Search results for: overview of porosity classification

Authors: Khaled Nagaty, Heba Nagaty, Gerard McKee

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A signature is a behavioral biometric that is used for authenticating users in most financial and legal transactions. Signatures can be easily forged by skilled forgers. Therefore, it is essential to verify whether a signature is genuine or forged. The aim of any signature verification algorithm is to accommodate the differences between signatures of the same person and increase the ability to discriminate between signatures of different persons. This work presented in this paper proposes an automatic signature verification system to indicate whether a signature is genuine or not. The system comprises four phases: (1) The pre-processing phase in which image scaling, binarization, image rotation, dilation, thinning, and connecting ridge breaks are applied. (2) The feature extraction phase in which global and local features are extracted. The local features are minutiae points, curvature orientation, and curve plateau. The global features are signature area, signature aspect ratio, and Hu moments. (3) The post-processing phase, in which false minutiae are removed. (4) The classification phase in which features are enhanced before feeding it into the classifier. k-nearest neighbors and support vector machines are used. The classifier was trained on a benchmark dataset to compare the performance of the proposed offline signature verification system against the state-of-the-art. The accuracy of the proposed system is 92.3%.

Keywords: signature, ridge breaks, minutiae, orientation

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Authors: Vephkhvia Grigalashvili

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This paper provides an overview of criteria for good governance in Georgian defense sector and scientific outcomes of comparative research. A respect for good governance and its realization into Georgian national defense sector represents a fundamental institutional necessity as well as country`s politico-legal obligation within the framework of the existing collaboration mechanisms with NATO (especially Building Integrity (BI) Programme) and the Association Agreement between the EU and Georgia. Furthermore good governance is considered as a democracy measuring criterion in country`s Euro-Atlantic integration process. Accordingly, integration and further development of the contemporary approaches of good governance into Georgian defense management model represents a burning issue of the country. The assessment of an existing model of the country, identification of defects and determination of course of institutional reforms in a mutual comparison format of good governance mechanisms of NATO or/and the EU member Eastern European or Baltic countries positively assessed by the international organizations is considered as a precondition for its effective realization. Scientific aims of this study are: (a) to conduct comparative analysis of Georgian national principles and generalized standards of NATO or/and the EU member Eastern European and Baltic countries in following segments of good governance: open governance; anticorruption policy; conflict of interests; integrity; internal and external control bodies; (b) to formulate theoretical and practical recommendations on reforms to be implemented in the country`s national defence sector. As research reveals, although, institutional / legal pillars of good governance in Georgian defense sector generally are in compliance with international principles, the quality of implementation of good government norms still remains as an area that needs further development by raising awareness of public servants and community.

Keywords: anti-corruption policy within Georgian defense governance, conflict of interests within Georgian defense governance, good governance in Georgian defense sector, principles of integrity in Georgian defense management

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Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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Authors: Cheng-Jhe Lee, Chiun-Chieh Hsu

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Due to the rapid development of the Internet and the increased availability of digital documents, the excessive information on the Internet has led to information overflow problem. In order to solve these problems for effective information retrieval, document clustering in text mining becomes a popular research topic. Clustering is the unsupervised classification of data items into groups without the need of training data. Many conventional document clustering methods perform inefficiently for large document collections because they were originally designed for relational database. Therefore they are impractical in real-world document clustering and require special handling for high dimensionality and high volume. We propose the FIHC (Frequent Itemset-based Hierarchical Clustering) method, which is a hierarchical clustering method developed for document clustering, where the intuition of FIHC is that there exist some common words for each cluster. FIHC uses such words to cluster documents and builds hierarchical topic tree. In this paper, we combine FIHC algorithm with ontology to solve the semantic problem and mine the meaning behind the words in documents. Furthermore, we use the closed frequent itemsets instead of only use frequent itemsets, which increases efficiency and scalability. The experimental results show that our method is more accurate than those of well-known document clustering algorithms.

Keywords: FIHC, documents clustering, ontology, closed frequent itemset

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Authors: Sher Ali

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Organic agriculture has gained significant momentum globally as a sustainable and environmentally friendly farming practice. In Pakistan, amidst growing concerns about food security, environmental degradation, and health issues related to conventional farming methods, the adoption of organic agriculture presents a promising pathway for agricultural development. This abstract aims to provide an overview of the status, opportunities, challenges, and future directions of organic agriculture in Pakistan. It delves into the current state of organic farming practices, including the extent of adoption, key crops cultivated, and the regulatory framework governing organic certification. Furthermore, the abstract discusses the unique opportunities that Pakistan offers for organic agriculture, such as its diverse agro-climatic zones, rich biodiversity, and traditional farming knowledge. It highlights successful initiatives and case studies that showcase the potential of organic farming to improve rural livelihoods, enhance food security, and promote sustainable agricultural practices. However, the abstract also addresses the challenges hindering the widespread adoption of organic agriculture in Pakistan, ranging from limited awareness and technical know-how among farmers to inadequate infrastructure and market linkages. It emphasizes the need for supportive policies, capacity-building programs, and investment in research and extension services to overcome these challenges and promote the growth of the organic agriculture sector. Lastly, the abstract outlines future directions and recommendations for advancing organic agriculture in Pakistan, including strategies for scaling up production, strengthening certification mechanisms, and fostering collaboration among stakeholders. By shedding light on the opportunities, challenges, and potential of organic agriculture in Pakistan, this abstract aims to contribute to the discourse on sustainable farming practices at the upcoming Agro Conference in the USA. It invites participants to engage in dialogue, share experiences, and explore avenues for collaboration toward promoting organic agriculture for a healthier, more resilient food system.

Keywords: agriculture, challenges, organic, Pakistan

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Authors: Mina Adel Shokry Fahim, Jūratė Sužiedelytė Visockienė

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With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realisation often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.

Keywords: air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter

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Authors: Nikola Tomasevic, Marko Batic, Sanja Vranes

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Emerging Semantic Web technologies can be seen as the next step in evolution of the intelligent facility management systems. Particularly, this considers increased usage of open source and/or standardized concepts for data classification and semantic interpretation. To deliver such facility management systems, providing the comprehensive integration and interoperability platform in from of the facility data model is a prerequisite. In this paper, one of the possible modelling approaches to provide such integrative facility data model which was based on the ontology modelling concept was presented. Complete ontology development process, starting from the input data acquisition, ontology concepts definition and finally ontology concepts population, was described. At the beginning, the core facility ontology was developed representing the generic facility infrastructure comprised of the common facility concepts relevant from the facility management perspective. To develop the data model of a specific facility infrastructure, first extension and then population of the core facility ontology was performed. For the development of the full-blown facility data models, Malpensa and Fiumicino airports in Italy, two major European air-traffic hubs, were chosen as a test-bed platform. Furthermore, the way how these ontology models supported the integration and interoperability of the overall airport energy management system was analyzed as well.

Keywords: airport ontology, energy management, facility data model, ontology modeling

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Authors: Win Thandar Soe, Innocent Mpawenimana, Mathieu Di Fazio, Cécile Belleudy, Aung Ze Ya

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Intelligent electronic equipment and automation network is the brain of high-tech energy management systems in critical role of smart homes dominance. Smart home is a technology integration for greater comfort, autonomy, reduced cost, and energy saving as well. These services can be provided to home owners for managing their home appliances locally or remotely and consequently allow them to automate intelligently and responsibly their consumption by individual or collective control systems. In this study, three smart plugs are described and one of them tested on typical household appliances. This article proposes to collect the data from the wireless technology and to extract some smart data for energy management system. This smart data is to quantify for three kinds of load: intermittent load, phantom load and continuous load. Phantom load is a waste power that is one of unnoticed power of each appliance while connected or disconnected to the main. Intermittent load and continuous load take in to consideration the power and using time of home appliances. By analysing the classification of loads, this smart data will be provided to reduce the communication of wireless sensor network for energy management system.

Keywords: energy management, load profile, smart plug, wireless sensor network

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Authors: Giang Ngo Tinh Nguyen

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Agriculture plays an important role in the economy of Vietnam and coffee is one of most crucial agricultural commodities for exporting but the current farming methods and processing infrastructure could not keep up with the development of the sector. There are many catastrophic impacts on the environment such as deforestation; soil degradation that leads to a decrease in the quality of coffee beans. Therefore, improving supply chain to develop the cultivation of sustainable coffee is one of the most important strategies to boost the coffee industry and create a competitive advantage for Vietnamese coffee in the worldwide market. If all stakeholders in the supply chain network unite together; the sustainable production of coffee will be scaled up and the future of coffee industry will be firmly secured. Buon Ma Thuot city, Dak Lak province is the principal growing region for Vietnamese coffee which accounted for a third of total coffee area in Vietnam. It plays a strategically crucial role in the development of sustainable Vietnamese coffee. Thus, the research is to improve the supply chain of sustainable Vietnamese coffee production in Buon Ma Thuot city, Dak Lak province, Vietnam for the purpose of increasing the yields and export availability as well as helping coffee farmers to be more flexible in an ever-changing market situation. It will help to affirm Vietnamese coffee brand when entering international market; improve the livelihood of farmers and conserve the environment of this area. Besides, after analyzing the data, a logistic regression model is established to explain the relationship between the dependent variable and independent variables to help sustainable coffee organizations forecast the probability of farmer will be having a sustainable certificate with their current situation and help them choose promising candidates to develop sustainable programs. It investigates opinions of local farmers through quantitative surveys. Qualitative interviews are also used to interview local collectors and staff of Trung Nguyen manufacturing company to have an overview of the situation.

Keywords: supply chain management, sustainable agricultural development, sustainable coffee, Vietnamese coffee

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Authors: Ebru Turgal, Beyza Doganay Erdogan

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Machine learning aims to model the relationship between the response and features. Medical decision-making researchers would like to make decisions about patients’ course and treatment, by examining the repeated measurements over time. Boosting approach is now being used in machine learning area for these aims as an influential tool. The aim of this study is to show the usage of multivariate tree boosting in this field. The main reason for utilizing this approach in the field of decision-making is the ease solutions of complex relationships. To show how multivariate tree boosting method can be used to identify important features and feature-time interaction, we used the data, which was collected retrospectively from Ankara University Chest Diseases Department records. Dataset includes repeated PF ratio measurements. The follow-up time is planned for 120 hours. A set of different models is tested. In conclusion, main idea of classification with weighed combination of classifiers is a reliable method which was shown with simulations several times. Furthermore, time varying variables will be taken into consideration within this concept and it could be possible to make accurate decisions about regression and survival problems.

Keywords: boosted multivariate trees, longitudinal data, multivariate regression tree, panel data

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Authors: Dang Thuy Binh, Truong Thi Oanh, Le Phan Khanh Hung, Luong thi Tuong Vy

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One of the greatest environmental challenges facing Asia is the management and conservation of the marine biodiversity threaten by fisheries overexploitation, pollution, habitat destruction, and climate change. To date, a few molecular taxonomical studies has been conducted on marine fauna in Vietnam. The purpose of this study was to clarify the phylogeny of economic and ecological reef fish species in Vietnam Reef fish species covering Labridae, Scaridae, Nemipteridae, Serranidae, Acanthuridae, Lutjanidae, Lethrinidae, Mullidae, Balistidae, Pseudochromidae, Pinguipedidae, Fistulariidae, Holocentridae, Synodontidae, and Pomacentridae representing 28 genera were collected from South and Center, Vietnam. Combine with Genbank sequences, a phylogenetic tree was constructed based on 16S gene of mitochondrial DNA using maximum parsimony, maximum likelihood, and Bayesian inference approaches. The phylogram showed the well-resolved clades at genus and family level. Perciformes is the major order of reef fish species in Vietnam. The monophyly of Perciformes is not strongly supported as it was clustered in the same clade with Tetraodontiformes syngnathiformes and Beryciformes. Continue sampling of commercial fish species and classification based on morphology and genetics to build DNA barcoding of fish species in Vietnam is really necessary.

Keywords: reef fish, 16s rDNA, Vietnam, phylogeny

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Authors: Djeddi Khaled, Houssou Hind, Miloudi Abdelatif, Rabah Siham

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Camels play a vital role as multipurpose animals, providing milk meat and serving as a means of transportation. They serve as a financial reserve for pastoralists and hold significant cultural and social value. Camel milk, known for its exceptional nutritional properties, is considered a valuable substitute for human milk. However, udder infections, particularly mastitis, pose significant challenges to camel farming. Clinical and subclinical mastitis can lead to substantial economic losses. Mastitis, especially the subclinical form, is a persistent and prevalent condition affecting milk hygiene and quality in dairy camels. This review offers insights into the prevalence and risk factors associated with subclinical mastitis in camels. The prevalence of subclinical mastitis in dairy camels was found to range from 9.28% to 87.78%. Major pathogens responsible for camel mastitis include Staphylococcus aureus, Coagulase-negative Staphylococcus, Streptococcus agalactiae, Streptococcus dysgalactiae, Escherichia coli, Micrococcus spp, Pasteurella haemolytica and Corynebacterium spp. The study outlines key risk factors contributing to camel mastitis, emphasizing factors such as severe tick infestation, age, stage of lactation, parity, body condition score, skin lesion on the teats or udders, anti-suckling devices, previous history of the udder, conformation of the udder, breed, unhygienic milking practices, production system, amongst others have been reported to be important in the prevalence of subclinical mastitis. This comprehensive overview provides valuable insights into the multifaceted aspects of camel mastitis, encompassing prevalent bacterial pathogens and diverse risk factors. The findings underscore the importance of holistic management practices, emphasizing hygiene, health monitoring, and targeted interventions to ensure the well-being and productivity of camels in various agro-pastoral contexts.

Keywords: bacterial pathogens, camel, mastitis, risk factors

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Musatafa Abbas Abbood Albadr, Masri Ayob, Sabrina Tiun, Fahad Taha Al-Dhief, Mohammad Kamrul Hasan

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Breast Cancer (BC) is considered one of the most frequent reasons of cancer death in women between 40 to 55 ages. The BC is diagnosed by using digital images of the FNA (Fine Needle Aspirate) for both benign and malignant tumors of the breast mass. Therefore, this work proposes the Online Sequential Extreme Learning Machine (OSELM) algorithm for diagnosing BC by using the tumor features of the breast mass. The current work has used the Wisconsin Diagnosis Breast Cancer (WDBC) dataset, which contains 569 samples (i.e., 357 samples for benign class and 212 samples for malignant class). Further, numerous measurements of assessment were used in order to evaluate the proposed OSELM algorithm, such as specificity, precision, F-measure, accuracy, G-mean, MCC, and recall. According to the outcomes of the experiment, the highest performance of the proposed OSELM was accomplished with 97.66% accuracy, 98.39% recall, 95.31% precision, 97.25% specificity, 96.83% F-measure, 95.00% MCC, and 96.84% G-Mean. The proposed OSELM algorithm demonstrates promising results in diagnosing BC. Besides, the performance of the proposed OSELM algorithm was superior to all its comparatives with respect to the rate of classification.

Keywords: breast cancer, machine learning, online sequential extreme learning machine, artificial intelligence

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Authors: Shriya Shukla, Lachin Fernando

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Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

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Authors: Panwasn Mahalawalert

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The purposes of this research were analyzed differential item functioning and differential test functioning of SWUSAT aptitude test classification by sex variable. The data used in this research is the secondary data from Srinakharinwirot University Scholastic Aptitude Test 2013 (SWUSAT). SWUSAT test consists of four subjects. There are verbal ability test, number ability test, reasoning ability test and spatial ability test. The data analysis was analyzed in 2 steps. The first step was analyzing descriptive statistics. In the second step were analyzed differential item functioning (DIF) and differential test functioning (DTF) by using the DIFAS program. The research results were as follows: The results of DIF and DTF analysis for all 10 tests in year 2013. Gender was the characteristic that found DIF all 10 tests. The percentage of item number that found DIF is between 6.67% - 60%. There are 5 tests that most of items favors female group and 2 tests that most of items favors male group. There are 3 tests that the number of items favors female group equal favors male group. For Differential test functioning (DTF), there are 8 tests that have small level.

Keywords: aptitude test, differential item functioning, differential test functioning, educational measurement

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Authors: K. Yuki, R. Tsuji, K. Takai, S. Aramaki, R. Kibushi, N. Unno, K. Suzuki

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A small cold plate with uni-directional porous copper is proposed for cooling power electronics such as an on-vehicle inverter with the heat generation of approximately 500 W/cm2. The uni-directional porous copper with the pore perpendicularly orienting the heat transfer surface is soldered to a grooved heat transfer surface. This structure enables the cooling liquid to evaporate in the pore of the porous copper and then the vapor to discharge through the grooves. In order to minimize the cold plate, a double flow channel concept is introduced for the design of the cold plate. The cold plate consists of a base plate, a spacer, and a vapor discharging plate, totally 12 mm in thickness. The base plate has multiple nozzles of 1.0 mm in diameter for the liquid supply and 4 slits of 2.0 mm in width for vapor discharging, and is attached onto the top surface of the porous copper plate of 20 mm in diameter and 5.0 mm in thickness. The pore size is 0.36 mm and the porosity is 36 %. The cooling liquid flows into the porous copper as an impinging jet flow from the multiple nozzles, and then the vapor, which is generated in the pore, is discharged through the grooves and the vapor slits outside the cold plate. A heated test section consists of the cold plate, which was explained above, and a heat transfer copper block with 6 cartridge heaters. The cross section of the heat transfer block is reduced in order to increase the heat flux. The top surface of the block is the grooved heat transfer surface of 10 mm in diameter at which the porous copper is soldered. The grooves are fabricated like latticework, and the width and depth are 1.0 mm and 0.5 mm, respectively. By embedding three thermocouples in the cylindrical part of the heat transfer block, the temperature of the heat transfer surface ant the heat flux are extrapolated in a steady state. In this experiment, the flow rate is 0.5 L/min and the flow velocity at each nozzle is 0.27 m/s. The liquid inlet temperature is 60 °C. The experimental results prove that, in a single-phase heat transfer regime, the heat transfer performance of the cold plate with the uni-directional porous copper is 2.1 times higher than that without the porous copper, though the pressure loss with the porous copper also becomes higher than that without the porous copper. As to the two-phase heat transfer regime, the critical heat flux increases by approximately 35% by introducing the uni-directional porous copper, compared with the CHF of the multiple impinging jet flow. In addition, we confirmed that these heat transfer data was much higher than that of the ordinary single impinging jet flow. These heat transfer data prove high potential of the cold plate with the uni-directional porous copper from the view point of not only the heat transfer performance but also energy saving.

Keywords: cooling, cold plate, uni-porous media, heat transfer

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Authors: Ruangdech Sirikit

Abstract:

The purposes of this study were analyzed differential item functioning and differential test functioning of SWUSAT aptitude test classification by sex variable. The data used in this research is the secondary data from Srinakharinwirot University Scholastic Aptitude Test 2011 (SWUSAT 2011) SWUSAT test consists of four subjects. There are verbal ability test, number ability test, reasoning ability test and spatial ability test. The data analysis was carried out in 2 steps. The first step was analyzing descriptive statistics. In the second step were analyzed differential item functioning (DIF) and differential test functioning (DTF) by using the DIFAS program. The research results were as follows: The results of data analysis for all 10 tests in year 2011. Sex was the characteristic that found DIF all 10 tests. The percentage of item number that found DIF was between 10% - 46.67%. There are 4 tests that most of items favors female group. There are 3 tests that most of items favors male group and there are 3 tests that the number of items favors female group equal favors male group. For Differential test functioning (DTF), there are 8 tests that have small DIF effect variance.

Keywords: differential item functioning, differential test functioning, SWUSAT, aptitude test

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Authors: Bandar Alahmadi, Lethia Jackson

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Machine Learning model is used today in many real-life applications. The safety and security of such model is important, so the results of the model are as accurate as possible. One challenge of machine learning model security is the adversarial examples attack. Adversarial examples are designed by the attacker to cause the machine learning model to misclassify the input. We propose a method to generate adversarial examples to attack image classifiers. We are modifying the successfully classified images, so a classifier misclassifies them after the modification. In our method, we do not update the whole image, but instead we detect the important region, modify it, place it back to the original image, and then run it through a classifier. The algorithm modifies the detected region using two methods. First, it will add abstract image matrix on back of the detected image matrix. Then, it will perform a rotation attack to rotate the detected region around its axes, and embed the trace of image in image background. Finally, the attacked region is placed in its original position, from where it was removed, and a smoothing filter is applied to smooth the background with foreground. We test our method in cascade classifier, and the algorithm is efficient, the classifier confident has dropped to almost zero. We also try it in CNN (Convolutional neural network) with higher setting and the algorithm was successfully worked.

Keywords: adversarial examples, attack, computer vision, image processing

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Authors: Malarvizhi Selvaraj, Paul Fergus, Andy Shaw

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Uterine contractions produced during labour have the potential to damage the foetus by diminishing the maternal blood flow to the placenta. In order to observe this phenomenon labour and delivery are routinely monitored using cardiotocography monitors. An obstetrician usually makes the diagnosis of foetus hypoxia by interpreting cardiotocography recordings. However, cardiotocography capture and interpretation is time-consuming and subjective, often lead to misclassification that causes damage to the foetus and unnecessary caesarean section. Both of these have a high impact on the foetus and the cost to the national healthcare services. Automatic detection of foetal heart rate may be an objective solution to help to reduce unnecessary medical interventions, as reported in several studies. This paper aim is to provide a system for better identification and interpretation of abnormalities of the fetal heart rate using RStudio. An open dataset of 552 Intrapartum recordings has been filtered with 0.034 Hz filters in an attempt to remove noise while keeping as much of the discriminative data as possible. Features were chosen following an extensive literature review, which concluded with FIGO features such as acceleration, deceleration, mean, variance and standard derivation. The five features were extracted from 552 recordings. Using these features, recordings will be classified either normal or abnormal. If the recording is abnormal, it has got more chances of hypoxia.

Keywords: cardiotocography, foetus, intrapartum, hypoxia

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Authors: Claudia Valderrama-Ulloa, Cristian Schmitt, Juan Pablo Marchetti, Viviana Bucarey

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Access to adequate housing is a fundamental human right and a crucial factor for health. Housing should be inclusive, accessible, and able to meet the needs of all its inhabitants at every stage of their lives without hindering their health, autonomy, or independence. This article addresses the importance of designing housing for people with disabilities, which varies depending on individual abilities, preferences, and cultural considerations. Based on the components of the International Classification of Functioning, Disability and Health, wheelchair users, little people (achondroplasia), children with autism spectrum disorder and Down syndrome were characterized, and six domains of activities related to daily life inside homes were defined. The article describes the main barriers homes present for this group of people. It proposes a list of architectural and design aspects to reduce barriers to housing use. The aspects are divided into three main groups: space management, building services, and supporting facilities. The article emphasizes the importance of consulting professionals and users with experience designing for diverse needs to create inclusive, safe, and supportive housing for people with disabilities.

Keywords: achondroplasia, autism spectrum disorder, disability, down syndrome, wheelchair user

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Authors: Tuji Jemal Ahmed

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Food security and utilization are critical aspects of ensuring the well-being and prosperity of a nation. This paper examines the current state of food security and utilization in Ethiopia, focusing on the challenges, opportunities, and strategies employed to address the issue. Ethiopia, a country in East Africa, has made significant progress in recent years to improve food security and utilization for its population. However, persistent challenges such as recurrent droughts, limited access to resources, and low agricultural productivity continue to pose obstacles to achieving sustainable food security. The paper begins by providing an overview of the concept of food security, emphasizing its multidimensional nature and the importance of access, availability, utilization, and stability. It then explores the specific factors influencing food security and utilization in Ethiopia, including natural resources, climate variability, agricultural practices, infrastructure, and socio-economic factors. Furthermore, the paper highlights the initiatives and interventions implemented by the Ethiopian government, non-governmental organizations, and international partners to enhance food security and utilization. These efforts include agricultural extension programs, irrigation projects, investments in rural infrastructure, and social safety nets to protect vulnerable populations. The study also examines the role of technology and innovation in improving food security and utilization in Ethiopia. It explores the potential of sustainable agricultural practices, such as conservation agriculture, improved seed varieties, and precision farming techniques. Additionally, it discusses the role of digital technologies in enhancing access to market information, financial services, and agricultural inputs for smallholder farmers. Finally, the paper discusses the importance of collaboration and partnerships between stakeholders, including government agencies, development organizations, research institutions, and communities, in addressing food security and utilization challenges. It emphasizes the need for integrated and holistic approaches that consider both production and consumption aspects of the food system.

Keywords: food security, utilization, Ethiopia, challenges

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Authors: Tuji Jemal Ahmed

Abstract:

Food security and utilization are critical aspects of ensuring the well-being and prosperity of a nation. This paper examines the current state of food security and utilization in Ethiopia, focusing on the challenges, opportunities, and strategies employed to address the issue. Ethiopia, a country in East Africa, has made significant progress in recent years to improve food security and utilization for its population. However, persistent challenges such as recurrent droughts, limited access to resources, and low agricultural productivity continue to pose obstacles to achieving sustainable food security. The paper begins by providing an overview of the concept of food security, emphasizing its multidimensional nature and the importance of access, availability, utilization, and stability. It then explores the specific factors influencing food security and utilization in Ethiopia, including natural resources, climate variability, agricultural practices, infrastructure, and socio-economic factors. Furthermore, the paper highlights the initiatives and interventions implemented by the Ethiopian government, non-governmental organizations, and international partners to enhance food security and utilization. These efforts include agricultural extension programs, irrigation projects, investments in rural infrastructure, and social safety nets to protect vulnerable populations. The study also examines the role of technology and innovation in improving food security and utilization in Ethiopia. It explores the potential of sustainable agricultural practices, such as conservation agriculture, improved seed varieties, and precision farming techniques. Additionally, it discusses the role of digital technologies in enhancing access to market information, financial services, and agricultural inputs for smallholder farmers. Finally, the paper discusses the importance of collaboration and partnerships between stakeholders, including government agencies, development organizations, research institutions, and communities, in addressing food security and utilization challenges. It emphasizes the need for integrated and holistic approaches that consider both production and consumption aspects of the food system.

Keywords: food security, utilization, Ethiopia, challenges

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Authors: Hao Li, Guy Y. Cornejo Maceda, Yiqing Li, Jianguo Tan, Marek Morzynski, Bernd R. Noack

Abstract:

We propose an automated analysis of the flow control behaviour from an ensemble of control laws and associated time-resolved flow snapshots. The input may be the rich database of machine learning control (MLC) optimizing a feedback law for a cost function in the plant. The proposed methodology provides (1) insights into the control landscape, which maps control laws to performance, including extrema and ridge-lines, (2) a catalogue of representative flow states and their contribution to cost function for investigated control laws and (3) visualization of the dynamics. Key enablers are classification and feature extraction methods of machine learning. The analysis is successfully applied to the stabilization of a mixing layer with sensor-based feedback driving an upstream actuator. The fluctuation energy is reduced by 26%. The control replaces unforced Kelvin-Helmholtz vortices with subsequent vortex pairing by higher-frequency Kelvin-Helmholtz structures of lower energy. These efforts target a human interpretable, fully automated analysis of MLC identifying qualitatively different actuation regimes, distilling corresponding coherent structures, and developing a digital twin of the plant.

Keywords: machine learning control, mixing layer, feedback control, model-free control

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Authors: Khalifa Alatresh, Mirac Aydin

Abstract:

This research explores the impacts of irrigation by treated wastewater (TWW) on the mineralogical and chemical attributes of sandy calcareous soils in the Southern region of Misurata. Soil samples obtained from three horizons (A, B, and C) of six TWW-irrigated pedons (29years) and six other pedons from nearby non-irrigated areas (dry-control). The results demonstrated that the TWW-irrigated pedons had significantly higher salinity (EC), sodium adsorption ratio (SAR), exchangeable sodium percentage (ESP), cation exchange capacity (CEC), available phosphor (AP), total nitrogen (TN), and organic matter (OM) relative to the control pedons. Nonetheless, all the values of interest (EC < 4000 µs/cm < SAR < 13, pH < 8.5 and ESP < 15) remained lower than the thresholds, showing no issues with sodicity or salinity. Irrigated pedons contained significantly higher amounts of total clay and showed an altered distribution of particle sizes and minerals identified (quartz, calcite, microcline, albite, anorthite, and dolomite) within the profile. The observed results included the occurrence of Margarite, Anorthite, Chabazite, and Tridymite minerals after the application of TWW in small quantities that are not enough to influence soil genesis and classification.0,51 cm.

Keywords: treated wastewater, sandy calcareous soils, soil mineralogy, and chemistry

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Authors: Christelle Tchoupé Makougoum

Abstract:

In the context of agriculture, differences across localities in term of climate change can create systematic variation among farmers technical efficiency. Failure to account for climate variability could lead to wrong conclusions about farmers’ technical efficiency and also it could bias the ranking of farmers according to their managerial performance. The literature on agricultural productivity has given little attention to this issue whereas it is necessary for establishing to what extent climate affects farmers efficiency. This article contributes to the preview literature by two ways. First, it proposed a new econometric model that accounting for the climate change influences on technical efficiency in the specific area of agriculture. Second it estimates the inefficiency due to climate change and the real managerial performance of Malian farmers. Using the Mali’s data from agricultural census and CRU TS3 climatic database we implemented an adjusted stochastic frontier methodology to account for the impact of environmental factors. The results yield three main findings. First, instability in temperatures and rainfall decreases technical efficiency on average. Second, the climate change modifies the classification of the farmers according to their efficiency scores. Thirdly it is noted that, although climate changes are partly responsible for the deviation from the border, the capacity of farmers to combine inputs into the optimal proportion is more to undermine. The study concluded that improving farmer efficiency should include fostering their resilience to climate change.

Keywords: agriculture, climate change, stochastic production function, technical efficiency

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Authors: Christian Ihwiwhu, Nnamdi Obioha, Udeme John, Edward Bobade, Oghenerunor Bekibele, Adedeji Awujoola, Ibi-Ada Itotoi

Abstract:

Understanding the complexity in the distribution of hydrocarbon in a simple structure with flow baffles and connectivity issues is critical in targeting and developing the remaining pay in a mature asset. Subtle facies changes (heterogeneity) can have a drastic impact on reservoir fluids movement, and this can be crucial to identifying sweet spots in mature fields. This study aims to evaluate selected reservoirs in Ovhor Field, Niger Delta, Nigeria, with the objective of optimising production from the field by targeting undeveloped oil reserves, bypassed pay, and gaining an improved understanding of the selected reservoirs to increase the company’s reservoir limits. The task at the Ovhor field is complicated by poor stratigraphic seismic resolution over the field. 3-D geological (sedimentology and stratigraphy) interpretation, use of results from quantitative interpretation, and proper understanding of production data have been used in recognizing flow baffles and undeveloped compartments in the field. The full field 3-D model has been constructed in such a way as to capture heterogeneities and the various compartments in the field to aid the proper simulation of fluid flow in the field for future production prediction, proper history matching and design of good trajectories to adequately target undeveloped oil in the field. Reservoir property models (porosity, permeability, and net-to-gross) have been constructed by biasing log interpreted properties to a defined environment of deposition model whose interpretation captures the heterogeneities expected in the studied reservoirs. At least, two scenarios have been modelled for most of the studied reservoirs to capture the range of uncertainties we are dealing with. The total original oil in-place volume for the four reservoirs studied is 157 MMstb. The cumulative oil and gas production from the selected reservoirs are 67.64 MMstb and 9.76 Bscf respectively, with current production rate of about 7035 bopd and 4.38 MMscf/d (as at 31/08/2019). Dynamic simulation and production forecast on the 4 reservoirs gave an undeveloped reserve of about 3.82 MMstb from two (2) identified oil restoration activities. These activities include side-tracking and re-perforation of existing wells. This integrated approach led to the identification of bypassed oil in some areas of the selected reservoirs and an improved understanding of the studied reservoirs. New wells have/are being drilled now to test the results of our studies, and the results are very confirmatory and satisfying.

Keywords: facies, flow baffle, bypassed pay, heterogeneities, history matching, reservoir limit

Procedia PDF Downloads 121

Authors: B. Raghu, Shashi Vemuri, Ch. Sreenivasa Rao

Abstract:

The survey on pesticide use pattern was carried out by interviewing farmers growing chill in open fields and poly houses based on the questionnaire prepared to assess their knowledge and practices on crop cultivation, general awareness on pesticide recommendations and use. Education levels of poly house farmers are high compared to open field farmers, where 57.14% poly house farmers are high school educated, whereas 35% open field farmers are illiterates. Majority farmers use nursery of 35 days and grow in <0.5 acre poly house in summer and rabi and < 1 acre in open field during kharif. Awareness on pesticide related issues is varying among poly house and open field farmers with some commonality, where 28.57% poly house farmers know about recommended pesticides while only 10% open field farmers are aware of this issue. However, in general, all farmers contact pesticide dealer for recommendations, poly house farmers prefer to contact scientists (35.71%) and open field farmers prefer to contact agricultural officers (33.33). Most farmers are unaware about pesticide classification and toxicity symbols on packing. Farmers are aware about endosulfan ban, but only 21.42% poly house and 11.66% open field farmers know about ban of monocrotofos on vegetables. Very few farmers know about pesticide residues and related issues, but know washing helps to reduce contamination.

Keywords: open field, pesticide usage, polyhouses, residues survey

Procedia PDF Downloads 461

Authors: Baladitya Swaika, Rahul Khatry

Abstract:

Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

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Authors: Neha Devi, P. K. Joshi

Abstract:

Fire fuel map is one of the most critical factors for planning and managing the fire hazard and risk. One of the most significant forms of global disturbance, impacting community dynamics, biogeochemical cycles and local and regional climate across a wide range of ecosystems ranging from boreal forests to tropical rainforest is wildfire Assessment of fire danger is a function of forest type, fuelwood stock volume, moisture content, degree of senescence and fire management strategy adopted in the ground. Remote sensing has potential of reduction the uncertainty in mapping fuels. Hyperspectral remote sensing is emerging to be a very promising technology for wildfire fuels characterization. Fine spectral information also facilitates mapping of biophysical and chemical information that is directly related to the quality of forest fire fuels including above ground live biomass, canopy moisture, etc. We used Hyperion imagery acquired in February, 2016 and analysed four fuel characteristics using Hyperion sensor data on-board EO-1 satellite, acquired over the Shiwalik Himalayas covering the area of Champawat, Uttarakhand state. The main objective of this study was to present an overview of methodologies for mapping fuel properties using hyperspectral remote sensing data. Fuel characteristics analysed include fuel biomass, fuel moisture, and fuel condition and fuel type. Fuel moisture and fuel biomass were assessed through the expression of the liquid water bands. Fuel condition and type was assessed using green vegetation, non-photosynthetic vegetation and soil as Endmember for spectral mixture analysis. Linear Spectral Unmixing, a partial spectral unmixing algorithm, was used to identify the spectral abundance of green vegetation, non-photosynthetic vegetation and soil.

Keywords: forest fire fuel, Hyperion, hyperspectral, linear spectral unmixing, spectral mixture analysis

Procedia PDF Downloads 154