Search results for: accuracy assessment.

Authors: Muhammad Ali

Abstract:

Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Kayne Uriel K. Rodrigo, Jerriane Hillary Heart S. Marcial, Samuel C. Brillo

Abstract:

Rice leaf diseases significantly impact yield production in rice-dependent countries, affecting their agricultural sectors. As part of precision agriculture, early and accurate detection of these diseases is crucial for effective mitigation practices and minimizing crop losses. Hence, this study proposes an enhancement to the Convolutional Neural Network (CNN), a widely-used method for Rice Leaf Disease Image Classification, by incorporating MobileViTV2—a recently advanced architecture that combines CNN and Vision Transformer models while maintaining fewer parameters, making it suitable for broader deployment on edge devices. Our methodology utilizes a publicly available rice disease image dataset from Kaggle, which was validated by a university structural biologist following the guidelines provided by the Philippine Rice Institute (PhilRice). Modifications to the dataset include renaming certain disease categories and augmenting the rice leaf image data through rotation, scaling, and flipping. The enhanced dataset was then used to train the MobileViTV2 model using the Timm library. The results of our approach are as follows: the model achieved notable performance, with 98% accuracy in both training and validation, 6% training and validation loss, and a Receiver Operating Characteristic (ROC) curve ranging from 95% to 100% for each label. Additionally, the F1 score was 97%. These metrics demonstrate a significant improvement compared to a conventional CNN-based approach, which, in a previous 2022 study, achieved only 78% accuracy after using 5 convolutional layers and 2 dense layers. Thus, it can be concluded that MobileViTV2, with its fewer parameters, outperforms traditional CNN models, particularly when applied to Rice Leaf Disease Image Identification. For future work, we recommend extending this model to include datasets validated by international rice experts and broadening the scope to accommodate biotic factors such as rice pest classification, as well as abiotic stressors such as climate, soil quality, and geographic information, which could improve the accuracy of disease prediction.

Keywords: convolutional neural network, MobileViTV2, rice leaf disease, precision agriculture, image classification, vision transformer

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Authors: Y. S. Lin, H. L. Lin

Abstract:

Climate change and land-use change caused by urban expansion increase the frequency of urban flooding. To mitigate the increase in runoff volume, low-impact development (LID) is a green approach for reducing the area of impervious surface and managing stormwater at the source with decentralized micro-scale control measures. However, the current benefit assessment and practical application of LID in Taiwan is still tending to be development plan in the community and building site scales. As for urban design, site-based moisture-holding capacity has been common index for evaluating LID’s effectiveness of urban design, which ignore the diversity, and complexity of the urban built environments, such as different densities, positive and negative spaces, volumes of building and so on. Such inflexible regulations not only probably make difficulty for most of the developed areas to implement, but also not suitable for every different types of built environments, make little benefits to some types of built environments. Looking toward to enable LID to strength the link with urban design to reduce the runoff in coping urban flooding, the research consider different characteristics of different types of built environments in developing LID strategy. Classify the built environments by doing the cluster analysis based on density measures, such as Ground Space Index (GSI), Floor Space Index (FSI), Floors (L), and Open Space Ratio (OSR), and analyze their impervious surface rates and runoff volumes. Simulate flood situations by using quasi-two-dimensional flood plain flow model, and evaluate the flood mitigation effectiveness of different types of built environments in different low-impact development strategies. The information from the results of the assessment can be more precisely implement in urban design. In addition, it helps to enact regulations of low-Impact development strategies in urban design more suitable for every different type of built environments.

Keywords: low-impact development, urban design, flooding, density measures

Procedia PDF Downloads 333

Authors: Mst Shapna Akter, Hossain Shahriar

Abstract:

Neural network approaches are machine learning methods used in many domains, such as healthcare and cyber security. Neural networks are mostly known for dealing with image datasets. While training with the images, several fundamental mathematical operations are carried out in the Neural Network. The operation includes a number of algebraic and mathematical functions, including derivative, convolution, and matrix inversion and transposition. Such operations require higher processing power than is typically needed for computer usage. Central Processing Unit (CPU) is not appropriate for a large image size of the dataset as it is built with serial processing. While Graphics Processing Unit (GPU) has parallel processing capabilities and, therefore, has higher speed. This paper uses advanced Neural Network techniques such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models to compare CPU and GPU resources. A system for classifying autism disease using face images of an autistic and non-autistic child was used to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It has been observed that GPU runs faster than the CPU in all tests performed. Moreover, the performance of the Neural Network models in terms of accuracy increases on GPU compared to CPU.

Keywords: autism disease, neural network, CPU, GPU, transfer learning

Procedia PDF Downloads 117

Authors: Queen Suraajini Rajendran, Sai Hung Cheung

Abstract:

Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

Procedia PDF Downloads 367

Authors: Ron Dick, P. S. D. V. Prasadarao, Glenn Coltman

Abstract:

Agenesis of the corpus callosum (ACC) is a failure to develop corpus callosum - the large bundle of fibers of the brain that connects the two cerebral hemispheres. It can occur as a partial or complete absence of the corpus callosum. In the general population, its estimated prevalence rate is 1 in 4000 and a wide range of genetic, infectious, vascular, and toxic causes have been attributed to this heterogeneous condition. The diagnosis of ACC is often achieved by neuroimaging procedures. Though persons with ACC can perform normally on intelligence tests they generally present with a range of neuropsychological and social deficits. The deficit profile is characterized by poor coordination of motor movements, slow reaction time, processing speed and, poor memory. Socially, they present with deficits in communication, language processing, the theory of mind, and interpersonal relationships. The present paper illustrates the role of neuropsychological assessment with implications to psychosocial management in a case of agenesis of the corpus callosum. Method: A 27-year old left handed Caucasian male with a history of ACC was self-referred for a neuropsychological assessment to assist him in his employment options. Parents noted significant difficulties with coordination and balance at an early age of 2-3 years and he was diagnosed with dyspraxia at the age of 14 years. History also indicated visual impairment, hypotonia, poor muscle coordination, and delayed development of motor milestones. MRI scan indicated agenesis of the corpus callosum with ventricular morphology, widely spaced parallel lateral ventricles and mild dilatation of the posterior horns; it also showed colpocephaly—a disproportionate enlargement of the occipital horns of the lateral ventricles which might be affecting his motor abilities and visual defects. The MRI scan ruled out other structural abnormalities or neonatal brain injury. At the time of assessment, the subject presented with such problems as poor coordination, slowed processing speed, poor organizational skills and time management, and difficulty with social cues and facial expressions. A comprehensive neuropsychological assessment was planned and conducted to assist in identifying the current neuropsychological profile to facilitate the formulation of a psychosocial and occupational rehabilitation programme. Results: General intellectual functioning was within the average range and his performance on memory-related tasks was adequate. Significant visuospatial and visuoconstructional deficits were evident across tests; constructional difficulties were seen in tasks such as copying a complex figure, building a tower and manipulating blocks. Poor visual scanning ability and visual motor speed were evident. Socially, the subject reported heightened social anxiety, difficulty in responding to cues in the social environment, and difficulty in developing intimate relationships. Conclusion: Persons with ACC are known to present with specific cognitive deficits and problems in social situations. Findings from the current neuropsychological assessment indicated significant visuospatial difficulties, poor visual scanning and problems in social interactions. His general intellectual functioning was within the average range. Based on the findings from the comprehensive neuropsychological assessment, a structured psychosocial rehabilitation programme was developed and recommended.

Keywords: agenesis, callosum, corpus, neuropsychology, psychosocial, rehabilitation

Procedia PDF Downloads 275

Authors: Chang-Fa Liu, Yi-Ting Wang, Yuan Liu, Hai-Feng Wei, Lei Fang, Jin Li

Abstract:

Heavy metals in sediments can cause adverse ecological effects while it exceeds a given criteria. The present study investigated sediment environmental quality, pollutant enrichment, ecological risk, and source identification for copper, cadmium, lead, zinc, mercury, and arsenic in the sediments collected from tidal flat marsh of Shuangtai estuary, China. The arithmetic mean integrated pollution index, geometric mean integrated pollution index, fuzzy integrated pollution index, and principal component score were used to characterize sediment environmental quality; fuzzy similarity and geo-accumulation Index were used to evaluate pollutant enrichment; correlation matrix, principal component analysis, and cluster analysis were used to identify source of pollution; environmental risk index and potential ecological risk index were used to assess ecological risk. The environmental qualities of sediment are classified to very low degree of contamination or low contamination. The similar order to element background of soil in the Liaohe plain is region of Sanjiaozhou, Honghaitan, Sandaogou, Xiaohe by pollutant enrichment analysis. The source identification indicates that correlations are significantly among metals except between copper and cadmium. Cadmium, lead, zinc, mercury, and arsenic will be clustered in the same clustering as the first principal component. Copper will be clustered as second principal component. The environmental risk assessment level will be scaled to no risk in the studied area. The order of potential ecological risk is As > Cd > Hg > Cu > Pb > Zn.

Keywords: ecological risk assessment, heavy metals, sediment, marsh, Shuangtai estuary

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Authors: Yas Barzegaar, Atrin Barzegar

Abstract:

The aim of this research is to develop an intelligent system to analyze the risk level of wheat flour product manufacturing system. The model consists of five Fuzzy Inference Systems in two different layers to analyse the risk of a wheat flour product manufacturing system. The first layer of the model consists of four Fuzzy Inference Systems with three criteria. The output of each one of the Physical, Chemical, Biological and Environmental Failures will be the input of the final manufacturing systems. The proposed model based on Mamdani Fuzzy Inference Systems gives a performance ranking of wheat flour products manufacturing systems. The first step is obtaining data to identify the failure modes from expert’s opinions. The second step is the fuzzification process to convert crisp input to a fuzzy set., then the IF-then fuzzy rule applied through inference engine, and in the final step, the defuzzification process is applied to convert the fuzzy output into real numbers.

Keywords: failure modes, fuzzy rules, fuzzy inference system, risk assessment

Procedia PDF Downloads 101

Authors: Agnes Havril

Abstract:

The aim of this paper is to introduce some partial findings of an ongoing research project which is investigating the improvement of intercultural competence of Saudi female university students in English as a Second Language academic environment at the multicultural Jazan University. Since previous research results support the idea that this university generation has the desire to become interculturally or globally competent university students, the present-day investigation is focusing on the assessment of Saudi-specific cultural terms and intercultural competence components in comparison with the Anglo-Saxon oriented western perspective of intercultural competence theories and models. On this stage of the research quantitative research methodology is applied and a survey is being conducted among the female university students in different academic specializations. This paper discusses some empirical data with the aim of identifying and evaluating certain supplementary aspects of intercultural dimensions and components of the intercultural competence construct. The research results also highlight several gender issues in the gender separated higher education in the Kingdom of Saudi Arabia.

Keywords: gender separation, globally competent university student, intercultural competence, intercultural competence construct, higher education

Procedia PDF Downloads 335

Authors: Yuan Liu, Hongyun Liu

Abstract:

Teacher-student relationship plays an important role on facilitating students’ learning behavior, school engagement, and academic outcomes. It is believed that good relationship will enhance the human agency—the intrinsic motivation—mainly through the strengthening of autonomic support, feeling of relatedness, and the individual’s competence to increase the academic outcomes. This is in line with self-determination theory (SDT), which generally views that the intrinsic motivation imbedded with human basic needs is one of the most important factors that would lead to better school engagement, academic outcomes, and well-being. Based on SDT, the present study explored the relation of among teacher-student relationship (teacher’s encouragement, respect), students’ motivation (extrinsic and intrinsic), and achievement outcomes. The study was based on a large scale academic assessment and questionnaire survey conducted by the Center for Assessment and Improvement of Basic Education Quality in Mainland China (2013) on Grade 8 students. The results indicated that intrinsic motivation mediated the relation between teacher-student relationship and academic achievement outcomes.

Keywords: teacher-student relationship, intrinsic motivation, academic achievement, mediation

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Authors: Z. Sakka, I. Assakkaf, T. Al-Yaqoub, J. Parol

Abstract:

A reliability-based methodology for the analysis assessment and evaluation of reinforced concrete structural elements of concrete structures is presented herein. The results of the reliability analysis and assessment for structural elements are verified by the results obtained from the deterministic methods. The analysis outcomes of reliability-based analysis are compared against the safety limits of the required reliability index β according to international standards and codes. The methodology is based on probabilistic analysis using reliability concepts and statistics of the main random variables that are relevant to the subject matter, and for which they are to be used in the performance-function equation(s) related to the structural elements under study. These methodology techniques can result in reliability index β, which is commonly known as the reliability index or reliability measure value that can be utilized to assess and evaluate the safety, human risk, and functionality of the structural component. Also, these methods can result in revised partial safety factor values for certain target reliability indices that can be used for the purpose of redesigning the reinforced concrete elements of the building and in which they could assist in considering some other remedial actions to improve the safety and functionality of the member.

Keywords: structural reliability, concrete structures, FORM, Monte Carlo simulation

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Authors: Stewart A. Keir, Faik A. Hamad

Abstract:

This study aims to use both analytical and experimental methods of analysis to examine the accuracy of Computational Fluid Dynamics (CFD) models that can then be used for more complex analyses, accurately representing more elaborate flow phenomena such as internal shockwaves and boundary layers. The geometry used in the analytical study and CFD model is taken from the experimental rig. The analytical study is undertaken using isentropic and adiabatic relationships and the output of the analytical study, the 'shockwave location tool', is created. The results from the analytical study are then used to optimize the redesign an experimental rig for more favorable placement of pressure taps and gain a much better representation of the shockwaves occurring in the divergent section of the nozzle. The CFD model is then optimized through the selection of different parameters, e.g. turbulence models (Spalart-Almaras, Realizable k-epsilon & Standard k-omega) in order to develop an accurate, robust model. The results from the CFD model can then be directly compared to experimental and analytical results in order to gauge the accuracy of each method of analysis. The CFD model will be used to visualize the variation of various parameters such as velocity/Mach number, pressure and turbulence across the shock. The CFD results will be used to investigate the interaction between the shock wave and the boundary layer. The validated model can then be used to modify the nozzle designs which may offer better performance and ease of manufacture and may present feasible improvements to existing high-speed flow applications.

Keywords: CFD, nozzle, fluent, gas dynamics, shock-wave

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Authors: David P. Tantua, Abdulkader Helwan

Abstract:

Biometrics has been recently used for the human identification systems using the biological traits such as the fingerprints and iris scanning. Identification systems based biometrics show great efficiency and accuracy in such human identification applications. However, these types of systems are so far based on some image processing techniques only, which may decrease the efficiency of such applications. Thus, this paper aims to develop a human palmprint identification system using multi-layer perceptron neural network which has the capability to learn using a backpropagation learning algorithms. The developed system uses images obtained from a public database available on the internet (CASIA). The processing system is as follows: image filtering using median filter, image adjustment, image skeletonizing, edge detection using canny operator to extract features, clear unwanted components of the image. The second phase is to feed those processed images into a neural network classifier which will adaptively learn and create a class for each different image. 100 different images are used for training the system. Since this is an identification system, it should be tested with the same images. Therefore, the same 100 images are used for testing it, and any image out of the training set should be unrecognized. The experimental results shows that this developed system has a great accuracy 100% and it can be implemented in real life applications.

Keywords: biometrics, biological traits, multi-layer perceptron neural network, image skeletonizing, edge detection using canny operator

Procedia PDF Downloads 371

Authors: Olalekan Bamidele Aruleba

Abstract:

This research assessed the impact of manpower training and development in the construction industry. The aim is to determine the effect of training and development on employees for effective organizational growth in the construction industry to identify the training method for each category of employee in the construction industry, challenges to training and development of workers in the construction industry and impact of manpower training and development on employees and employers. Data for the study were obtained through a well-structured questionnaire administered to building professionals in Nigeria construction firm. Eighty (80) questionnaires were distributed among building professionals in three selected local governments within Ondo State and sixty-four (64) were returned. Data collected were analysed using descriptive statistics and ranking. Findings of the study revealed that in house training and in-service training methods were preferred by most construction industry. It concluded that the attitude of top management and lack of fund was seen as the significant challenges militating against training of employees. The study recommended that manpower training and development must be sustained by all stakeholders in the industry in order to improve workers' productivity; the organization should adopt the right method in training each category of employees and carry out the need assessment for training to avoid training wrong employees.

Keywords: construction, development, manpower, training

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Authors: Ali Hamza, Bertug Akintug

Abstract:

Having an appropriate plan of sanitation systems is one of the critical issues for global urban slums. Poor sanitation systems in urban slums outcomes an enhanced vulnerability of severe diseases, low hygiene and environmental risks within our environment. Mentioning human excreta being one of the most highly risked pollutants among all the other major contributors of sanitation pollutants is increasing public health risks and amounts of pollution loads within the slum environment. Higher population growth, urge of urbanization and illegal status of urban slums makes it impossible to increase the level of performance of sanitation systems in urban slums. According to Sustainable Sanitation Alliance, design parameters for sanitation systems were set up to ensure sustainable environment. This paper reviews the characteristics of human excreta at present, treatment technologies, and procedures of processes that can be adopted feasibly in the urban slums. Keeping these factors as our significant concern of study, assessment of sustainable sanitation systems is done using sanitation chain concept in accordance to the pre-determined sustainability indicators and criteria which reflect the potential and feasible application of waterless sanitation systems bringing sustainable sanitation systems in urban slums.

Keywords: human excreta, sanitation chain, sustainable sanitation systems, urban slums

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Authors: Sanjal Gavande, Thomas Mazzuchi, Shahram Sarkani

Abstract:

In a complex aerospace operational environment, identifying failures in a procedure involving multiple human-machine interactions are difficult. These failures could lead to accidents causing loss of hardware or human life. The likelihood of failure further increases if operational procedures are tested for a novel system with multiple human-machine interfaces and with no prior performance data. The existing approach in the literature of reviewing complex operational tasks in a flowchart or tabular form doesn’t provide any insight into potential system failures due to human decision-making ability. To address these challenges, this research explores an agent-based simulation approach for reliability assessment and fault detection in complex human-machine systems while utilizing a human decision-making model. The simulation will predict the emergent behavior of the system due to the interaction between humans and their decision-making capability with the varying states of the machine and vice-versa. Overall system reliability will be evaluated based on a defined set of success-criteria conditions and the number of recorded failures over an assigned limit of Monte Carlo runs. The study also aims at identifying high-likelihood failure locations for the system. The research concludes that system reliability and failures can be effectively calculated when individual human and machine agent states are clearly defined. This research is limited to the operations phase of a system lifecycle process in an aerospace environment only. Further exploration of the proposed agent-based and human decision-making model will be required to allow for a greater understanding of this topic for application outside of the operations domain.

Keywords: agent-based model, complex human-machine system, human decision-making model, system reliability assessment

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Authors: Hosseini Siahi Zohreh, Sana Mohammad Jafar

Abstract:

With increase in fat and over weight in children and its undesirable effects on different organisms of the body and since many of the sicknesses are due to over weight and with losing weight these sicknesses disappear, and on the other hand with mal nutrition and under weight in children other kind of sicknesses such as derogation of body's security system, frequent infection, insufficient growth, shortness, and delay in maturity etc. are some of the signs of being under weight. Therefore recognition of signs of over weight and under weight and their prevalence in children are important. To determine this difficulty we have used the body mass index as screening tool since it is very prevalent and a good and important guide and has very good relation with body fat in children. In this study 2321 students from primary schools in Behbahan have been chosen randomly and evaluated by height and weight and their body mass index have been calculated and then recorded on the BMI percentile diagram which is for age and gender. The following results obtained: The amount of total fat, over weight and slimness are 9.3, 12.1 and 12.32 percent respectively. Therefore 21.4% of the children were over weighted. It did not show any meaningful statistical relation in fat conditions among boys and girls, but there has been a meaningful statistical relation in slimness among boys and girls.

Keywords: assessment, students, Behbahan, Body Mass Index

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Authors: Tejaswi Agarwal, Amritansh Agarwal

Abstract:

Forest ecosystem plays very important role in the global carbon cycle. It stores about 80% of all above ground and 40% of all below ground terrestrial organic carbon. There is much interest in the extent of tropical forests and their rates of deforestation for two reasons: greenhouse gas contributions and the impact of profoundly negative biodiversity. Deforestation has many ecological, social and economic consequences, one of which is the loss of biological diversity. The rapid deployment of remote sensing (RS) satellites and development of RS analysis techniques in the past three decades have provided a reliable, effective, and practical way to characterize terrestrial ecosystem properties. Global estimates of tropical deforestation vary widely and range from 50,000 to 170,000km2 /yr Recent FAO tropical deforestation estimates for 1990–1995 cite 116,756km2 / yr globally. Remote Sensing can prove to be a very useful tool in monitoring of forests and associated deforestation to a sufficient level of accuracy without the need of physically surveying the forest areas as many of them are physically inaccessible. The methodology for the assessment of forest cover using digital image processing (ERDAS) has been followed. The satellite data for the study was procured from Indian institute of remote Sensing (IIRS), Dehradoon in the digital format. While procuring the satellite data, care was taken to ensure that the data was cloud free and did not belong to dry and leafless season. The Normalized Difference Vegetation Index (NDVI) has been used as a numerical indicator of the reduction in ground biomass. NDVI = (near I.R - Red)/ (near I.R + Red). After calculating the NDVI variations and associated mean, we have analysed the change in ground biomass. Through this paper, we have tried to indicate the rate of deforestation over a given period of time by comparing the forest cover at different time intervals. With the help of remote sensing and GIS techniques, it is clearly shown that the total forest cover is continuously degrading and transforming into various land use/land cover category.

Keywords: remote sensing, deforestation, supervised classification, NDVI, change detection

Procedia PDF Downloads 1198

Authors: Morteza Aien, Masoud Rashidinejad, Mahmud Fotuhi-Firuzabad

Abstract:

As energetic and environmental issues are getting more and more attention all around the world, the penetration of distributed energy resources (DERs) mainly those harvesting renewable energies (REs) ascends with an unprecedented rate. This matter causes more uncertainties to appear in the power system context; ergo, the uncertainty analysis of the system performance is an obligation. The uncertainties of any system can be represented probabilistically or possibilistically. Since sufficient historical data about all the system variables is not available, therefore, they do not have a probability density function (PDF) and must be represented possibilistiacally. When some of system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution is appealed. The first of this two-paper series formulates a new possibilistic-probabilistic tool for the load flow uncertainty assessment. The proposed methodology is based on the evidence theory and joint propagation of possibilistic and probabilistic uncertainties. This possibilistic- probabilistic formulation is solved in the second companion paper in an uncertain load flow (ULF) study problem.

Keywords: probabilistic uncertainty modeling, possibilistic uncertainty modeling, uncertain load flow, wind turbine generator

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Authors: J. K. Pis-An

Abstract:

Objective: The purpose of the study was to examine the vulnerability of farmers to the effects of climate change, specifically the El Niño phenomenon was felt in the Philippines in 2009-2010. Methods: KAP Survey determines behavioral response to vulnerability to the effects of El Niño. Body Mass Index: Dietary Assessment using 24-hour food recall. Results: 75% of the respondents claimed that crop significantly decreased during drought. Indications that households of farmers are large where 51.6% are composed of 6-10 family members with 68% annual incomes below Php 100,00. Anthropometric assessment showed that the prevalence of Chronic Energy Deficiency Grade 1 among females 17% and 28.57% for low normal. While male body mass index result for chronic energy deficiency grade 1 10%, low normal 18.33% and and obese grade 1, 31.67%. Dietary assessment of macronutrient intake of carbohydrates, protein, and fat 31.6 % among respondents are below recommended amounts. Micronutrient deficiency of calcium, iron, vit. A, thiamine, riboflavin, niacin, and Vit. C. Conclusion: Majority of the rural populations are engaged into farming livelihood that makes up the backbone of their economic growth. Placing the current nutritional status of the farmers in the context of food security, there are reasons to believe that the status will go for worse if the extreme climatic conditions will once again prevail in the region. Farmers rely primarily on home grown crops for their food supply, a reduction in farm production during drought is expected to adversely affect dietary intake. The local government therefore institute programs to increase food resiliency and to prioritize health of the population as the moving force for productivity and development.

Keywords: world health organization, united nation framework convention on climate change, anthropometric, macronutrient, micronutrient

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Authors: Jagadish Rao Padubidri, Mehak Bhandary, Sowmya J. Rao

Abstract:

Introduction: The significance of the human foot and its measurements in identifying an individual has been proved a lot of times by different studies in different geographical areas and its association to the stature and gender of the individual has been justified by many researches. In our study we have used different foot measurements including the length, width, malleol height and navicular height for establishing its association to stature and gender and to find out its accuracy. The purpose of this study is to show the relation of foot measurements with stature and gender, and to derive Multiple and Logistic regression equations for stature and gender estimation in South Indian population. Materials and Methods: The subjects for this study were 200 South Indian students out of which 100 were females and 100 were males, aged between 18 to 24 years. The data for the present study included the stature, foot length, foot breath, foot malleol height, foot navicular height of both right and left foot. Descriptive statistics, T-test and Pearson correlation coefficients were derived between stature, gender and foot measurements. The stature was estimated from right and left foot measurements for both male and female South Indian population using multiple regression analysis and logistic regression analysis for gender estimation. Results: The means, standard deviation, stature, right and left foot measurements and T-test in male population were higher than in females. LFL (Left foot length) is more than RFL (Right Foot length) in male groups, but in female groups the length of both foot are almost equal [RFL=226.6, LFL=227.1]. There is not much of difference in means of RFW (Right foot width) and LFW (Left foot width) in both the genders. Significant difference were seen in mean values of malleol and navicular height of right and left feet in male gender. No such difference was seen in female subjects. Conclusions: The study has successfully demonstrated the correlation of foot length in stature estimation in all the three study groups in both right and left foot. Next in parameters are Foot width and malleol height in estimating stature among male and female groups. Navicular height of both right and left foot showed poor relationship with stature estimation in both male and female groups. Multiple regression equations for both right and left foot measurements to estimate stature were derived with standard error ranging from 11-12 cm in males and 10-11 cm in females. The SEE was 5.8 when both male and female groups were pooled together. The logistic regression model which was derived to determine gender showed 85% accuracy and 92.5% accuracy using right and left foot measurements respectively. We believe that stature and gender can be estimated with foot measurements in South Indian population.

Keywords: foot length, gender, stature, South Indian

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Authors: Elham Saraee, Saurabh Singh, Margrit Betke

Abstract:

Evaluation of a person’s posture while exercising is important in physical therapy. During a therapy session, a physical therapist or a monitoring system must assure that the person is performing an exercise correctly to achieve the desired therapeutic effect. In this work, we introduce a system called POSTURECHECK for exercise assessment in physical therapy. POSTURECHECK assesses the posture of a person who is exercising with the Proficio robotic arm while being recorded by the Microsoft Kinect interface. POSTURECHECK extracts unique features from the person’s upper body during the exercise, and classifies the sequence of postures as correct or incorrect using Bayesian estimation and majority voting. If POSTURECHECK recognizes an incorrect posture, it specifies what the user can do to correct it. The result of our experiment shows that POSTURECHECK is capable of recognizing the incorrect postures in real time while the user is performing an exercise.

Keywords: Bayesian estimation, majority voting, Microsoft Kinect, PostureCheck, Proficio robotic arm, upper body physical therapy

Procedia PDF Downloads 279

Authors: Amritansh Agarwal, Tejaswi Agarwal

Abstract:

Forest ecosystem plays very important role in the global carbon cycle. It stores about 80% of all above ground and 40% of all below ground terrestrial organic carbon. There is much interest in the extent of tropical forests and their rates of deforestation for two reasons: greenhouse gas contributions and the impact of profoundly negative biodiversity. Deforestation has many ecological, social and economic consequences, one of which is the loss of biological diversity. The rapid deployment of remote sensing (RS) satellites and development of RS analysis techniques in the past three decades have provided a reliable, effective, and practical way to characterize terrestrial ecosystem properties. Global estimates of tropical deforestation vary widely and range from 50,000 to 170,000 km2 /yr Recent FAO tropical deforestation estimates for 1990–1995 cite 116,756km2 / yr globally. Remote Sensing can prove to be a very useful tool in monitoring of forests and associated deforestation to a sufficient level of accuracy without the need of physically surveying the forest areas as many of them are physically inaccessible. The methodology for the assessment of forest cover using digital image processing (ERDAS) has been followed. The satellite data for the study was procured from USGS website in the digital format. While procuring the satellite data, care was taken to ensure that the data was cloud and aerosol free by making using of FLAASH atmospheric correction technique. The Normalized Difference Vegetation Index (NDVI) has been used as a numerical indicator of the reduction in ground biomass. NDVI = (near I.R - Red)/ (near I.R + Red). After calculating the NDVI variations and associated mean we have analysed the change in ground biomass. Through this paper we have tried to indicate the rate of deforestation over a given period of time by comparing the forest cover at different time intervals. With the help of remote sensing and GIS techniques it is clearly shows that the total forest cover is continuously degrading and transforming into various land use/land cover category.

Keywords: remote sensing, deforestation, supervised classification, NDVI change detection

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Authors: Selma R. Oliveira, E. W. Cazarini

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Recently, studies have referenced innovation as a key factor affecting the performance of firms. Companies make use of its innovative capacities to achieve sustainable competitive advantage. In this perspective, the objective of this paper is to contribute to innovation planning policies in industry 4.0. Thus, this paper examines the disruptive innovation capacity on firm performance in Europe. This procedure was prepared according to the following phases: Phase 1: Determination of the conceptual model; and Phase 2: Verification of the conceptual model. The research was initially conducted based on the specialized literature, which extracted the data regarding the constructs/structure and content in order to build the model. The research involved the intervention of experts knowledgeable on the object studied, selected by technical-scientific criteria. The data were extracted using an assessment matrix. To reduce subjectivity in the results achieved the following methods were used complementarily and in combination: multicriteria analysis, multivariate analysis, psychometric scaling and neurofuzzy technology. The data were extracted using an assessment matrix and the results were satisfactory, validating the modeling approach.

Keywords: disruptive innovation, capacity, performance, Industry 4.0

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Authors: Jitendra Pratap, Jonathan Sivyer

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Introduction: Dual energy/Spectral CT scanning permits simultaneous acquisition of two x-ray spectra datasets and can complement radiological diagnosis by allowing tissue characterisation (e.g., uric acid vs. non-uric acid renal stones), enhancing structures (e.g. boost iodine signal to improve contrast resolution), and quantifying substances (e.g. iodine density). However, the latter showed inconsistent results between the 2 main modes of dual energy scanning (i.e. dual source vs. dual layer). Therefore, the present study aimed to determine which technology is more accurate in quantifying iodine density. Methods: Twenty vials with known concentrations of iodine solutions were made using Optiray 350 contrast media diluted in sterile water. The concentration of iodine utilised ranged from 0.1 mg/ml to 1.0mg/ml in 0.1mg/ml increments, 1.5 mg/ml to 4.5 mg/ml in 0.5mg/ml increments followed by further concentrations at 5.0 mg/ml, 7mg/ml, 10 mg/ml and 15mg/ml. The vials were scanned using Dual Energy scan mode on a Siemens Somatom Force at 80kV/Sn150kV and 100kV/Sn150kV kilovoltage pairing. The same vials were scanned using Spectral scan mode on a Philips iQon at 120kVp and 140kVp. The images were reconstructed at 5mm thickness and 5mm increment using Br40 kernel on the Siemens Force and B Filter on Philips iQon. Post-processing of the Dual Energy data was performed on vendor-specific Siemens Syngo VIA (VB40) and Philips Intellispace Portal (Ver. 12) for the Spectral data. For each vial and scan mode, the iodine concentration was measured by placing an ROI in the coronal plane. Intraclass correlation analysis was performed on both datasets. Results: The iodine concentrations were reproduced with a high degree of accuracy for Dual Layer CT scanner. Although the Dual Source images showed a greater degree of deviation in measured iodine density for all vials, the dataset acquired at 80kV/Sn150kV had a higher accuracy. Conclusion: Spectral CT scanning by the dual layer technique has higher accuracy for quantitative measurements of iodine density compared to the dual source technique.

Keywords: CT, iodine density, spectral, dual-energy

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Authors: Rhea Kapoor

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Fractal dimension provides a way to characterize non-geometric shapes like those found in nature. The purpose of this research is to estimate Minkowski fractal dimension of human lung images for early detection of lung cancer. Lung cancer is the leading cause of death among all types of cancer and an early histopathological analysis will help reduce deaths primarily due to late diagnosis. A Python application program, PathoPy2.0, was developed for analyzing medical images in pixelated format and estimating Minkowski fractal dimension using a new box-counting algorithm that allows windowing of images for more accurate calculation in the suspected areas of cancerous growth. Benchmark geometric fractals were used to validate the accuracy of the program and changes in fractal dimension of lung images to indicate the presence of issues in the lung. The accuracy of the program for the benchmark examples was between 93-99% of known values of the fractal dimensions. Fractal dimension values were then calculated for lung images, from National Cancer Institute, taken over time to correctly detect the presence of cancerous growth. For example, as the fractal dimension for a given lung increased from 1.19 to 1.27 due to cancerous growth, it represents a significant change in fractal dimension which lies between 1 and 2 for 2-D images. Based on the results obtained on many lung test cases, it was concluded that fractal dimension of human lungs can be used to diagnose lung cancer early. The ideas behind PathoPy2.0 can also be applied to study patterns in the electrical activity of the human brain and DNA matching.

Keywords: fractals, histopathological analysis, image processing, lung cancer, Minkowski dimension

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Authors: John Gaber

Abstract:

Planners regularly use citizen input as empirical data to help them better understand community issues they know very little about. This type of community data is based on the lived experiences of local residents and is known as "emic" data. What is becoming more common practice for planners is their use of data from local experts and stakeholders (known as "etic" data or the outsider perspective) to help them fill in the gaps in their analysis of applied planning research projects. Utilizing international Health Impact Assessment (HIA) data, I look at who planners invite to their citizen input investigations. Research presented in this paper shows that planners access a wide range of emic and etic community perspectives in their search for the “community’s view.” The paper concludes with how planners can chart out a new empirical path in their execution of emic/etic citizen participation strategies in their applied planning research projects.

Keywords: citizen participation, emic data, etic data, Health Impact Assessment (HIA)

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Authors: Sitthichai Laisema, Panita Wannapiroon

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This study is a research and development which is intended to: 1) design ubiquitous collaborative learning activities with virtual teams using CPS processes to develop creative thinking and collaboration skills, and 2) assess the suitability of the ubiquitous collaborative learning activities. Its methods are divided into 2 phases. Phase 1 is the design of ubiquitous collaborative learning activities with virtual teams using CPS processes, phase 2 is the assessment of the suitability of the learning activities. The samples used in this study are 5 professionals in the field of learning activity design, ubiquitous learning, information technology, creative thinking, and collaboration skills. The results showed that ubiquitous collaborative learning activities with virtual teams using CPS processes to develop creative thinking and collaboration skills consist of 3 main steps which are: 1) preparation before learning, 2) learning activities processing and 3) performance appraisal. The result of the learning activities suitability assessment from the professionals is in the highest level.

Keywords: ubiquitous learning, collaborative learning, virtual team, creative problem solving

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Authors: Keigh D. Guinto, Ma. Romina M. Santos

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One of the most catastrophic natural disasters in the Philippines is floods. Twelve (12) million people reside in Metro Manila, National Capital Region (NCR), prone to flooding. A flood can cause widespread devastation resulting in damaged properties and infrastructures and loss of life. By using the analytical hierarchy process, six (6) parameters were selected, namely elevation, slope, lithology, distance from the river, river network density, and flow accumulation. Ranking of these parameters demonstrates that distance from the river with 25.31% and river density with 17.30% ranked the highest causative factor to flooding. This is followed by flow accumulation with 16.72%, elevation with 15.33%, slope with 13.53%, and the least flood causative factor is lithology with 11.8%. The generated flood susceptibility map of Mandaluyong has three (3) classes: high susceptibility, moderate susceptibility, and low susceptibility. The flood susceptibility map generated in this study can be used as an aid for planning flood mitigation, land use planning, and general public awareness. This study can also be used for emergency management and can be applied in the disaster risk management of Mandaluyong.

Keywords: analytical hierarchy process, assessment, flood, geographic information system

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Authors: Kittipon Chittanukul, Chayut Bureethan, Chutimon Piromyaporn

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In Thailand, the natural gas pipeline system requires the preparation of an Environmental Impact Assessment (EIA) report for approval by the relevant agency, the Office of Natural Resources and Environmental Policy and Planning (ONEP), in the pre-construction stage. As of December 2022, PTT has a lot of gas pipeline system spanning around the country. Our experience has shown that the EIA is a significant part of the project plan. In 2011, There was a catastrophic flood in multiple areas of Thailand. It destroyed lives and properties. This event is still in Thai people’s mind. Furthermore, rainfall has been increasing for three consecutive years (2020-2022). Moreover, municipalities are situated in low land river basin and tropical rainfall zone. So many areas still suffer from flooding. Especially in 2022, there will be a 60% increase in water demand compared to the previous year. Therefore, all activities will take into account the quality of the receiving water. The above information emphasizes water and wastewater management are significant in EIA report. PTT has accumulated a large number of lessons learned in water and wastewater management. Our pipeline system execution is composed of EIA stage, construction stage, and operation and maintenance phase. We provide practical Information on water and wastewater management to enhance the EIA process for the pipeline system. The examples of lessons learned in water and wastewater management include techniques to address water and wastewater impact throughout the overall pipelines systems, mitigation measures and monitoring results of these measures. This practical information will alleviate the anxiety of the ONEP committee when approving the EIA report and will build trust among stakeholders in the vicinity of the gas pipeline system area.

Keywords: environmental impact assessment, gas pipeline system, low land basin, high risk flooding area, mitigation measure

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