beings will be defined again.

Keywords: Protagoras, techne, arête, paidia

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Authors: Yeliz Karaca, Carlo Cattani

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This study focuses on the local Holder exponent as a measure of the function regularity for time series related to finance data. In this study, the attributes of the finance dataset belonging to 13 countries (India, China, Japan, Sweden, France, Germany, Italy, Australia, Mexico, United Kingdom, Argentina, Brazil, USA) located in 5 different continents (Asia, Europe, Australia, North America and South America) have been examined.These countries are the ones mostly affected by the attributes with regard to financial development, covering a period from 2012 to 2017. Our study is concerned with the most important attributes that have impact on the development of finance for the countries identified. Our method is comprised of the following stages: (a) among the multi fractal methods and Brownian motion Holder regularity functions (polynomial, exponential), significant and self-similar attributes have been identified (b) The significant and self-similar attributes have been applied to the Artificial Neuronal Network (ANN) algorithms (Feed Forward Back Propagation (FFBP) and Cascade Forward Back Propagation (CFBP)) (c) the outcomes of classification accuracy have been compared concerning the attributes that have impact on the attributes which affect the countries’ financial development. This study has enabled to reveal, through the application of ANN algorithms, how the most significant attributes are identified within the relevant dataset via the Holder functions (polynomial and exponential function).

Keywords: artificial neural networks, finance data, Holder regularity, multifractals

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Authors: Fatima Melit, Nedjemeddine Bounar

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Polycrystalline samples of Ce1-xMxO2-δ (x=0.1, 0.15, 0.2)(M=Gd, Y) were prepared by solid-state chemical reaction from mixtures of pre-dried oxides powders of CeO2, Gd2O3 and Y2O3 in the appropriate stoichiometric ratio to explore their use as solid electrolytes for intermediate temperature solid oxide fuel cells (IT-SOFCs). Their crystal structures and ionic conductivities were characterised by X-ray powder diffraction (XRD) and AC complex impedance spectroscopy (EIS). The XRD analyses confirm that all the resulting synthesised co-doped cerium oxide powders are single-phase and crystallise in the cubic structure system with the space group Fm3m. On the one hand, the lattice parameter (a ) of the phases increases with increasing Gd content; on the other hand, with increasing Y-substitution rate, the latter decreases. The results of complex impedance conductivity measurements have shown that doping has a remarkable effect on conductivity. The co-doped cerium phases showed significant ionic conductivity values, making these materials excellent candidates for solid oxide electrolytes at intermediate temperatures.

Keywords: electrolyte, Ceria, X-ray diffraction, EIS, SEM, SOFC

Procedia PDF Downloads 143

Authors: Sushma Ghogale

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With a surge in user-generated content or feedback or reviews on the internet, it has become possible and important to know consumers' opinions about products and services. This data is important for both potential customers and businesses providing the services. Data from social media is attracting significant attention and has become the most prominent channel of expressing an unregulated opinion. Prospective customers look for reviews from experienced customers before deciding to buy a product or service. Several websites provide a platform for users to post their feedback for the provider and potential customers. However, the biggest challenge in analyzing such data is in extracting latent features and providing term-level analysis of the data. This paper proposes an approach to use topic modeling to classify the reviews into topics and conduct sentiment analysis to mine the opinions. This approach can analyse and classify latent topics mentioned by reviewers on business sites or review sites, or social media using topic modeling to identify the importance of each topic. It is followed by sentiment analysis to assess the satisfaction level of each topic. This approach provides a classification of hotel reviews using multiple machine learning techniques and comparing different classifiers to mine the opinions of user reviews through sentiment analysis. This experiment concludes that Multinomial Naïve Bayes classifier produces higher accuracy than other classifiers.

Keywords: latent Dirichlet allocation, topic modeling, text classification, sentiment analysis

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Authors: Zegrar Ahmed, Ghabi Mohamed

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The degradation of arid and semi-arid ecosystems in Algeria has become a palpable fact that only hinders progress and rural development. In these exceptionally fragile environments, the decline of vegetation is done according to an alarming increase and wind erosion dominates. The ecosystem is subjected to a long hot dry season and low annual average rainfall. The urgency of the fight against desertification is imposed by the very nature of the process that tends to self-accelerate, resulting when human intervention is not forthcoming the irreversibility situations, preventing any possibility of restoration state of these zones. These phenomena have led to different degradation processes, such as the destruction of vegetation, soil erosion, and deterioration of the physical environment. In this study, the work is mainly based on the criteria for classification and identification of physical parameters for spatial analysis and multi-sources to determine the vulnerability of major steppe formations and their impact on desertification. we used Landsat data with two different dates March 2010 and November 2014 in order to determine the changes in land cover, sand moving and land degradation for the diagnosis of the desertification Phenomenon. The application, through specific processes, including the supervised classification was used to characterize the main steppe formations. An analysis of the vulnerability of plant communities was conducted to assign weights and identify areas most susceptible to desertification. Vegetation indices are used to characterize the steppe formations to determine changes in land use.

Keywords: remote sensing, SIG, ecosystem, degradation, desertification

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Authors: Sajee A. Gamage, Champa J. Wijesinghe, Patricia Burtner, Ananda R. Wickremasinghe

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Background: Teachers working in the context of special education have an enormous responsibility of enhancing performance skills of children in their classroom settings. Fine Motor Skills (FMS) are essential functional skills for children to gain independence in Activities of Daily Living. Children with Down Syndrome (DS) are predisposed to specific challenges due to deficits in FMS. This study is aimed to determine the teachers’ experience on improving FMS of children with DS in the context of special education of Southern Province, Sri Lanka. Methodology: A cross-sectional study was conducted among all consenting eligible teachers (n=147) working in the context of special education in government schools of Southern Province of Sri Lanka. A self-administered questionnaire was developed based on literature and expert opinion to assess teachers’ experience regarding deficits of FMS, limitations of classroom activity performance and barriers to improve FMS of children with DS. Results: Approximately 93% of the teachers were females with a mean age ( ± SD) of 43.1 ( ± 10.1) years. Thirty percent of the teachers had training in special educationand 83% had children with DS in their classrooms. Major deficits of FMS reported were deficits in grasping (n=116; 79%), in-hand manipulation (n=103; 70%) and bilateral hand use (n=99; 67.3%). Paperwork (n=70; 47.6%), painting (n=58; 39.5%), scissor work (n=50; 34.0%), pencil use for writing (n=45; 30.6%) and use of tools in the classroom (n=41; 27.9%) were identified as major classroom performance limitations of children with DS. Parental factors (n=67; 45.6%), disease specific characteristics (n=58; 39.5%) and classroom factors (n=36; 24.5%), were identified as major barriers to improve FMS in the classroom setting. Lack of resources and standard tools, social stigma and late school admission were also identified as barriers to FMS training. Eighty nine percent of the teachers informed that training fine motor activities in a special education classroom was more successful than work with normal classroom setting. Conclusion: Major areas of FMS deficits were grasping, in-hand manipulation and bilateral hand use; classroom performance limitations included paperwork, painting and scissor work of children with DS. Teachers recommended regular practice of fine motor activities according to individual need. Further research is required to design a culturally specific FMS assessment tool and intervention methods to improve FMS of children with DS in Sri Lanka.

Keywords: classroom activities, Down syndrome, experience, fine motor skills, special education, teachers

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Authors: Kwaku Damoah

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This paper presents a methodology and software application (App) designed to empower users in accessing, retrieving, and comparatively exploring data within the hierarchical network framework of the Government Finance Statistics (GFS) system. It explores the ease of navigating the GFS system and identifies the gaps filled by the new methodology and App. The GFS, embodies a complex Hierarchical Network Classification (HNC) structure, encapsulating institutional units, revenues, expenses, assets, liabilities, and economic activities. Navigating this structure demands specialized knowledge, experience, and skill, posing a significant challenge for effective analytics and fiscal policy decision-making. Many professionals encounter difficulties deciphering these classifications, hindering confident utilization of the system. This accessibility barrier obstructs a vast number of professionals, students, policymakers, and the public from leveraging the abundant data and information within the GFS. Leveraging R programming language, Data Science Analytics and Machine Learning, an efficient methodology enabling users to access, navigate, and conduct exploratory comparisons was developed. The machine learning Fiscal Analytics App (FLOWZZ) democratizes access to advanced analytics through its user-friendly interface, breaking down expertise barriers.

Keywords: data science, data wrangling, drilldown analytics, government finance statistics, hierarchical network classification, machine learning, web application.

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Authors: Nicolay Worren, Christopher A. Brown

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Axiomatic design (AD) has mainly been applied to support the design of physical products and software solutions. However, it was intended as a general design approach that would also be applicable to the design of social systems, including organizations (i.e., organization design). In this article, we consider how AD may be successfully transferred to the field of organizational design. On the one hand, it provides a much-needed pragmatic approach that can help leaders clarify the link between the purpose and structure of their organizations, identify ineffective organizational structures, and increase the chance of achieving strategic goals. On the other hand, there are four conceptual challenges that may create uncertainty and resistance among scholars and practitioners educated in the social sciences: 1) The exclusive focus in AD on negative interdependencies ('coupling'); 2) No obvious way of representing the need for integration across design parameters (DPs); 3) A lack of principles for handling control processes that seem to require 'deliberate coupling' of FRs; and 4) A lack of principles for handling situations where conflicting FRs (i.e., coupling) might require integration rather than separation. We discuss alternative options for handling these challenges so that scholars and practitioners can make use of AD for organization design.

Keywords: axiomatic design, organization design, social systems, concept definitions

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Authors: Paolo Russu

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This paper investigates the economic and ecological dynamics that emerge in Protected Areas (PAs) due to interactions between visitors and the animals that live there. The PAs contain two species whose interactions are determined by the Lotka-Volterra equations system. Visitors' decisions to visit PAs are influenced by the entrance cost required to enter the park and the chance of witnessing the species living there. Visitors have contradictory effects on the species and thus on the sustainability of the protected areas: on the one hand, an increase in the number of tourists damages the natural habitat of the regions and thus the species living there; on the other hand, it increases the total amount of entrance fees that the managing body of the PAs can use to perform defensive expenditures that protect the species from extinction. For a given set of parameter values, saddle-node bifurcation, Hopf bifurcation, homoclinic orbits, and a Bogdanov–Takens bifurcation of codimension two has been investigated. The system displays periodic doubling and chaotic solutions, as numerical examples demonstrate. Pontryagin's Maximum Principle was utilised to develop an optimal admission charge policy that maximised social gain and ecosystem conservation.

Keywords: chaos, bifurcation points, dynamical model, optimal control

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Authors: Gani Pllana

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In the conditions of the rapid development of technics and technology in recent years, the cooperation of the scientific-technical language with the standard Albanian language is continuing with a higher intensity than before. We notice a vigor of enrichment in the vocabulary of technical terminology, due to the birth and formation of new fields and subfields of technics, technology, as computing, mechatronics, telemetry, a multitude of concepts many of which, on the one hand, are marked with names of the languages they come from, mainly from English, but on the other hand, they meet their needs with the lexical mother tongue composition (by common words being raised to terms) and with the activation of other layers, such as compound word terms. Thus, for example, in the field of computing, we notice in it the inclusion of the ordinary vocabulary for reproductive reasons, like mi, dritare, flamur, adresë, skedar (Engl: mouse, window, flag, address, file), and along with them, the compound word terms, serving to differentiate relevant concepts, like, adresë e hiperlidhjes, adresë e uebit, adresë relative, adresë virtuale (Engl. address hyperlink, web address, relative address, virtual address) etc.

Keywords: common words, Albanian language, technical terminology, standardization

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Authors: H. P. Kerry

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Drone technology has become a significant discourse in a nation’s national security, while this technology could constitute a danger to national security on the one hand, on the other hand, it is used in developed and developing countries for border security, and in some cases, for protection of security agents and migrants. In the case of Nigeria, drones are used by the military to monitor and tighten security around the borders. However, terrorist groups have devised a means to utilize the technology to their advantage. Therefore, the potential danger in the widespread proliferation of this technology has become a myriad of risks. The research on the effects of cross-border use of drones in Nigerian national security looks at the negative and positive consequences of using drone technology. The study employs the use of interviews and relevant documents to obtain data while the study applied the Just War theory to justify the reason why countries use force; it further buttresses the points with what the realist theory thinks about the use of force. In conclusion, the paper recommends that the Nigerian government through the National Assembly should pass a bill for the establishment of a law that will guide the use of armed and unarmed drones in Nigeria enforced by the Nigeria Civil Aviation Authority and the office of the National Security Adviser.

Keywords: armed drones, drones, cross-border, national security

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Authors: Anitha Muralidhara, Victor Engelen, Christophe Len, Pascal Pandard, Guy Marlair

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Production of bio-based chemicals and materials from lignocellulosic biomass is gaining tremendous importance in advanced bio-refineries while aiming towards progressive replacement of petroleum based chemicals in transportation fuels and commodity polymers. One such attempt has resulted in the production of key furan derivatives (FD) such as furfural, HMF, MMF etc., via acid catalyzed dehydration (ACD) of C6 and C5 sugars, which are further converted into key chemicals or intermediates (such as Furandicarboxylic acid, Furfuryl alcohol etc.,). In subsequent processes, many high potential FD are produced, that can be converted into high added value polymers or high energy density biofuels. During ACD, an unavoidable polyfuranic byproduct is generated which is called humins. The family of FD is very large with varying chemical structures and diverse physicochemical properties. Accordingly, the associated risk profiles may largely vary. Hazardous Material (Haz-mat) classification systems such as GHS (CLP in the EU) and the UN TDG Model Regulations for transport of dangerous goods are one of the preliminary requirements for all chemicals for their appropriate classification, labelling, packaging, safe storage, and transportation. Considering the growing application routes of FD, it becomes important to notice the limited access to safety related information (safety data sheets available only for famous compounds such as HMF, furfural etc.,) in these internationally recognized haz-mat classification systems. However, these classifications do not necessarily provide information about the extent of risk involved when the chemical is used in any specific application. Factors such as thermal stability, speed of combustion, chemical incompatibilities, etc., can equally influence the safety profile of a compound, that are clearly out of the scope of any haz-mat classification system. Irrespective of the bio-based origin, FD has so far received inconsistent remarks concerning their toxicity profiles. With such inconsistencies, there is a fear that, a large family of FD may also follow extreme judgmental scenarios like ionic liquids, by ranking some compounds as extremely thermally stable, non-flammable, etc., Unless clarified, these messages could lead to misleading judgements while ranking the chemical based on its hazard rating. Safety is a key aspect in any sustainable biorefinery operation/facility, which is often underscored or neglected. To fill up these existing data gaps and to address ambiguities and discrepancies, the current study focuses on giving preliminary insights on safety assessment of FD and their potential targeted by-products. With the available information in the literature and obtained experimental results, physicochemical safety, environmental safety as well as (a scenario based) fire safety profiles of key FD, as well as side streams such as humins and levulinic acid, will be considered. With this, the study focuses on defining patterns and trends that gives coherent safety related information for existing and newly synthesized FD in the market for better functionality and sustainable applications.

Keywords: furanics, humins, safety, thermal and fire hazard, toxicity

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Authors: Dmitriy Babenko, Sergey Yegorov, Ilya Korshukov, Aidana Sultanbekova, Valentina Barkhanskaya, Tatiana Bashirova, Yerzhan Zhunusov, Yevgeniya Li, Viktoriya Parakhina, Svetlana Kolesnichenko, Yeldar Baiken, Aruzhan Pralieva, Zhibek Zhumadilova, Matthew S. Miller, Gonzalo H. Hortelano, Anar Turmuhambetova, Antonella E. Chesca, Irina Kadyrova

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Introduction: The rapidly evolving COVID-19 pandemic, along with the re-emergence of pathogens causing acute respiratory infections (ARI), has necessitated the development of novel diagnostic tools to differentiate various causes of ARI. MALDI-MS, due to its wide usage and affordability, has been proposed as a potential instrument for diagnosing SARS-CoV-2 versus non-SARS-CoV-2 ARI. The aim of this study was to investigate the potential of MALDI-MS in conjunction with a machine learning model to accurately distinguish between symptomatic infections caused by SARS-CoV-2 and non-SARS-CoV-2 during both the early and later phases of the pandemic. Furthermore, this study aimed to analyze mass spectrometry (MS) data obtained from nasal swabs of healthy individuals. Methods: We gathered mass spectra from 252 samples, comprising 108 SARS-CoV-2-positive samples obtained in 2020 (Covid 2020), 7 SARS-CoV- 2-positive samples obtained in 2023 (Covid 2023), 71 samples from symptomatic individuals without SARS-CoV-2 (Control non-Covid ARVI), and 66 samples from healthy individuals (Control healthy). All the samples were subjected to RT-PCR testing. For data analysis, we employed the caret R package to train and test seven machine-learning algorithms: C5.0, KNN, NB, RF, SVM-L, SVM-R, and XGBoost. We conducted a training process using a five-fold (outer) nested repeated (five times) ten-fold (inner) cross-validation with a randomized stratified splitting approach. Results: In this study, we utilized the Covid 2020 dataset as a case group and the non-Covid ARVI dataset as a control group to train and test various machine learning (ML) models. Among these models, XGBoost and SVM-R demonstrated the highest performance, with accuracy values of 0.97 [0.93, 0.97] and 0.95 [0.95; 0.97], specificity values of 0.86 [0.71; 0.93] and 0.86 [0.79; 0.87], and sensitivity values of 0.984 [0.984; 1.000] and 1.000 [0.968; 1.000], respectively. When examining the Covid 2023 dataset, the Naive Bayes model achieved the highest classification accuracy of 43%, while XGBoost and SVM-R achieved accuracies of 14%. For the healthy control dataset, the accuracy of the models ranged from 0.27 [0.24; 0.32] for k-nearest neighbors to 0.44 [0.41; 0.45] for the Support Vector Machine with a radial basis function kernel. Conclusion: Therefore, ML models trained on MALDI MS of nasopharyngeal swabs obtained from patients with Covid during the initial phase of the pandemic, as well as symptomatic non-Covid individuals, showed excellent classification performance, which aligns with the results of previous studies. However, when applied to swabs from healthy individuals and a limited sample of patients with Covid in the late phase of the pandemic, ML models exhibited lower classification accuracy.

Keywords: SARS-CoV-2, MALDI-TOF MS, ML models, nasopharyngeal swabs, classification

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Authors: Courret Gilles, Pidoux Damien

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Considering that sunshine is the ultimate reference in lighting, we have examined the spectral correlation between a series of electric light sources and sunlight. As the latter is marked by fluctuations, we have taken two spectra of reference: on the one hand, the CIE daylight standard illuminant, and on the other hand, the global illumination by the clear sky with the sun at 30° above the horizon. We determined the coefficients of correlation between the spectra filtered by the sensitivity of the CIE standard observer for photopic vision. We also calculated the luminous efficiency of the radiation in order to compare the ideal energy performances as well as the CIE color indexes Ra, Ra14, and Rf, since the choice of a light source requires a trade-off between color rendering and luminous efficiency. The benchmarking includes the most commonly used bulbs, various white LED (Lighting Emitting Diode) of warm white or cold white types, incandescent halogen as well as two HID lamps (High-Intensity Discharge) and two plasma lamps of different types, a solar simulator and a new version of the sulfur lamp. The latter obtains the best correlation, whether in comparison with the solar spectrum or that of the standard illuminant.

Keywords: electric light sources, plasma lamp, daylighting, sunlight, spectral correlation

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Authors: RAM PAL SINGH, VIKASH CHAUDHARY, MONIKA VERMA

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In this paper, we proposed the implementation of optimization based Extreme Learning Machine (ELM) for watermarking of B-channel of color image in discrete wavelet transform (DWT) domain. ELM, a regularization algorithm, works based on generalized single-hidden-layer feed-forward neural networks (SLFNs). However, hidden layer parameters, generally called feature mapping in context of ELM need not to be tuned every time. This paper shows the embedding and extraction processes of watermark with the help of ELM and results are compared with already used machine learning models for watermarking.Here, a cover image is divide into suitable numbers of non-overlapping blocks of required size and DWT is applied to each block to be transformed in low frequency sub-band domain. Basically, ELM gives a unified leaning platform with a feature mapping, that is, mapping between hidden layer and output layer of SLFNs, is tried for watermark embedding and extraction purpose in a cover image. Although ELM has widespread application right from binary classification, multiclass classification to regression and function estimation etc. Unlike SVM based algorithm which achieve suboptimal solution with high computational complexity, ELM can provide better generalization performance results with very small complexity. Efficacy of optimization method based ELM algorithm is measured by using quantitative and qualitative parameters on a watermarked image even though image is subjected to different types of geometrical and conventional attacks.

Keywords: BER, DWT, extreme leaning machine (ELM), PSNR

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Authors: Zachary Pan

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Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

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Authors: Kajal Zarei

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Supply chain management is becoming an increasingly important issue in many businesses today. In such circumstances, a number of reasons such as management deficiency in different segments of the supply chain, lack of streamlined processes, resistance to change the current systems and technologies, and lack of advanced information system have paved the ground to ask for innovative research studies. To this end, information technology (IT) is becoming a major driver to overcome the supply chain limitations and deficiencies. The emergence of IT has provided an excellent opportunity for redefining the supply chain to be more effective and competitive. This paper has investigated the IT impact on two-digit industry codes in the International Standard Industrial Classification (ISIC) that are operating in four groups of the supply chains. Firstly, the primary fields of the supply chain were investigated, and then paired comparisons of different industry parts were accomplished. Using experts' ideas and Analytical Hierarchy Process (AHP), the status of industrial activities in Kurdistan Province in Iran was determined. The results revealed that manufacturing and inventory fields have been more important compared to other fields of the supply chain. In addition, IT has had greater impact on food and beverage industry, chemical industry, wood industry, wood products, and production of basic metals. The results indicated the need to IT awareness in supply chain management; in other words, IT applications needed to be developed for the identified industries.

Keywords: supply chain, information technology, analytical hierarchy process, two-digit codes, international standard industrial classification

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Authors: Lalitchandra J. Shah, Katarzyna Bialek, Melinda L. Clarke, Jessica L. Jansson

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Handwriting is essential to academic success; however, the current literature is limited in the identification of pre-handwriting skills. The purpose of this study was to identify the pre-handwriting skills, which occupational therapy practitioners deem important to handwriting success, as well as those which aid in intervention planning. The online survey instrument consisted of 33 questions that assessed various skills related to the development of handwriting, as well as captured demographic information. Both occupational therapists and occupational therapy assistants were included in the survey study. The survey found that the respondents were in agreement that purposeful scribbling, the ability of a child to copy (vertical/horizontal lines, circle, squares, and triangles), imitating an oblique cross, cognitive skills (attention, praxis, self-regulation, sequencing), grasp patterns, hand dominance, in hand manipulation skills (shift, translation, rotation), bilateral integration, stabilization of paper, crossing midline, and visual perception were important indicators of handwriting readiness. The results of the survey support existing research regarding the skills necessary for the successful development of handwriting in children.

Keywords: development, handwriting, occupational therapy, visual perceptual skills

Procedia PDF Downloads 349