Search results for: human action classification

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: Eiman Kattan, Hong Wei

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

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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

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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: Tahmina Huq

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Action research equips teachers with the skills needed to work on a particular situation in their classroom. This paper aims to introduce a strategy, visual dialogue between student and teacher, used by the researcher to help autistic students with intellectual disabilities to regulate their immediate emotions to achieve their academic goals. This research has been conducted to determine whether teaching self-regulation strategies can be effective instead of segregating them. The researcher has identified that visual dialogue between the student and teacher is a helpful technique for teaching self-regulation. For this particular research, action research suits the purpose as the findings can be applied immediately in the classroom. Like many autistic students, the teacher had two 15 years old autistic students with intellectual disabilities in class who had difficulty in controlling their emotions and impulses. They expressed their emotions through aggressive behavior, such as shouting, screaming, biting teachers or any adult who was in their sight, and destroying school property. They needed two to four hours to recover from their meltdowns with the help of a psychologist. The students missed the classes as they were often isolated from the classroom and stayed in the calming room until they calmed down. This negatively affected their learning. Therefore, the researcher decided to implement a self-regulation strategy, a visual dialogue between students and teachers, instead of isolating them to recover from the meltdown. The data was collected through personal observations, a log sheet, personal reflections, and pictures. The result shows that the students can regulate their emotions shortly in the classroom (15 to 30 minutes). Through visual dialogue, they can express their feelings and needs in socially appropriate ways. The finding indicates that autistic students can regulate their emotions through visual dialogues and participate in activities by staying in the classroom. Thus it positively impacted their learning and social lives. In this paper, the researcher discussed the findings of exploring how teachers can successfully implement a self-regulation strategy for autistic students in classroom settings. The action research describes the strategy that has been found effective for managing the emotions of autistic students with intellectual disabilities.

Keywords: action research, self-regulation, autism, visual communication

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Authors: Tamas Madl

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Sudden cardiac death (SCD) constitutes a large proportion of cardiovascular mortalities, provides little advance warning, and the risk is difficult to recognize based on ubiquitous, low cost medical equipment such as the standard, 12-lead, ten second ECG. Autonomic abnormalities have been shown to be strongly predictive of SCD risk; yet current methods are not trivially applicable to the brevity and low temporal and electrical resolution of standard ECGs. Here, we build horizontal visibility graph representations of very short inter-beat interval time series, and perform unsuper- vised representation learning in order to convert these variable size objects into fixed-length vectors preserving similarity rela- tions. We show that such representations facilitate classification into healthy vs. at-risk patients on two different datasets, the Mul- tiparameter Intelligent Monitoring in Intensive Care II and the PhysioNet Sudden Cardiac Death Holter Database. Our results suggest that graph representation learning of heartbeat interval time series facilitates robust classification even in sequences as short as ten seconds.

Keywords: sudden cardiac death, heart rate variability, ECG analysis, time series classification

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Authors: Pam Bitrus Marcus

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Compounds in Berom, a Niger-Congo language that is spoken in parts of central Nigeria, have been understudied, and the semantics of N-V nominal compounds have not been sufficiently delineated. This study describes the lexical classification of compounds in Berom and, specifically, examines the semantics of nominal compounds with N-V constituents. The study relied on a data set of 200 compounds that were drawn from Bere Naha (a newsletter publication in Berom). Contrary to the nominalization process in defining the lexical class of compounds in languages, the study revealed that verbal and adjectival classes of compounds are also attested in Berom and N-V nominal compounds have an agentive or locative interpretation that is not solely determined by the meaning of the constituents of the compound but by the context of the usage.

Keywords: berom, berom compounds, nominal compound, N-V compounds

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Authors: Hamidreza Fallah Lajimi, Elham Karami, Alireza Arab, Fatemeh Alinasab

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Being responsive is an increasingly important skill for firms in today’s global economy; thus firms must be agile. Naturally, it follows that an organization’s agility depends on its supply chain being agile. However, achieving supply chain agility is a function of other abilities within the organization. This paper analyses results from a survey of 71 Iran manufacturing companies in order to identify some of the factors for agile organizations in managing their supply chains. Then we classification this company in four cluster with fuzzy c-mean technique and with Four validations functional determine automatically the optimal number of clusters.

Keywords: agile supply chain, clustering, fuzzy clustering, business engineering

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Authors: Adeleke Omolade

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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

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Authors: Himanshu Dehra

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This paper presents a paradigm for characterization and checking of human noise behavior. The definitions of ‘Noise’ and ‘Noise Behavior’ are devised. The concept of characterization and examining of Noise Behavior is obtained from the proposed paradigm of Psychoacoustics. The measurement of human noise behavior is discussed through definitions of noise sources and noise measurements. The noise sources, noise measurement equations and noise filters are further illustrated through examples. The theory and significance of solar energy acoustics is presented for life and its activities. Human comfort and health are correlated with human brain through physiological responses and noise protection. Examples of heat stress, intense heat, sweating and evaporation are also enumerated.

Keywords: human brain, noise behavior, noise characterization, noise filters, physiological responses, psychoacoustics

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Authors: Ouerchefani Dalel, Mahdhaoui Basma

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Land use change is one of the important factors needed to evaluate later on the impact of human actions on land degradation. This work present the application of a methodology based on remote sensing for evaluation land use change in an arid region of Tunisia. This methodology uses Landsat TM and ETM+ images to produce land use maps by supervised classification based on ground truth region of interests. This study showed that it was possible to rely on radiometric values of the pixels to define each land use class in the field. It was also possible to generate 3 land use classes of the same study area between 1988 and 2011.

Keywords: land use, change, remote sensing, GIS

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Authors: Belayneh Matebie, Michael Melese

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The study focuses on addressing the challenges in classifying and ensuring the quality of Eragrostis Teff, a small and round grain that is the smallest cereal grain. Employing a traditional classification method is challenging because of its small size and the similarity of its environmental characteristics. To overcome this, this study employs a machine learning approach to develop a source and quality classification system for Teff cereal. Data is collected from various production areas in the Amhara regions, considering two types of cereal (high and low quality) across eight classes. A total of 5,920 images are collected, with 740 images for each class. Image enhancement techniques, including scaling, data augmentation, histogram equalization, and noise removal, are applied to preprocess the data. Convolutional Neural Network (CNN) is then used to extract relevant features and reduce dimensionality. The dataset is split into 80% for training and 20% for testing. Different classifiers, including FVGG16, FINCV3, QSCTC, EMQSCTC, SVM, and RF, are employed for classification, achieving accuracy rates ranging from 86.91% to 97.72%. The ensemble of FVGG16, FINCV3, and QSCTC using the Max-Voting approach outperforms individual algorithms.

Keywords: Teff, ensemble learning, max-voting, CNN, SVM, RF

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Authors: Agnieszka Leszczynska

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Flexibility and agility constitute the most dominant features of modern human resource management systems. The former pertains to procedures, practices and competences of human resources, and the latter to the procedures and practices’ effectiveness in dealing with changing conditions in the surrounding environment. The purpose of the paper is to present the relations between the flexibility and agility of human resources and achieving sustainable competitiveness. Based upon hitherto research, we develop a conceptual model that links the constructs together. The conducted study is of theoretical and conceptual nature. Critical literature analysis and the synthesis method were applied. A premise was made that the three dimensions of HR (Human Resources) flexibility (employee skill flexibility, employee behaviour flexibility, and HR practice flexibility) and HR agility affect competitiveness, by increasing the flexibility, creativity of human resources, and improving quality performance, and exert an impact upon the quality of life of employees and social relations. In particular, the agility and flexibility of human resources contribute to the growth of adaptability and strategic orientation, which directly affects the organization's competitiveness. The research results will help to better understand the impact of flexibility and agility related to the HRM (Human Resources Management) system upon the implementation of the concept of sustainable development in the organization.

Keywords: agility, human resource, sustainable competitiveness, sustainable development

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Authors: Yoshinori Wakatake

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Many defects after the release of software packages are caused due to omissions of sufficient test items in test specifications. Poor test specifications are detected by manual review, which imposes a high human load. The prevention of omissions depends on the end-user awareness of test specification writers. If test specifications were written while envisioning the behavior of end-users, the number of omissions in test items would be greatly reduced. The paper pays attention to the point that writers who can achieve it differ from those who cannot in not only the description richness but also their gaze information. It proposes a method to estimate the degree of user-awareness of writers through the analysis of their gaze information when writing test specifications. We conduct an experiment to obtain the gaze information of a writer of the test specifications. Test specifications are automatically classified using gaze information. In this method, a Random Forest model is constructed for the classification. The classification is highly accurate. By looking at the explanatory variables which turn out to be important variables, we know behavioral features to distinguish test specifications of high quality from others. It is confirmed they are pupil diameter size and the number and the duration of blinks. The paper also investigates test specifications automatically classified with gaze information to discuss features in their writing ways in each quality level. The proposed method enables us to automatically classify test specifications. It also prevents test item omissions, because it reveals writing features that test specifications of high quality should satisfy.

Keywords: blink, eye tracking, gaze information, pupil diameter, quality improvement, specification document, user-awareness

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Authors: Mohd Iz'aan Paiz Bin Zamri, Anis Salwa Mohd Khairuddin, Norrima Mokhtar, Rubiyah Yusof

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Effective statistical feature extraction and classification are important in image-based automatic inspection and analysis. An automatic wood species recognition system is designed to perform wood inspection at custom checkpoints to avoid mislabeling of timber which will results to loss of income to the timber industry. The system focuses on analyzing the statistical pores properties of the wood images. This paper proposed a fuzzy-based feature extractor which mimics the experts’ knowledge on wood texture to extract the properties of pores distribution from the wood surface texture. The proposed feature extractor consists of two steps namely pores extraction and fuzzy pores management. The total number of statistical features extracted from each wood image is 38 features. Then, a backpropagation neural network is used to classify the wood species based on the statistical features. A comprehensive set of experiments on a database composed of 5200 macroscopic images from 52 tropical wood species was used to evaluate the performance of the proposed feature extractor. The advantage of the proposed feature extraction technique is that it mimics the experts’ interpretation on wood texture which allows human involvement when analyzing the wood texture. Experimental results show the efficiency of the proposed method.

Keywords: classification, feature extraction, fuzzy, inspection system, image analysis, macroscopic images

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Authors: Xin Chen

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Pre-industrial old-world human cultural diversity and societal complexity exhibits remarkable geographic regularities. Along the latitudinal axis from the equator to the arctic, a descending trend of human ethno-cultural diversity is found to be in coincidence with a descending trend of biological diversity. Along the same latitudinal axis, the pre-industrial human societal complexity shows to peak at the intermediate latitude. It is postulated that human cultural diversity and societal complexity are strongly influenced by collective learning, and that collective learning is positively related to human population size, social interactions, and environmental challenges. Under such postulations the relationship between collective learning and important geographical-environmental factors, including climate and biodiversity/bio-productivity is examined. A hypothesis of intermediate bio-productivity is formulated to account for those latitudinal patterns of pre-industrial human societal complexity.

Keywords: cultural diversity, soetal complexity, latitudinal patterns, biodiversity, bio-productivity, collective learning

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Authors: Santanu Chattopadhyay, Gautam Sarkar, Arabinda Das

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This paper presents wavelet based classification of various heart diseases. Electrocardiogram signals of different heart patients have been studied. Statistical natures of electrocardiogram signals for different heart diseases have been compared with the statistical nature of electrocardiograms for normal persons. Under this study four different heart diseases have been considered as follows: Myocardial Ischemia (MI), Congestive Heart Failure (CHF), Arrhythmia and Sleep Apnea. Statistical nature of electrocardiograms for each case has been considered in terms of kurtosis values of two types of wavelet coefficients: approximate and detail. Nine wavelet decomposition levels have been considered in each case. Kurtosis corresponding to both approximate and detail coefficients has been considered for decomposition level one to decomposition level nine. Based on significant difference, few decomposition levels have been chosen and then used for classification.

Keywords: arrhythmia, congestive heart failure, discrete wavelet transform, electrocardiogram, myocardial ischemia, sleep apnea

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Authors: Ana Luiza Casasanta Garcia, Maria Del Carmen Cortizo

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From the classic contractualist and early declarations of modern rights, discussions on policies for the protection and promotion of human rights were highlighted in an attempt to ensure the realization of human dignity and its values, which are (re) negotiated according to the needs evidenced in each historical and contextual moment. Aiming at guaranteeing human rights to Brazilian citizens, created in 2009 and updated in 2010, the Third National Human Rights Program (PNDH III) in force highlights guidelines and recommendations to guarantee human rights, among them, to guarantee the rights of women in Brazil. Based on this document, this article aims to locate historically and culturally the understanding of human rights related to the rights of women in Brazilian territory, from the analysis of the guiding axes of women's rights of the PNDH III. In methodological terms, the qualitative approach and documentary research were used to analyze the data according to the critical discourse analysis. As a result, it has been found that the process of building and maintaining the guarantee of women's human rights needs a reformulation that also shows a social revolution. This is justified by the fact that even with the provision in the PNDH III that, in order to guarantee the rights of women, it is necessary, for example, to adapt the Penal Code to the decriminalization of abortion and the professionalization of prostitution, these points are still very controversial and are not put into practice by the State. Finally, the importance of the critique of politics and the current system of production of understandings in favor of this social transformation is emphasized.

Keywords: human rights of women, social transformation, national human rights program III, public politics

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Authors: Uche A. Nnawulezi

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This study evolved from the need to look at and evaluate the difficulties in the domestication of International Laws and Human Rights Laws in Nigeria. Essentially, the paper-based its examination on documentary evidence and depended much on secondary sources, for example, textbooks, journals, articles, periodicals and research reports emanating from suggestions of international law experts, jurists and human rights lawyers on the development challenges in domesticating international laws and human rights laws in Nigeria. These data were analyzed by the application of content analysis and careful observation of the current municipal laws which has posed great challenges in the domestication of International laws. This paper might follow the historical backdrop of the practices in the use of International law in Nigeria and should likewise consider the challenges inherent in these practices. The paper suggests that a sustainable domestication of International Laws and its application in Nigerian courts will ensure a better enforcement of human rights within the domestic jurisdiction.

Keywords: international law, human rights, domestication, challenges

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Authors: Tooba Sabir, Namra Sabir, Mohammad Amjad Sabir

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The human-environment relationship has been projected since the beginning of literary tradition i.e. pastoral tradition, however, the interest of critics, writers and poets, in this view, has been developed, since the last couple of decades because of the increasing scope of environmental studies and growing environmental issues. One such novel, that projects human-environment relationship, is ‘The Alchemist.’ It is Paulo Coelho’s one of the most read novels. It holds a central theme that the universe conspires to help a person achieve his destiny, projecting anthropocentrism and human domination by centralizing human and devaluing the intrinsic worth of ecosystem. However, ecocritical analysis of the text reveals that the novel contains, at several instances, ecocentrism as well e.g. ‘everything on earth is being continuously transformed because earth is alive.’ This portrays ecosphere as living and dynamic entity rather than a mere instrument for human to achieve his destiny. The idea that the universe shares the same language projects unity of nature showing the relationship between human and non-human aspects of the environment as one being and not separate or superior to one another. It depicts human as a part of the environment and not the lord of the world. Therefore, it can be concluded that the novel oscillates between both the ecocentric and the anthropocentric phenomena. It is not suggested, however, that one phenomenon should be valued over the other but that the complexities of both the phenomena should be recognized, acknowledged and valued in order to encourage the interactions between literature and environment.

Keywords: anthropocentrism, ecocentrism, ecocritical analysis, human-environment relationship

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Authors: Issa Qabaja, Fadi Thabtah

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Email phishing classification is one of the vital problems in the online security research domain that have attracted several scholars due to its impact on the users payments performed daily online. One aspect to reach a good performance by the detection algorithms in the email phishing problem is to identify the minimal set of features that significantly have an impact on raising the phishing detection rate. This paper investigate three known feature selection methods named Information Gain (IG), Chi-square and Correlation Features Set (CFS) on the email phishing problem to separate high influential features from low influential ones in phishing detection. We measure the degree of influentially by applying four data mining algorithms on a large set of features. We compare the accuracy of these algorithms on the complete features set before feature selection has been applied and after feature selection has been applied. After conducting experiments, the results show 12 common significant features have been chosen among the considered features by the feature selection methods. Further, the average detection accuracy derived by the data mining algorithms on the reduced 12-features set was very slight affected when compared with the one derived from the 47-features set.

Keywords: data mining, email classification, phishing, online security

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Authors: Qian Huiya, Feng Lin

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The progressive collapse of reinforced concrete (RC) structures will cause huge casualties and property losses. Therefore, it is necessary to evaluate the ability of structures against progressive collapse accurately. This paper numerically investigated the effect of storey number on the mechanism and quantitative contribution of the Vierendeel action (VA) in progressive collapse under corner column removal scenario. First, finite element (FE) models of multi-storey RC frame structures were developed using LS-DYNA. Then, the accuracy of the modeling technique was validated by test results conducted by the authors. Last, the validated FE models were applied to investigated the structural behavior of the RC frames with different storey numbers from one to six storeys. Results found the multi-storey substructure formed additional plastic hinges at the beam ends near the corner column in the second to top storeys, and at the lower end of the corner column in the first storey. The average ultimate resistance of each storey of the multi-storey substructures were increased by 14.0% to 18.5% compared with that of the single-storey substructure experiencing no VA. The contribution of VA to the ultimate resistance was decreased with the increase of the storey number.

Keywords: progressive collapse, reinforced concrete structure, storey number, Vierendeel action

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Authors: Arif Almutairi, Monjur Mourshed

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Due to varying geographical and tectonic factors, the region of Saudi Arabia has been subjected to numerous natural and man-made maritime disasters during the last two decades. Natural maritime disasters, such as cyclones and tsunamis, have been recorded in coastal areas of the Indian Ocean (including the Arabian Sea and the Gulf of Aden). Therefore, the Indian Ocean is widely recognised as the potential source of future destructive natural disasters that could affect Saudi Arabia’s coastline. Meanwhile, man-made maritime disasters, such as those arising from piracy and oil pollution, are located in the Red Sea and the Arabian Gulf, which are key locations for oil export and transportation between Asia and Europe. This paper provides a brief overview of maritime disasters surrounding Saudi Arabia’s coastline in order to classify them by frequency of occurrence and location, and discuss their future impact the region. Results show that the Arabian Gulf will be more vulnerable to natural maritime disasters because of its location, whereas the Red Sea is more vulnerable to man-made maritime disasters, as it is the key location for transportation between Asia and Europe. The results also show that with the aid of proper classification, effective disaster management can reduce the consequences of maritime disasters.

Keywords: disaster classification, maritime disaster, natural disasters, man-made disasters

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Authors: Yuanxiang Miao

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Couchsurfing is a free homestay and social networking service accessible via the website and mobile app. Couchsurfers can directly request free accommodations from others and receive offers from each other. However, it is typically difficult for people to make a decision that accepts or declines a request when they receive it from Couchsurfers because they do not know each other at all. People are expected to meet up with some Couchsurfers who are kind, generous, and interesting while it is unavoidable to meet up with someone unfriendly. This paper utilized classification algorithms of Machine Learning to help people to find out the Good Couchsurfers and Not Good Couchsurfers on the Couchsurfing website. By knowing the prior experience, like Couchsurfer’s profiles, the latest references, and other factors, it became possible to recognize what kind of the Couchsurfers, and furthermore, it helps people to make a decision that whether to host the Couchsurfers or not. The value of this research lies in a case study in Kyoto, Japan in where the author has hosted 54 Couchsurfers, and the author collected relevant data from the 54 Couchsurfers, finally build a model based on classification algorithms for people to predict Couchsurfers. Lastly, the author offered some feasible suggestions for future research.

Keywords: Couchsurfing, Couchsurfers prediction, classification algorithm, hospitality tourism platform, hospitality sciences, machine learning

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Authors: Lucas B. Naves, Maria José Abreu

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Reprocessing single-use device has attracted interesting on the medical environment over the last decades. The reprocessing technique was sought in order to reduce the cost of purchasing the new medical device, which can achieve almost double of the price of the reprocessed product. In this manuscript, we have done a literature review, aiming the reuse of medical device that was firstly designed for single use only, but has become, more and more, effective on its reprocessing procedure. We also show the regulation, the countries which allows this procedure, the classification of these device and also the most important issue concerning the re-utilization of medical device, how to minimizing the risk of gram positive and negative bacteria, avoid cross-contamination, hepatitis B (HBV), and C (HCV) virus, and also human immunodeficiency virus (HIV).

Keywords: reusing, reprocessing, single-use medical device, HIV, hepatitis B and C

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Authors: Melina Kopke, Jelle Van Dijk

Abstract:

Persons with cognitive disabilities, such as Autism Spectrum Disorder (ASD) are often dependent on some form of professional support. Recent transformations in Dutch healthcare have spurred institutions to apply new, empowering methods and tools to enable their clients to cope (more) independently in daily life. Assistive Technologies (ATs) seem promising as empowering tools. While ATs can, functionally speaking, help people to perform certain activities without human assistance, we hold that, from a design-theoretical perspective, such technologies often fail to empower in a deeper sense. Most technologies serve either to prescribe or to monitor users’ actions, which in some sense objectifies them, rather than strengthening their agency. This paper proposes that theories of embodied interaction could help formulating a design vision in which interactive assistive devices augment, rather than replace, human agency and thereby add to a persons’ empowerment in daily life settings. It aims to close the gap between empowerment theory and the opportunities provided by assistive technologies, by showing how embodiment and empowerment theory can be applied in practice in the design of new, interactive assistive devices. Taking a Research-through-Design approach, we conducted a case study of designing to support independently living people with ASD with structuring daily activities. In three iterations we interlaced design action, active involvement and prototype evaluations with future end-users and healthcare professionals, and theoretical reflection. Our co-design sessions revealed the issue of handling daily activities being multidimensional. Not having the ability to self-manage one’s daily life has immense consequences on one’s self-image, and also has major effects on the relationship with professional caregivers. Over the course of the project relevant theoretical principles of both embodiment and empowerment theory together with user-insights, informed our design decisions. This resulted in a system of wireless light units that users can program as a reminder for tasks, but also to record and reflect on their actions. The iterative process helped to gradually refine and reframe our growing understanding of what it concretely means for a technology to empower a person in daily life. Drawing on the case study insights we propose a set of concrete design principles that together form what we call the embodied empowerment design framework. The framework includes four main principles: Enabling ‘reflection-in-action’; making information ‘publicly available’ in order to enable co-reflection and social coupling; enabling the implementation of shared reflections into an ‘endurable-external feedback loop’ embedded in the persons familiar ’lifeworld’; and nudging situated actions with self-created action-affordances. In essence, the framework aims for the self-development of a suitable routine, or ‘situated practice’, by building on a growing shared insight of what works for the person. The framework, we propose, may serve as a starting point for AT designers to create truly empowering interactive products. In a set of follow-up projects involving the participation of persons with ASD, Intellectual Disabilities, Dementia and Acquired Brain Injury, the framework will be applied, evaluated and further refined.

Keywords: assistive technology, design, embodiment, empowerment

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Authors: Guenther Schuh, Michael Riesener, Jan Kantelberg

Abstract:

Due to the fugacity of markets and the reduction of product lifecycles, manufacturing companies from high-wage countries are nowadays faced with the challenge to place more innovative products within even shorter development time on the market. At the same time, volatile customer requirements have to be satisfied in order to successfully differentiate from market competitors. One potential approach to address the explained challenges is provided by agile values and principles. These agile values and principles already proofed their success within software development projects in the form of management frameworks like Scrum or concrete procedure models such as Extreme Programming or Crystal Clear. Those models lead to significant improvements regarding quality, costs and development time and are therefore used within most software development projects. Motivated by the success within the software industry, manufacturing companies have tried to transfer agile mechanisms of action to the development of hardware products ever since. Though first empirical studies show similar effects in the agile development of hardware products, no comprehensive procedure model for the design of development iterations has been developed for hardware development yet due to different constraints of the domains. For this reason, this paper focusses on the design of agile product development processes by transferring mechanisms of action used in agile software development towards product development. This is conducted by decomposing the individual systems 'product development' and 'agile software development' into relevant elements and symbiotically composing the elements of both systems in respect of the design of agile product development processes afterwards. In a first step, existing product development processes are described following existing approaches of the system theory. By analyzing existing case studies from industrial companies as well as academic approaches, characteristic objectives, activities and artefacts are identified within a target-, action- and object-system. In partial model two, mechanisms of action are derived from existing procedure models of agile software development. These mechanisms of action are classified in a superior strategy level, in a system level comprising characteristic, domain-independent activities and their cause-effect relationships as well as in an activity-based element level. Within partial model three, the influence of the identified agile mechanism of action towards the characteristic system elements of product development processes is analyzed. For this reason, target-, action- and object-system of the product development are compared with the strategy-, system- and element-level of agile mechanism of action by using the graph theory. Furthermore, the necessity of existence of activities within iteration can be determined by defining activity-specific degrees of freedom. Based on this analysis, agile product development processes are designed in form of different types of iterations within a last step. By defining iteration-differentiating characteristics and their interdependencies, a logic for the configuration of activities, their form of execution as well as relevant artefacts for the specific iteration is developed. Furthermore, characteristic types of iteration for the agile product development are identified.

Keywords: activity-based process model, agile mechanisms of action, agile product development, degrees of freedom

Procedia PDF Downloads 199

Authors: Sidra Naeem, Ayesha Naeem, Sahar Rahim, Nadia Nawaz Qadri

Abstract:

Citrus greening is a bacterial disease that causes considerable damage to citrus fruits worldwide. Efficient method for this disease detection must be carried out to minimize the production loss. This paper presents a pattern recognition system that comprises three stages for the detection of citrus greening from Orange leaves: segmentation, feature extraction and classification. Image segmentation is accomplished by adaptive thresholding. The feature extraction stage comprises of three visual descriptors i.e. shape, color and texture. From shape feature we have used asymmetry index, from color feature we have used histogram of Cb component from YCbCr domain and from texture feature we have used local binary pattern. Classification was done using support vector machines and k nearest neighbors. The best performances of the system is Accuracy = 88.02% and AUROC = 90.1% was achieved by automatic segmented images. Our experiments validate that: (1). Segmentation is an imperative preprocessing step for computer assisted diagnosis of citrus greening, and (2). The combination of shape, color and texture features form a complementary set towards the identification of citrus greening disease.

Keywords: citrus greening, pattern recognition, feature extraction, classification

Procedia PDF Downloads 176

Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, Bayesian, echocardiographic image, feature vector

Procedia PDF Downloads 434