Search results for: human action classification

Authors: Iris Maria Velez Osorio

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There has been in the last decade a concern about the environment, particularly about clean and enough water for human consumption but, some enterprises had some trouble to understand the limited resources in the environment. This research tries to understand how some industries are better oriented to the preservation of the environment through investment for strategic management of scarce resources and try in the best way possible, the contaminants. It was made an industry classification since four different group of theories for Corporate Social Responsibility agree with variables of: investment in environmental care, water protection, and residues treatment finding different levels of commitment with CSR.

Keywords: corporate social responsibility, environment, strategic management, water

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

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Authors: Gaston Bacquet

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In light of existing social and structural violence in Chilean higher education which has resulted in instances of inequality, exclusion and discrimination, this research study attempted to provide trainee teachers in Chile with non-violent tools to deal with the tensions arising from these issues. Through a participatory action research design framed within a series of non-violent, non-Western perspectives, this study provided co-participants with opportunities to discuss current problems affecting HE in Chile stemming from the aforementioned violence, to think about strategies to address these and the challenges they might encounter. The study, which involved two different groups of trainee teachers from Chilean universities, consisted of two iterations of the action research cycle (planning – acting – observing – reflecting) carried out over two 3-months periods. Findings reveal instances of forced cultural assimilation, bullying, and direct and structural violence as key issues to address, and a strong individualistic mindset trumping collective well-being as the main challenge to bring non-violent strategies into their classrooms.

Keywords: non-violence education, contemplative pedagogy, participatory action research, dialogical education

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Authors: Jian-Xiu Wan, Yao Yin

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In complex terrain and poor geological conditions areas, Railway, highway and other transportation constructions are still strongly developing. However, various geological disasters happened such as landslide, rock heap and so on. According to the results of geological investigation, the form of skirt (trapezoidal), semicircle and triangle rock heaps are mainly due to complex internal force and external force, in a certain extent, which is related to the terrain, the nature of the rock mass, the supply area and the surface shape of rock heap. Combined with the above factors, discrete element numerical simulation of rock mass is established under different terrain conditions based on 3DEC, and accelerated formation process of rock heap under seismic action is simulated. The fragmentation structure supply area is calculated, in which the most dangerous area is located. At the same time, the formation mechanism and development process are studied in different terrain conditions, and the structure of rock heap is judged by section, which can provide a strong theoretical and technical support for the prevention and control of geological disasters.

Keywords: 3DEC, fragmentation structure, rock heap, slope, seismic action

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Authors: Miftahuddin Khilji

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The concept of human right is not alien to Islam. The Shari‘ah requires all its followers the sense of responsibility to perform their duties first and then claim their rights. This eventually guarantees the protection of human rights and ensures a peaceful society. The ultimate goal of Shari‘ah is to preserve five basic necessities which are also known as Maqasid ul Shari‘ah or Objectives of Islamic Law. This goal ensures for the members of society their rights without harming public welfare. Despite of the fact that human rights have been fully guaranteed by Islam and their compliance is required by Allah Almighty; not by any legislative body or other sovereign such as kings etc. However, many western writers, organizations and so called liberal thinkers try to create concerns, doubts and misconceptions in minds of the society members. A number of issues are pointed out and people are misguided about the concept of human rights in Islam. This paper aims to discuss main the concept of human rights in the light of perfect and balanced system of laws and principles of Shari‘ah and address those misconceptions and doubts by analyzing them and answering to questions raised about the subject. It would be an effort to prove that human rights are much more significant to Shari‘ah more than any other national or international legislative body.

Keywords: human rights, Islamic law, law, Shariah

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Authors: Ilzira A. Minigalieva, Tatiana V. Bushueva, Eleonore Frohlich, Vladimir Panov, Ekaterina Shishkina, Boris A. Katsnelson

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Background: The overwhelming majority of the experimental studies in the field of metal nanotoxicology have been performed on cultures of established cell lines, with very few researchers focusing on animal experiments, while a juxtaposition of conclusions inferred from these two types of research is blatantly lacking. The least studied aspect of this problem relates to characterizing and predicting the combined toxicity of metallic nanoparticles. Methods: Comparative and combined toxic effects of purposefully prepared spherical NiO and Mn₃O₄ nanoparticles (mean diameters 16.7 ± 8.2 nm and 18.4 ± 5.4 nm respectively) were estimated on cultures of human cell lines: MRC-5 fibroblasts, THP-1 monocytes, SY-SY5Y neuroblastoma cells, as well as on the latter two lines differentiated to macrophages and neurons, respectively. The combined cytotoxicity was mathematically modeled using the response surface methodology. Results: The comparative assessment of the studied NPs unspecific toxicity previously obtained in vivo was satisfactorily reproduced by the present in vitro tests. However, with respect to manganese-specific brain damage which had been demonstrated by us in animal experiment with the same NPs, the testing on neuronall cell culture showed only a certain enhancing effect of Mn₃O₄-NPs on the toxic action of NiO-NPs, while the role of the latter prevailed. Conclusion: From the point of view of the preventive toxicology, the experimental modeling of metallic NPs combined toxicity on cell cultures can give non-reliable predictions of the in vivo action’s effects.

Keywords: manganese oxide, nickel oxide, nanoparticles, in vitro toxicity

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Authors: Jesús Horacio, Alfredo Ollero, Víctor Bouzas-Blanco, Augusto Pérez-Alberti

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Rivers have traditionally been classified, assessed and managed in terms of hydrological, chemical and / or biological criteria. Geomorphological classifications had in the past a secondary role, although proposals like River Styles Framework, Catchment Baseline Survey or Stroud Rural Sustainable Drainage Project did incorporate geomorphology for management decision-making. In recent years many studies have been attracted to the geomorphological component. The geomorphological processes and their associated forms determine the structure of a river system. Understanding these processes and forms is a critical component of the sustainable rehabilitation of aquatic ecosystems. The fluvial auto-classification approach suggests that a river is a self-built natural system, with processes and forms designed to effectively preserve their ecological function (hydrologic, sedimentological and biological regime). Fluvial systems are formed by a wide range of elements with multiple non-linear interactions on different spatial and temporal scales. Besides, the fluvial auto-classification concept is built using data from the river itself, so that each classification developed is peculiar to the river studied. The variables used in the classification are specific stream power and mean grain size. A discriminant analysis showed that these variables are the best characterized processes and forms. The statistical technique applied allows to get an individual discriminant equation for each geomorphological type. The geomorphological classification was developed using sites with high naturalness. Each site is a control point of high ecological and geomorphological quality. The changes in the conditions of the control points will be quickly recognizable, and easy to apply a right management measures to recover the geomorphological type. The study focused on Galicia (NW Spain) and the mapping was made analyzing 122 control points (sites) distributed over eight river basins. In sum, this study provides a method for fluvial geomorphological classification that works as an open and flexible tool underlying the fluvial auto-classification concept. The hydrographic mapping is the visual expression of the results, such that each river has a particular map according to its geomorphological characteristics. Each geomorphological type is represented by a particular type of hydraulic geometry (channel width, width-depth ratio, hydraulic radius, etc.). An alteration of this geometry is indicative of a geomorphological disturbance (whether natural or anthropogenic). Hydrographic mapping is also dynamic because its meaning changes if there is a modification in the specific stream power and/or the mean grain size, that is, in the value of their equations. The researcher has to check annually some of the control points. This procedure allows to monitor the geomorphology quality of the rivers and to see if there are any alterations. The maps are useful to researchers and managers, especially for conservation work and river restoration.

Keywords: fluvial auto-classification concept, mapping, geomorphology, river

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Authors: Najm Alotaibi

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Human motion capture has become one of the major area of interest in the field of computer vision. Some of the major application areas that have been rapidly evolving include the advanced human interfaces, virtual reality and security/surveillance systems. This study provides a brief overview of the techniques and applications used for the markerless human motion capture, which deals with analyzing the human motion in the form of mathematical formulations. The major contribution of this research is that it classifies the computer vision based techniques of human motion capture based on the taxonomy, and then breaks its down into four systematically different categories of tracking, initialization, pose estimation and recognition. The detailed descriptions and the relationships descriptions are given for the techniques of tracking and pose estimation. The subcategories of each process are further described. Various hypotheses have been used by the researchers in this domain are surveyed and the evolution of these techniques have been explained. It has been concluded in the survey that most researchers have focused on using the mathematical body models for the markerless motion capture.

Keywords: human motion capture, computer vision, vision-based, tracking

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Authors: Kashika P. H.

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The development of computer vision systems has been greatly aided by the efficient and precise detection of images and videos. Although the ability to recognize and comprehend images is a strength of the human brain, employing technology to tackle this issue is exceedingly challenging. In the past few years, the use of Deep Learning algorithms to treat object detection has dramatically expanded. One of the key issues in the realm of image recognition is the recognition and detection of certain notable people from randomly acquired photographs. Face recognition uses a way to identify, assess, and compare faces for a variety of purposes, including user identification, user counting, and classification. With the aid of an accessible deep learning-based API, this article intends to recognize various faces of people and their facial descriptors more accurately. The purpose of this study is to locate suitable individuals and deliver accurate information about them by using the Amazon Rekognition system to identify a specific human from a vast image dataset. We have chosen the Amazon Rekognition system, which allows for more accurate face analysis, face comparison, and face search, to tackle this difficulty.

Keywords: Amazon rekognition, API, deep learning, computer vision, face detection, text detection

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Authors: A. Deswal, N. S. Deora, H. N. Mishra

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The aim of the present study was to develop a rapid method for electronic nose for online quality control of oat milk. Analysis by electronic nose and bacteriological measurements were performed to analyse spoilage kinetics of oat milk samples stored at room temperature and refrigerated conditions for up to 15 days. Principal component analysis (PCA), discriminant factorial analysis (DFA) and soft independent modelling by class analogy (SIMCA) classification techniques were used to differentiate the samples of oat milk at different days. The total plate count (bacteriological method) was selected as the reference method to consistently train the electronic nose system. The e-nose was able to differentiate between the oat milk samples of varying microbial load. The results obtained by the bacteria total viable counts showed that the shelf-life of oat milk stored at room temperature and refrigerated conditions were 20 hours and 13 days, respectively. The models built classified oat milk samples based on the total microbial population into “unspoiled” and “spoiled”.

Keywords: electronic-nose, bacteriological, shelf-life, classification

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Authors: Mohammad Motiee

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From Kierkegaard to Sartre, the concept of 'being' was debated over various angles in the philosophy of being. Then, the futility, nothingness and absurdity of human condition in the world were all justified and led to a kind of solution by different approaches like Christianity, loss of faith, authentic existence and responsibility. In an extreme concern, the human condition in the world was pondered in different ways and the philosophy of thought tried to render an awareness of such condition for human beings. The present study aims at illustration of some approaches presented by prominent existentialists to justify the controversies in the concept of existence in human life.

Keywords: existence, existentialism, alienation, being

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Authors: Modi Alsubaie, Willem Kuyken, Rebecca Abbott, Barnaby Dunn, Chris Dickens, Tina Keil, William Henley

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Background: Recently, there has been an increased interest in studying the effects of mindfulness-based interventions for people with psychological and physical problems. However, the mechanisms of action in these interventions that lead to beneficial physical and psychological outcomes have yet to be clearly identified. Purpose: The aim of this paper is to review, systematically, the evidence to date on the mechanisms of action in mindfulness interventions in populations with physical and/or psychological conditions. Method: Searches of seven databases (PsycINFO, Medline (Ovid), Cochrane Central Register of Controlled Trials, EMBASE, CINAHL, AMED, ClinicalTrials.gov) were undertaken in June 2014 and July 2015. We evaluated to what extent the studies we identified met the criteria suggested by Kazdin for establishing mechanisms of action within a psychological treatment (2007, 2009). Results: We identified four trials examining mechanisms of mindfulness interventions in those with comorbid psychological and physical health problems and 14 in those with psychological conditions. These studies examined a diverse range of potential mechanisms, including mindfulness and rumination. Of these candidate mechanisms, the most consistent finding was that greater self-reported change in mindfulness mediated superior clinical outcomes. However, very few studies fully met the Kazdin criteria for examining treatment mechanisms. Conclusion: There was evidence that global changes in mindfulness are linked to better outcomes. This evidence pertained more to interventions targeting psychological rather than physical health conditions. While there is promising evidence that MBCT/MBSR intervention effects are mediated by hypothesised mechanisms, there is a lack of methodological rigour in the field of testing mechanisms of action for both MBCT and MBSR, which precludes definitive conclusions.

Keywords: MBCT, MBSR, mechanisms, physical conditions, psychological conditions, systematic review

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Authors: Reza Shakoori

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This paper presents a structural correction of Arabic and Persian strokes using manipulation of their chain codes in order to improve the rate and performance of Persian and Arabic handwritten word recognition systems. It collects pure and effective features to represent a character with one consolidated feature vector and reduces variations in order to decrease the number of training samples and increase the chance of successful classification. Our results also show that how the proposed approaches can simplify classification and consequently recognition by reducing variations and possible noises on the chain code by keeping orientation of characters and their backbone structures.

Keywords: Arabic, chain code normalization, OCR systems, image processing

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

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

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

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Authors: Tamanda Kamwendo

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The concept of benefit sharing has been a prominent global debate in the world, gaining traction in human research ethics. Despite its prevalence, the concept of benefit sharing is not without controversy over its meaning and justification. This is due to the fact that it lacks a broadly accepted definition and many proponents discuss benefit sharing by arguing for its necessity rather than engaging in critical intellectual engagement with technical issues such as what it implies. What is clear in the literature is that the underlying premise of benefit-sharing is that research involving underprivileged and marginalized people is currently unjust and inequitable because these people are denied access to these gains; thus, benefit-sharing arrangements are required for these research projects to be just and equitable. This paper, therefore, investigates the discourses and justifications behind the concept of benefit sharing to human participants, particularly when dealing with human genomics research. Furthermore, considering that benefit sharing is generally viewed as a transaction between research organizations and research participants, it raises ethical concerns concerning the commodification of human material and undermines the sanctity of the human genome. This is predicated on the idea that research sponsors would be compelled to deliver a minimum set of possible benefits to research participants and communities in exchange for their involvement in the study. There is, therefore, need to protect benefit-sharing practices in international health research by developing a governance legal framework. A legal framework of benefit sharing will also dispel the issue of commodification of human material where human genomic research is done.

Keywords: benefit sharing, human participants, human genomic research, ethical concerns

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Authors: Rohit Gupta, Ravinder Agarwal

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Surface electromyographic (sEMG) signal has the potential to identify the human activities and intention. This potential is further exploited to control the artificial limbs using the sEMG signal from residual limbs of amputees. The paper deals with the development of multichannel cost efficient sEMG signal interface for research application, along with evaluation of proposed class dependent statistical approach of the feature selection method. The sEMG signal acquisition interface was developed using ADS1298 of Texas Instruments, which is a front-end interface integrated circuit for ECG application. Further, the sEMG signal is recorded from two lower limb muscles for three locomotions namely: Plane Walk (PW), Stair Ascending (SA), Stair Descending (SD). A class dependent statistical approach is proposed for feature selection and also its performance is compared with 12 preexisting feature vectors. To make the study more extensive, performance of five different types of classifiers are compared. The outcome of the current piece of work proves the suitability of the proposed feature selection algorithm for locomotion recognition, as compared to other existing feature vectors. The SVM Classifier is found as the outperformed classifier among compared classifiers with an average recognition accuracy of 97.40%. Feature vector selection emerges as the most dominant factor affecting the classification performance as it holds 51.51% of the total variance in classification accuracy. The results demonstrate the potentials of the developed sEMG signal acquisition interface along with the proposed feature selection algorithm.

Keywords: classifiers, feature selection, locomotion, sEMG

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Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Azin Karami, Bahareh Heydari

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There is a difference between national sovereignty ( national security guarantee) and human rights standards (human security guarantee). Under the pretext of providing security for the majority, Governments violate human rights standards and lead to populism. This paper illustrates despite the human rights standards of non-citizens, they mostly confront different practical and social realities. (a large gap between the reality and the truth). This paper has focused on one of vulnerable irregular non-citizens immigrants from Mediterranean . In addition, it has considered challenges of the basic and primary human rights standards of this group. It shows how government policies affect the flow of irregular immigration. This paper is based upon UN data about Mediterranean immigrants and polls answered by 68 people who intended to migrate from Mediterranean (28 female and 40 male people, the average age of 30 to 40). The model is supposed to be a convenient one to present objective, real evidence of irregular immigrants and discusses the challenges that this group of immigrants confront them .This paper shows clear concept of immigrants.

Keywords: human rights, human security, national sovereignty, irregular immigrants

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Authors: Ahmed El-Banbi

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Several PVT models exist to represent how PVT properties are handled in sub-surface and surface engineering calculations for oil and gas production. The most commonly used models include black oil, modified black oil (MBO), and compositional models. These models are used in calculations that allow engineers to optimize and forecast well and reservoir performance (e.g., reservoir simulation calculations, material balance, nodal analysis, surface facilities, etc.). The choice of which model is dependent on fluid type and the production process (e.g., depletion, water injection, gas injection, etc.). Based on close to 2,000 reservoir fluid samples collected from different basins and locations, this paper presents some conclusions on the occurrence of reservoir fluids. It also reviews the common methods used to classify reservoir fluid types. Based on new criteria related to the production behavior of different fluids and economic considerations, an updated classification of reservoir fluid types is presented in the paper. Recommendations on the use of different PVT models to simulate the behavior of different reservoir fluid types are discussed. Each PVT model requirement is highlighted. Available methods for the calculation of PVT properties from each model are also discussed. Practical recommendations and tips on how to control the calculations to achieve the most accurate results are given.

Keywords: PVT models, fluid types, PVT properties, fluids classification

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Authors: Asma A. Alsufyani, Maram A. Alharthi, Maha J. Althobaiti, Manal S. Alharthi, Huda Rizq

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Twitter is one of the most popular microblogging sites that allows users to publish short text messages called 'tweets'. Increasing the number of accounts to follow (followings) increases the number of tweets that will be displayed from different topics in an unclassified manner in the timeline of the user. Therefore, it can be a vital solution for many Twitter users to have their tweets in a timeline classified into general categories to save the user’s time and to provide easy and quick access to tweets based on topics. In this paper, we developed a classifier for timeline tweets trained on a dataset consisting of 3600 tweets in total, which were collected from Saudi Twitter and annotated manually. We experimented with the well-known Bag-of-Words approach to text classification, and we used support vector machines (SVM) in the training process. The trained classifier performed well on a test dataset, with an average F1-measure equal to 92.3%. The classifier has been integrated into an application, which practically proved the classifier’s ability to classify timeline tweets of the user.

Keywords: corpus creation, feature extraction, machine learning, short text classification, social media, support vector machine, Twitter

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Authors: Young Nam Jo, Moon Jeong Kang, Hong Hee Yoo

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Most human vibration models have been modeled as a multibody system consisting of some rigid bodies and spring-dampers. These models are developed for certain posture and conditions. So, the models cannot be used in vibration analysis in various posture and conditions. The purpose of this study is to develop a human vibration model that represent human vibration characteristics under various conditions by employing a musculoskeletal model. To do this, the human vibration model is developed based on biomechanical models. In addition, muscle models are employed instead of spring-dampers. Activations of muscles are controlled by PD controller to maintain body posture under vertical vibration is applied. Each gain value of the controller is obtained to minimize the difference of apparent mass and acceleration transmissibility between experim ent and analysis by using an optimization method.

Keywords: human vibration analysis, hill type muscle model, PD control, whole-body vibration

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Authors: Shokoufeh Abrisham, Seyed Mahmoud Hossieni, Elham Pishbin

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In this paper, a hybrid method based on the fault tree analysis (FTA) and Bayesian networks (BNs) are employed to evaluate the team performance quality of advanced cardiac life support (ACLS) procedures in emergency department. According to American Heart Association (AHA) guidelines, a category relying on staff action leading to clinical incidents and also some discussions with emergency medicine experts, a fault tree model for ACLS procedure is obtained based on the human performance. The obtained FTA model is converted into BNs, and some different scenarios are defined to demonstrate the efficiency and flexibility of the presented model of BNs. Also, a sensitivity analysis is conducted to indicate the effects of team leader presence and uncertainty knowledge of experts on the quality of ACLS. The proposed model based on BNs shows that how the results of risk analysis can be closed to reality comparing to the obtained results based on only FTA in medical procedures.

Keywords: advanced cardiac life support, fault tree analysis, Bayesian belief networks, numan performance, healthcare systems

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Authors: Seyhan Karaçavuş, Bülent Yılmaz, Ömer Kayaaltı, Semra İçer, Arzu Taşdemir, Oğuzhan Ayyıldız, Kübra Eset, Eser Kaya

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In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.

Keywords: cancer stage, cancer cell type, non-small cell lung carcinoma, PET, texture analysis

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Authors: Jacinta Musyoka, Wesley Too

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Cervical cancer is the second leading cause of cancer-related deaths in Kenya and the second most common cancer among women, yet preventable following prevention strategies put in place, which includes vaccination with Human Papilloma Virus Vaccine (HPPV) among the young adolescent girls. Kenya has the highest burden of cervical cancer and the leading cause of death among women of reproductive age and is a known frequent type of cancer amongst women. This is expected to double by 2025 if the necessary steps are not taken, which include vaccinating girls between the ages of 9 and 14 and screening women. Parental decision is critical in ensuring that their daughters receive this vaccine. Hence this study sought to establish parental willingness and factors associate with the acceptability to vaccine adolescent daughters against the human papilloma virus for cervical cancer prevention in Machakos County, Eastern Region of Kenya. Method: Cross-sectional study design utilizing a mixed methods approach was used to collect data from Nguluni Health Centre in Machakos County; Matungulu sub-county, Kenya. This study targeted all parents of adolescent girls seeking health care services in the Matungulu sub-county area who were aged 18 years and above. A total of 220 parents with adolescent girls aged 10-14 years were enrolled into the study after informed consent were sought. All ethical considerations were observed. Quantitative data were analyzed using Multivariate regression analysis, and thematic analysis was used for qualitative data related to perceptions of parents on HPVV. Results, conclusions, and recommendations- ongoing. We expect to report findings and articulate contributions based on the study findings in due course before October 2022

Keywords: adolescents, human papilloma virus, kenya, parents

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Authors: Eiman Kattan, Hong Wei

Abstract:

In using Convolutional Neural Network (CNN) for classification, there is a set of hyperparameters available for the configuration purpose. This study aims to evaluate the impact of a range of parameters in CNN architecture i.e. AlexNet on land cover classification based on four remotely sensed datasets. The evaluation tests the influence of a set of hyperparameters on the classification performance. The parameters concerned are epoch values, batch size, and convolutional filter size against input image size. Thus, a set of experiments were conducted to specify the effectiveness of the selected parameters using two implementing approaches, named pertained and fine-tuned. We first explore the number of epochs under several selected batch size values (32, 64, 128 and 200). The impact of kernel size of convolutional filters (1, 3, 5, 7, 10, 15, 20, 25 and 30) was evaluated against the image size under testing (64, 96, 128, 180 and 224), which gave us insight of the relationship between the size of convolutional filters and image size. To generalise the validation, four remote sensing datasets, AID, RSD, UCMerced and RSCCN, which have different land covers and are publicly available, were used in the experiments. These datasets have a wide diversity of input data, such as number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in both training and testing. The results have shown that increasing the number of epochs leads to a higher accuracy rate, as expected. However, the convergence state is highly related to datasets. For the batch size evaluation, it has shown that a larger batch size slightly decreases the classification accuracy compared to a small batch size. For example, selecting the value 32 as the batch size on the RSCCN dataset achieves the accuracy rate of 90.34 % at the 11th epoch while decreasing the epoch value to one makes the accuracy rate drop to 74%. On the other extreme, setting an increased value of batch size to 200 decreases the accuracy rate at the 11th epoch is 86.5%, and 63% when using one epoch only. On the other hand, selecting the kernel size is loosely related to data set. From a practical point of view, the filter size 20 produces 70.4286%. The last performed image size experiment shows a dependency in the accuracy improvement. However, an expensive performance gain had been noticed. The represented conclusion opens the opportunities toward a better classification performance in various applications such as planetary remote sensing.

Keywords: CNNs, hyperparamters, remote sensing, land cover, land use

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Authors: Ernest Yeboah Acheampong, Malek Bouhaouala, Michel Raspaud

Abstract:

Studies on African sport workers’ migration have given less attention to examining the extent to which the individual (sports workers) contributes to a socio-economic development of their domestic communities. The decision to support or not to support can also have a debilitating effect on the domestic communities. This article therefore, analyses the choices of action of these actors with an exact focus on footballers to the domestic community. This exploratory survey focuses on 13 UEFA countries leagues of footballers from 43 African countries, including seventeen interviews and four autobiographies of the players. Max Weber theory of individual subjectivity can underpin their decisions making processes to either offer assistance or not to their locales. This study revealed some players closed relationships, particularly those raised in the typical locales as they often provide support via projects like building hospitals, schools, sporting facilities, health centres, and scholarship schemes among others. While others shown commitment and readiness to offer assistance, touch livelihood, and promote social development based on their lived experiences abroad. With many lamenting against lack of support from local and national authorities as disincentive to do more yet committed to the cause of the society. This article can conclude that football athletes logics of action depend on the individual values and conceptions from evidence of their socio-economic projects, as well as social embeddedness in the locality

Keywords: choices of action, domestic development, footballers, transnational migration

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Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

Abstract:

This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

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Authors: Altilio Rosa, Chirico Francesco, Foglia Goffredo

Abstract:

In recent years, the number of Unmanned Aerial Vehicles (UAVs) has significantly increased. The rapid development of commercial and recreational drones makes them an important part of our society. Despite the growing list of their applications, these vehicles pose a huge threat to civil and military installations: detection, classification and neutralization of such flying objects become an urgent need. Radar is an effective remote sensing tool for detecting and tracking flying objects, but scenarios characterized by the presence of a high number of tracks related to flying birds make especially challenging the drone detection task: operator PPI is cluttered with a huge number of potential threats and his reaction time can be severely affected. Flying birds compared to UAVs show similar velocity, RADAR cross-section and, in general, similar characteristics. Building from the absence of a single feature that is able to distinguish UAVs and birds, this paper uses a multiple features approach where an original feature selection technique is developed to feed binary classifiers trained to distinguish birds and UAVs. RADAR tracks acquired on the field and related to different UAVs and birds performing various trajectories were used to extract specifically designed target movement-related features based on velocity, trajectory and signal strength. An optimization strategy based on a genetic algorithm is also introduced to select the optimal subset of features and to estimate the performance of several classification algorithms (Neural network, SVM, Logistic regression…) both in terms of the number of selected features and misclassification error. Results show that the proposed methods are able to reduce the dimension of the data space and to remove almost all non-drone false targets with a suitable classification accuracy (higher than 95%).

Keywords: birds, classification, machine learning, UAVs

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Authors: Adeleke Omolade

Abstract:

The study investigated the impacts of capital goods' import and human capital on the economic growth of the Sub Sahahra Africa (SSA). 30 countries were used in the Panel- ARDL analysis where economic growth is the dependent variables and capital goods' import, human capital, primary export, investment exchange rate, among others were used as the independent variables. The result from the panel analysis indicates that capital goods' import will significantly and positively influence economic growth but human capital fails to have significant positive impact on economic growth of the SSA. Earlier the trend analysis and the correlation results have shown that there is a weak association between capital goods' import and human capital in the SSA. The results offer an expository analysis that reveals that the quality of the human capital is very germane to the effective utilization of capital goods' import for the purpose of growth in a primary goods' export dominated region like the SSA.

Keywords: capital goods import, economic growth, human capital, Sub-Sahara Africa

Procedia PDF Downloads 214