Search results for: rain tree pods

Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Dalia. G. Aseel

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Begomoviruses are economically important plant viruses that infect dicotyledonous plants and exclusively transmitted by the whitefly Bemisia tabaci. Here, replicative form was isolated from Okra, Cotton, Tomato plants and whitefly infected with Begomoviruses. Using coat protein specific primers (AV1), the viral infection was verified with amplicon at 450 bp. The sequence of OLCuV-AV1 gene was recorded and received an accession number (FJ441605) from Genebank. The phylogenetic tree of OLCuV was closely related to Okra leaf curl virus previously isolated from Cameroon and USA with nucleotide sequence identity of 92%. The protein purification was carried out using His-Tag methodology by using Affinity Chromatography. The purified protein was separated on SDS-PAGE analysis and an enriched expected size of band at 30 kDa was observed. Furthermore, RAPD and SDS-PAGE were used to detect genetic variability between different hosts of okra leaf curl virus (OLCuV), cotton leaf curl virus (CLCuV), tomato yellow leaf curl virus (TYLCuV) and the whitefly vector. Finally, the present study would help to understand the relationship between the whitefly and different economical crops in Egypt.

Keywords: okra leaf curl virus, AV1 gene, sequencing, phylogenetic, cloning, purified protein, genetic diversity and viral proteins

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Authors: C. C. Igwe

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Several parts of Neem tree (Azadirachta indica) are used in traditional medicine in many West African countries for the treatment of various human diseases. The leaf, stem - bark and seed were air dried for 8, 5 and 7 days, respectively. The shells were carfully separated from the seeds, each powdered sample obtained with mechanical miller and 250 mm sieve. The neem samples were individually subjected to extraction with acetone, n-hexane for 48hr and 72 hr, respectively. Physico-chemical and antibacterial evaluation were carried out using standard methods. Results of physico - chemical analyses of the extracted oil from the seed shows that it has a brownish colour, with a smell similar to garlic while the moisture content, refractive index are 0.76% and 1.47 respectively. Other vital chemical results obtained from the neem oil such as saponification value (234.62), acid value (10.84 %), free fatty acid (5.84 %) and peroxide value (10.52%) indicated the oil extracted satisfied standard oils parameters for quality soap and cosmetics production. The antibacterial screening by disc diffusion revealed the oil demonstrated high activity against Staphylococcus aureus. Both the physio-chemical and antibacterial of samples have been certified by National Agency for Food and Drugs Administration and Control. The preliminary results of this study may validate the medicinal value of the plant. Further studies are in progress to clarify the in vivo potentials of neem extracts in the management of human communicable diseases and this is a subject of investigation in our group.

Keywords: anti-bacterial, neem extract, physico-chemical analyses, staphylococcus aureus

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Authors: H. Aljabiry, L. Bailey, S. Young

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Qatar is a desert with an average temperature 37⁰C, reaching over 40⁰C during summer. Precipitation is uncommon and mostly in winter. Qatar depends on desalination for drinking water and on groundwater and recycled water for irrigation. Water consumption and network leakage per capita in Qatar are amongst the highest in the world; re-use of treated wastewater is extremely limited with only 14% of treated wastewater being used for irrigation. This has led to the country disposing of unwanted water from various sources in lagoons situated around the country, causing concern over the possibility of environmental pollution. Accordingly, our hypothesis underpinning this research is that the quality and quantity of water in lagoons is having an impact on the groundwater reservoirs in Qatar. Lagoons (n = 14) and wells (n = 55) were sampled for both summer and winter in 2018 (summer and winter). Water, adjoining soil and plant samples were analysed for multiple elements by Inductively Coupled Plasma Mass Spectrometry. Organic and inorganic carbon were measured (CN analyser) and the major anions were determined by ion chromatography. Salinization in both the lagoon and the wells was seen with good correlations between Cl⁻, Na⁺, Li, SO₄, S, Sr, Ca, Ti (p-value < 0.05). Association of heavy metals was observed of Ni, Cu, Ag, and V, Cr, Mo, Cd which is due to contamination from anthropological activities such as wastewater disposal or spread of contaminated dust. However, looking at each elements none of them exceeds the Qatari regulation. Moreover, gypsum saturation in the system was observed in both the lagoon and wells water samples. Lagoons and the water of the well are found to be of a saline type as well as Ca²⁺, Cl⁻, SO₄²⁻ type evidencing both gypsum dissolution and salinization in the system. Moreover, Maps produced by Inverse distance weighting showed an increasing level of Nitrate in the groundwater in winter, and decrease chloride and sulphate level, indicating recharge effect after winter rain events. While E. coli and faecal bacteria were found in most of the lagoons, biological analysis for wells needs to be conducted to understand the biological contamination from lagoon water infiltration. As a conclusion, while both the lagoon and the well showed the same results, more sampling is needed to understand the impact of the lagoons on the groundwater.

Keywords: groundwater quality, lagoon, treated wastewater, water management, wastewater treatment, wetlands

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Authors: Thanasit Suksutdhi

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This survey research aims to study the standard of service quality of food and beverage service staffs in hotel business by studying the service standard of three sample hotels, Siam Kempinski Hotel Bangkok, Four Seasons Resort Chiang Mai, and Banyan Tree Phuket. In order to find the international service standard of food and beverage service, triangular research, i.e. quantitative, qualitative, and survey were employed. In this research, questionnaires and in-depth interview were used for getting the information on the sequences and method of services. There were three parts of modified questionnaires to measure service quality and guest’s satisfaction including service facilities, attentiveness, responsibility, reliability, and circumspection. This study used sample random sampling to derive subjects with the return rate of the questionnaires was 70% or 280. Data were analyzed by SPSS to find arithmetic mean, SD, percentage, and comparison by t-test and One-way ANOVA. The results revealed that the service quality of the three hotels were in the international level which could create high satisfaction to the international customers. Recommendations for research implementations were to maintain the area of good service quality, and to improve some dimensions of service quality such as reliability. Training in service standard, product knowledge, and new technology for employees should be provided. Furthermore, in order to develop the service quality of the industry, training collaboration between hotel organization and educational institutions in food and beverage service should be considered.

Keywords: service standard, food and beverage department, sequence of service, service method

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Authors: Siwapech Silapaprayoon, Januluk Khanobdee, Sompid Samipak

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Chili peppers are the fruits of Capsicum pepper plants well known for their fiery burning sensation on the tongue after consumption. They are members of the Solanaceae or common nightshade family along with potato, tomato and eggplant. Thai cuisine has gained popularity for its distinct flavors due to usages of various spices and its heat from the addition of chili pepper. Though being used in little quantity for each dish, chili pepper holds a special place in Thai cuisine. There are many varieties of chili peppers in Thailand, and thirty accessions were collected at Rajamangala University of Technology Lanna, Lampang, Thailand. To effectively manage any germplasm it is essential to know the diversity and relationships among members. Thirty-six Inter Simple Sequence Repeat (ISSRs) DNA markers were used to analyze the germplasm. Total of 335 polymorphic bands was obtained giving the average of 9.3 alleles per marker. Unweighted pair-group mean arithmetic method (UPGMA) clustering of data using NTSYS-pc software indicated that the accessions showed varied levels of genetic similarity ranging from 0.57-1.00 similarity coefficient index indicating significant levels of variation. At SM coefficient of 0.81, the germplasm was separated into four groups. Phenotypic variation was discussed in context of phylogenetic tree clustering.

Keywords: diversity, germplasm, Chili pepper, ISSR

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Authors: H. Tavakoli, S. S. Mohtasebi, R. Alimardani, R. Gebbers

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Nitrogen (N) is one of the most important agricultural inputs affecting crop growth, yield and quality in rain-fed cereal production. N demand of crops varies spatially across fields due to spatial differences in soil conditions. In addition, the response of a crop to the fertilizer applications is heavily reliant on plant available water. Matching N supply to water availability is thus essential to achieve an optimal crop response. The objective of this study was to determine effect of drought stress on estimation of nitrogen status of winter wheat by some vegetation indices. During the 2012 growing season, a field experiment was conducted at the Bundessortenamt (German Plant Variety Office) Marquardt experimental station which is located in the village of Marquardt about 5 km northwest of Potsdam, Germany (52°27' N, 12°57' E). The experiment was designed as a randomized split block design with two replications. Treatments consisted of four N fertilization rates (0, 60, 120 and 240 kg N ha-1, in total) and two water regimes (irrigated (Irr) and non-irrigated (NIrr)) in total of 16 plots with dimension of 4.5 × 9.0 m. The indices were calculated using readings of a spectroradiometer made of tec5 components. The main parts were two “Zeiss MMS1 nir enh” diode-array sensors with a nominal rage of 300 to 1150 nm with less than 10 nm resolutions and an effective range of 400 to 1000 nm. The following vegetation indices were calculated: NDVI, GNDVI, SR, MSR, NDRE, RDVI, REIP, SAVI, OSAVI, MSAVI, and PRI. All the experiments were conducted during the growing season in different plant growth stages including: stem elongation (BBCH=32-41), booting stage (BBCH=43), inflorescence emergence, heading (BBCH=56-58), flowering (BBCH=65-69), and development of fruit (BBCH=71). According to the results obtained, among the indices, NDRE and REIP were less affected by drought stress and can provide reliable wheat nitrogen status information, regardless of water status of the plant. They also showed strong relations with nitrogen status of winter wheat.

Keywords: nitrogen status, drought stress, vegetation indices, precision agriculture

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Authors: N. Khater, I. Djbablia, A. Telaoumaten, S. A. Menina, H. Benbouza

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Thureferous Juniper is an endemic cupressacée constitutes a forest cover in the mountains of Aures (Algeria ). It is an heritage and important ecological richness, but continues to decline, highly endangered species in danger of extinction, these populations show significant originality due to climatic conditions of the environment, because of its strength and extraordinary vitality, made a powerful but fragile and unique ecosystem in which natural regeneration by seed is almost absent in Algeria. Because of the quality of seeds that are either dormant or affected at the tree and the ground level by a large number of pests and parasites, which will lead to the total disappearance of this species and consequently leading to the biodiversity. View the ecological and social- economic interest presented by this case, it deserves to be preserved and produced in large quantities in this respect. The present work aims to try to regenerate the Juniperus thurefera via vegetative propagation. We studied the potential of cuttings to form adventitious roots and buds. Cuttings were taken from young subjects from 5 to 20 years treated with indole butyric acid (AIB) and planted out inside perlite under atomizer whose temperature and light are controlled. The results show that the rate of rooting is important and encourages the regeneration of this species through vegetative propagation.

Keywords: juniperus thurefera, indole butyric acid, cutting, buds, rooting

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Authors: Charuvan Kasemsap

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This research was studied in Bangnampriao District, Chachernsao Province, Thailand. The primary data relating to flooding, drought, and saline intrusion problem on agriculture were collected by surveying, focus group, and in-depth interview with agricultural officers, technical officers of irrigation department, and local government leader of Bangnampriao District. The likelihood and consequence of risk were determined the risk index by risk assessment matrix. In addition, the risk index and the total coping capacity scores were investigated the vulnerability index by vulnerability matrix. It was found that the high-risk drought and saline intrusion was dramatically along Bang Pakong River owing to the end destination of Chao Phraya Irrigation system of Central Thailand. This leads yearly the damage of rice paddy, mango tree, orchard, and fish pond. Therefore, some agriculture avoids rice growing during January to May, and also pumps fresh water from a canal into individual storage pond. However, Bangnampriao District will be strongly affected by the impacts of climate change. Monthly precipitations are expected to decrease in number; dry seasons are expected to be more in number and longer in duration. Thus, the risk and vulnerability of agriculture are also increasing. Adaptation strategies need to be put in place in order to enhance the resilience of the agriculture.

Keywords: agriculture, bangnampriao, climate change, risk assessment

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Authors: Seongcheol Kwon, Jeongmin Kim, Kwang Ryel Ryu

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We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.

Keywords: situation-awareness, smart home, IoT, machine learning, classifier

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Authors: Ingrid Argote, John Archila, Marcelo Becker

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In agriculture, intelligent systems applications have generated great advances in automating some of the processes in the production chain. In order to improve the efficiency of those systems is proposed a vision system to estimate the amount of fruits in sweet orange trees. This work presents a system proposal using capture of thermal images and fuzzy logic. A bibliographical review has been done to analyze the state-of-the-art of the different systems used in fruit recognition, and also the different applications of thermography in agricultural systems. The algorithm developed for this project uses the metrics of the fuzzines parameter to the contrast improvement and segmentation of the image, for the counting algorith m was used the Hough transform. In order to validate the proposed algorithm was created a bank of images of sweet orange Citrus (L.) Osbeck acquired in the Maringá Farm. The tests with the algorithm Indicated that the variation of the tree branch temperature and the fruit is not very high, Which makes the process of image segmentation using this differentiates, This Increases the amount of false positives in the fruit counting algorithm. Recognition of fruits isolated with the proposed algorithm present an overall accuracy of 90.5 % and grouped fruits. The accuracy was 81.3 %. The experiments show the need for a more suitable hardware to have a better recognition of small temperature changes in the image.

Keywords: Agricultural systems, Citrus, Fuzzy logic, Thermal images.

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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There has been a need in recent years to predict student academic achievement prior to graduation. This is to assist them in improving their grades, especially for those who have struggled in the past. The purpose of this research is to use supervised learning techniques to create a model that predicts student academic progress. Many scholars have developed models that predict student academic achievement based on characteristics including smoking, demography, culture, social media, parent educational background, parent finances, and family background, to mention a few. This element, as well as the model used, could have misclassified the kids in terms of their academic achievement. As a prerequisite to predicting if the student will perform well in the future on related courses, this model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester. With a 96.7 percent accuracy, the model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost. This model is offered as a desktop application with user-friendly interfaces for forecasting student academic progress for both teachers and students. As a result, both students and professors are encouraged to use this technique to predict outcomes better.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Muna A. Eissawi, Safaa Abed Elfataah, Haytham Hago, Fatima E Abukunna, Ibtisam Amin Goreish, Nahid Gornas

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The study examined and analyzed the genetic diversity of DRB1locus of exon 2 of major histocompatibility complex of Sudanese desert sheep using PCR-RFLP and DNA sequencing. Five hundred samples belonging to five ecotypes of Desert Sudanese sheep (Abrag (Ab), Ashgar (Ash), Hamari (H), Kabashi (K) and Watish (W) were included. Amplification of exon 2 of the DRB1 gene yielded (300bp) amplified product in different ecotypes. Nine different digestion patterns corresponding to Five distinct alleles were observed with Rsa1 digestion. Genotype (ag) was the most common among all ecotypes, with a percentage comprised (40.4 %). The Hardy-Weinberg equilibrium (HWE) test showed that the studied ecotypes have significantly deviated from the theoretical proportions of Rsa1 patterns; probability values of the Chi-square test for HWE for MHC-DRB1 gene in SDS were 0.00 in all ecotypes. The constructed phylogenetic tree revealed the relation of 22 Sudanese isolates with each other and showed the shared sequences with 47 published foreign sequences randomly selected from different geographic regions. The results of this study highlight the effect of heterozygosity of MHC genes of the Desert sheep of Sudan which may clarify some of genetic back ground of their disease resistance and adaptation to environment.

Keywords: desert sheep, MHC, Ovar-DRB1, polymerase chain reaction (PCR), restriction fragment length polymorphism (RFLP)

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Authors: Ahsan Ali, Mayank Ostwal, Nikhil Agarwal

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Abstract—According to a World Bank report, millions of people across the globe still do not have access to improved water services. With uninterrupted growth of cities and urban inhabitants, there is a mounting need to safeguard the sustainable expansion of cities. Efficient functioning of the urban components and high living standards of the residents are needed to be ensured. The water and sanitation network of an urban development is one of its most essential parts of its critical infrastructure. The growth in urban population is leading towards increased water demand, and thus, the local water resources are severely strained. 'Smart water' is referred to water and waste water infrastructure that is able to manage the limited resources and the energy used to transport it. It enables the sustainable consumption of water resources through co-ordinate water management system, by integrating Information Communication Technology (ICT) solutions, intended at maximizing the socioeconomic benefits without compromising the environmental values. This paper presents a case study from a medium sized city in North-western Pakistan. Currently, water is getting contaminated due to the proximity between water and sewer pipelines in the study area, leading to public health issues. Due to unsafe grey water infiltration, the scarce ground water is also getting polluted. This research takes into account the design of smart urban water network by integrating ICT (Information and Communication Technology) with urban water network. The proximity between the existing water supply network and sewage network is analyzed and a design of new water supply system is proposed. Real time mapping of the existing urban utility networks will be projected with the help of GIS applications. The issue of grey water infiltration is addressed by providing sustainable solutions with the help of locally available materials, keeping in mind the economic condition of the area. To deal with the current growth of urban population, it is vital to develop new water resources. Hence, distinctive and cost effective procedures to harness rain water would be suggested as a part of the research study experiment.

Keywords: GIS, smart water, sustainability, urban water management

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Maria Toader, Gheorghe Valentin Roman

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The importance of fruit trees and bushes growing for Romania is due the concordance that exists between the different ecological conditions in natural basins, and the requirements of different species and varieties. There are, in Romania, natural areas dedicated to the main trees species: plum, apple, pear, cherry, sour cherry, finding optimal conditions for harnessing the potential of fruitfulness, making fruit quality both in terms of ratio commercial, and content in active principles. The share of fruits crops in the world economy of agricultural production is due primarily to the role of fruits in nourishment for human, and in the prevention and combating of diseases, in increasing the national income of cultivator countries and to improve comfort for human life. For Romania, the perspectives of the sector are positive, and are due to European funding opportunities, which provide farmers a specialized program that meets the needs of development and modernization of fruit growing industry, cultivation technology and equipment, organization and grouping of producers, creating storage facilities, conditioning, marketing and the joint use of fresh fruit. This paper shows the evolution of fruit growing, in Romania compared to other states. The document presents the current situation of the main tree species both in terms of surface but also of the productions and the role that this activity may have for the development of rural communities.

Keywords: fruit growing, fruits trees, productivity, rural development

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Authors: Alemayehu Muluneh, Saskia Keesstra, Leo Stroosnijder, Woldeamlak Bewket, Ashenafi Burka

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There are some scientific evidences and a belief by many that forests attract rain and deforestation contributes to a decline of rainfall. However, there is still a lack of concrete scientific evidence on the role of forests in rainfall amount. In this paper, we investigate the forest-rainfall relationships in the environmentally hot spot area of the Central Rift Valley (CRV) of Ethiopia. Specifically, we evaluate long term (1970-2009) rainfall variability and its relationship with historical forest cover and the relationship between existing forest cover and topographical variables and rainfall distribution. The study used 16 long term and 15 short term rainfall stations. The Mann-Kendall test, bi variate and multiple regression models were used. The results show forest and wood land cover continuously declined over the 40 years period (1970-2009), but annual rainfall in the rift valley floor increased by 6.42 mm/year. But, on the escarpment and highlands, annual rainfall decreased by 2.48 mm/year. The increase in annual rainfall in the rift valley floor is partly attributable to the increase in evaporation as a result of increasing temperatures from the 4 existing lakes in the rift valley floor. Though, annual rainfall is decreasing on the escarpment and highlands, there was no significant correlation between this rainfall decrease and forest and wood land decline and also rainfall variability in the region was not explained by forest cover. Hence, the decrease in annual rainfall on the escarpment and highlands is likely related to the global warming of the atmosphere and the surface waters of the Indian Ocean. Spatial variability of number of rainy days from systematically observed two-year’s rainfall data (2012-2013) was significantly (R2=-0.63) explained by forest cover (distance from forest). But, forest cover was not a significant variable (R2=-0.40) in explaining annual rainfall amount. Generally, past deforestation and existing forest cover showed very little effect on long term and short term rainfall distribution, but a significant effect on number of rainy days in the CRV of Ethiopia.

Keywords: elevation, forest cover, rainfall, slope

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Authors: Frantisek Kopriva, Kamila Michalkova, Radim Dudek, Jana Volejnikova

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Rationale: Diagnostic criteria of asthma–COPD overlap syndrome (ACOS) are controversial in pediatrics. Emphysema is characteristic of COPD and usually does not occur in typical asthma; its presence in patients with asthma suggests the concurrence with COPD. Low-dose chest computed tomography (CT) allows a non-invasive assessment of the lung tissue structure. Here we present CT findings of emphysematous changes in a child with ACOS. Patient and Methods: In a 6-year-old boy, atopy was confirmed by a skin prick test using common allergen extracts (grass and tree pollen, house dust mite, molds, cat, dog; manufacturer Stallergenes Greer, London, UK), where reactions over 3 mm were considered positive. Treatment with corticosteroids was started during the course of severe asthma. At 12 years of age, his spirometric parameters deteriorated despite treatment adjustment (VC 1.76 L=85%, FEV1 1.13 L=67%, TI%VCmax 64%, MEF25 19%, TLC 144%) and the bronchodilator test became negative. Results: Low-dose chest CT displayed irregular regions with increased radiolucency of pulmonary parenchyma (typical for hyperinflation in emphysematous changes) in both lungs. This was in accordance with the results of spirometric examination. Conclusions: ACOS is infrequent in children. However, low-dose chest CT scan can be considered to confirm this diagnosis or eliminate other diagnoses when the clinical condition is deteriorating and treatment response is poor.

Keywords: child, asthma, low-dose chest CT, ACOS

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Authors: Sally Shepherd, Jessica Wong, David Read

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Traumatic brachiocephalic artery aneurysm is a rare injury that typically occurs as a result of a blunt chest injury. A 19-year-old female sustained a head-on, high speed motor vehicle crash into a tree. Upon release after 45 minutes of entrapment, she was tachycardic but normotensive, with a significant seatbelt sign across her chest and open deformed right thigh with weak pulses in bilateral lower limbs. A chest XR showed mild upper mediastinal widening. A CT trauma series plus gated CT chest revealed a grade 3a aortic arch transection with brachiocephalic pseudoaneurysm. Endovascular repair of the brachiocephalic artery was attempted post-presentation but was unsuccessful as the first stent migrated to the infrarenal abdominal aorta and the second stent across the brachiocephalic artery origin had a persistent leak at the base. She was transferred to Intensive Care for strict blood pressure control. She returned to theatre 5 hours later for a median sternotomy, aortic arch repair with an 8mm graft extraction, and excision of the innominate artery pseudoaneurysm. She had an uncomplicated post-operative recovery. This case highlights that brachiocephalic artery injury is a rare but potentially lethal injury as a result of blunt chest trauma. Safe management requires a combined Vascular and Cardiothoracic team approach, as stenting alone may be insufficient.

Keywords: blunt chest injury, Brachiocephalic aneurysm, innominate artery, trauma

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Authors: Dewi Juliah Ratnaningsih, Imas Sukaesih Sitanggang

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Classification is one of data mining techniques that aims to discover a model from training data that distinguishes records into the appropriate category or class. Data mining classification methods can be applied in education, for example, to determine the classification of non-active students in Indonesia Open University. This paper presents a comparison of three methods of classification: Naïve Bayes, Bagging, and C.45. The criteria used to evaluate the performance of three methods of classification are stratified cross-validation, confusion matrix, the value of the area under the ROC Curve (AUC), Recall, Precision, and F-measure. The data used for this paper are from the non-active Indonesia Open University students in registration period of 2004.1 to 2012.2. Target analysis requires that non-active students were divided into 3 groups: C1, C2, and C3. Data analyzed are as many as 4173 students. Results of the study show: (1) Bagging method gave a high degree of classification accuracy than Naïve Bayes and C.45, (2) the Bagging classification accuracy rate is 82.99 %, while the Naïve Bayes and C.45 are 80.04 % and 82.74 % respectively, (3) the result of Bagging classification tree method has a large number of nodes, so it is quite difficult in decision making, (4) classification of non-active Indonesia Open University student characteristics uses algorithms C.45, (5) based on the algorithm C.45, there are 5 interesting rules which can describe the characteristics of non-active Indonesia Open University students.

Keywords: comparative analysis, data mining, clasiffication, Bagging, Naïve Bayes, C.45, non-active students, Indonesia Open University

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Authors: Carmel Khrisna Wong Moreno, Dinah M. Espina

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The increasing price of feed ingredients has prompted farmers to seek feasible feed alternatives like the utilization of locally-grown protein-rich feedstuff which is cheaper but gives a positive result in poultry production. Trichanthera gigantea, a fodder tree which is an excellent alternative as feed ingredient in the Philippines has now gained popularity as feed supplement. This study was conducted to determine the growth and laying performance of commercial hens fed with varying levels of Trichanthera gigantea leaf meal. The incorporation of Trichanthera gigantea leaf meal at 5%, 10%, and 15% into the diet of commercial hens did not affect the growth and laying performance. Results of the study revealed that the weight gain of the birds fed with Trichanthera gigantea supplemented diets was not significantly different with the control (100% commercial layer mash). The voluntary feed intake, feed conversion ratio, weekly average egg weight and egg production of the commercial hens fed with T. gigantea leaf meal supplemented diets were not significantly different from the control. Results of the study showed that the supplementation of Trichanthera gigantea leaf meal of up to 15% into the diets of commercial hens is highly acceptable since it does not affect the growth and laying performance of the birds. In addition, it would mean a 15% savings in production cost from commercial feeds.

Keywords: egg production, growth, laying performance, trichanthera gigantea (nees)

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Authors: A. B. Alarape, O. D. Aba

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The use of legume tree pruning as mulch in agroforestry system is a common practice to maintain soil organic matter and improve soil fertility in the tropics. The study was conducted to determine the influence of agroforestry trees leafy biomass and nitrogen fertilizer on crop growth rate and relative growth rate of maize. The experiments were laid out as 3 x 4 x 2 factorial in a split-split plot design with three replicates. Control, biomass species (Parkia biglobosa and Albizia lebbeck) as main plots were considered, rates of nitrogen considered include (0, 40, 80, 120 kg N ha⁻¹) as sub-plots, and maize varieties (DMR-ESR-7 and 2009 EVAT) were used as sub-sub plots. Data were analyzed using descriptive and inferential statistics (ANOVA) at α = 0.05. Incorporation of leafy biomass was significant in 2015 on Relative Growth Rate (RGR), while nitrogen application was significant on Crop Growth Rate (CGR). 2009 EVAT had higher CGR in 2015 at 4-6 and 6-8 WAP. Incorporation of Albizia leaves enhanced the growth of maize than Parkia leaves. Farmers are, therefore, encouraged to use Albizia leaves as mulch to enrich their soil for maize production and most especially, in case of availability of inorganic fertilizers. Though, production of maize with biomass and application of 120 kg N ha⁻¹ will bring better growth of maize.

Keywords: agroforestry trees, fertilizer, growth, incorporation, leafy biomass

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Authors: Jitendra Aswani

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In the first section of this study, impact of Global Financial Crisis (GFC) on the synchronization of fourteen Asian Stock Markets (ASM’s) of countries like Hong Kong, India, Thailand, Singapore, Taiwan, Pakistan, Bangladesh, South Korea, Malaysia, Indonesia, Japan, China, Philippines and Sri Lanka, has been analysed using the network science and its metrics like degree of node, clustering coefficient and network density. Then in the second section of this study by introducing the US stock market in existing network and developing a Minimum Spanning Tree (MST) spread of crisis from the US stock market to Asian Stock Markets (ASM) has been explained. Data used for this study is adjusted the closing price of these indices from 6th January, 2000 to 15th September, 2013 which further divided into three sub-periods: Pre, during and post-crisis. Using network analysis, it is found that Asian stock markets become more interdependent during the crisis than pre and post crisis, and also Hong Kong, India, South Korea and Japan are systemic important stock markets in the Asian region. Therefore, failure or shock to any of these systemic important stock markets can cause contagion to another stock market of this region. This study is useful for global investors’ in portfolio management especially during the crisis period and also for policy makers in formulating the financial regulation norms by knowing the connections between the stock markets and how the system of these stock markets changes in crisis period and after that.

Keywords: global financial crisis, Asian stock markets, network science, Kruskal algorithm

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Authors: Rodolfo Lorbieski, Silvia Modesto Nassar

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Many supervised machine learning tasks require decision making across numerous different classes. Multi-class classification has several applications, such as face recognition, text recognition and medical diagnostics. The objective of this article is to analyze an adapted method of Stacking in multi-class problems, which combines ensembles within the ensemble itself. For this purpose, a training similar to Stacking was used, but with three levels, where the final decision-maker (level 2) performs its training by combining outputs from the tree-based pair of meta-classifiers (level 1) from Bayesian families. These are in turn trained by pairs of base classifiers (level 0) of the same family. This strategy seeks to promote diversity among the ensembles forming the meta-classifier level 2. Three performance measures were used: (1) accuracy, (2) area under the ROC curve, and (3) time for three factors: (a) datasets, (b) experiments and (c) levels. To compare the factors, ANOVA three-way test was executed for each performance measure, considering 5 datasets by 25 experiments by 3 levels. A triple interaction between factors was observed only in time. The accuracy and area under the ROC curve presented similar results, showing a double interaction between level and experiment, as well as for the dataset factor. It was concluded that level 2 had an average performance above the other levels and that the proposed method is especially efficient for multi-class problems when compared to binary problems.

Keywords: stacking, multi-layers, ensemble, multi-class

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Authors: Akinwale Adeyinka

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Critical natural resources such as clean ground water, fertile topsoil, and biodiversity are diminishing at an exponential rate, orders of magnitude above that at which they can be regenerated. Based on news on world population record, over 6 billion people on earth, and almost a quarter million added each day, the scale of human activity and environmental impact is unprecedented. Soaring human population growth over the past century has created a visible challenge to earth’s life support systems. In addition, the world faces an onslaught of other environmental threats including degenerated global climate change, global warming, intensified acid rain, stratospheric ozone depletion and health threatening pollution. Overpopulation and the use of deleterious technologies combine to increase the scale of human activities to a level that underlies these entire problems. These intensifying trends cannot continue indefinitely, hopefully, through increased understanding and valuation of ecosystems and their services, earth’s basic life-support system will be protected for the future.To say the fact, human civilization is now the dominant cause of change in the global environment. Now that our relationship to the earth has change so utterly, we have to see that change and understand its implication. These are actually 2 aspects to the challenges which we should believe. The first is to realize that our power to harm the earth can indeed have global and even permanent effects. Second is to realize that the only way to understand our new role as a co-architect of nature is to see ourselves as part of a complex system that does operate according to the same simple rules of cause and effect we are used to. So understanding the physical/biological dimension of earth system is an important precondition for making sensible policy to protect our environment. Because we believe Sustainable Development Is a matter of reconciling respect for the environment, social equity and economic profitability. Also, we strongly believe that environmental protection is naturally about reducing air and water pollution, but it also includes the improvement of the environmental performance of existing process. That is why we should always have it at the heart of our business that the environmental problem is not our effect on the environment so much as our relationship with the environment. We should always think of being environmental friendly in our operation.

Keywords: Stratospheric ozone depletion ion , Climate Change, global warming, social equity and economic profitability

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

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Authors: Aree Thongpukdee, Chockpisit Thepsithar, Chuthalak Thammaso

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Use of plants grown in local area for edible has a long tradition in different culture. The indigenous knowledge such as usage of plants as vegetables by local people is risk to disappear when no records are done. In order to conserve and transfer this valuable heritage to the new generation, ethnobotanical study should be investigated and documented. The survey of vegetable plants traditionally used was carried out in the year 2012. Information was accumulated via questionnaires and oral interviewing from 100 people living in 36 villages of 9 districts in Amphoe Huai Mek, Kalasin, Thailand. Local plant names, utilized parts and preparation methods of the plants were recorded. Each mentioned plant species were collected and voucher specimens were prepared. A total of 55 vegetable plant species belonging to 34 families and 54 genera were identified. The plant habits were tree, shrub, herb, climber, and shrubby fern at 21.82%, 18.18%, 38.18%, 20.00% and 1.82% respectively. The most encountered vegetable plant families were Leguminosae (20%), Cucurbitaceae (7.27%), Apiaceae (5.45%), whereas families with 3.64% uses were Araceae, Bignoniaceae, Lamiaceae, Passifloraceae, Piperaceae and Solanaceae. The most common consumptions were fresh or brief boiled young shoot or young leaf as side dishes of ‘jaeo, laab, namprik, pon’ or curries. Most locally known vegetables included 45% of the studied plants which grow along road side, backyard garden, hedgerow, open forest and rice field.

Keywords: vegetable plants, ethnobotanical survey, Kalasin, Thailand

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Authors: Ahmad Shahin, Walid Moudani, Ali Bekraki

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This research aims at presenting a predictive data mining model to handle an accurate diagnosis of acute appendicitis with patients for the purpose of maximizing the health service quality, minimizing morbidity/mortality, and reducing cost. However, acute appendicitis is the most common disease which requires timely accurate diagnosis and needs surgical intervention. Although the treatment of acute appendicitis is simple and straightforward, its diagnosis is still difficult because no single sign, symptom, laboratory or image examination accurately confirms the diagnosis of acute appendicitis in all cases. This contributes in increasing morbidity and negative appendectomy. In this study, the authors propose to generate an accurate model in prediction of patients with acute appendicitis which is based, firstly, on the segmentation technique associated to ABC algorithm to segment the patients; secondly, on applying fuzzy logic to process the massive volume of heterogeneous and noisy data (age, sex, fever, white blood cell, neutrophilia, CRP, urine, ultrasound, CT, appendectomy, etc.) in order to express knowledge and analyze the relationships among data in a comprehensive manner; and thirdly, on applying dynamic programming technique to reduce the number of data attributes. The proposed model is evaluated based on a set of benchmark techniques and even on a set of benchmark classification problems of osteoporosis, diabetes and heart obtained from the UCI data and other data sources.

Keywords: healthcare management, acute appendicitis, data mining, classification, decision tree

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Authors: M. Eickermann, F. Ronellenfitsch, J. Junk

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Oilseed rape (Brassica napus L.) is one of the most important crops throughout Europe, but pressure due to pest insects and pathogens can reduce yield amount substantially. Therefore, the usage of pesticide applications is outstanding in this crop. In addition, climate change effects can interact with phenology of the host plant and their pests and can apply additional pressure on the yield. Next to the pollen beetle, Meligethes aeneus L., the seed-damaging pest insects, cabbage seed weevil (Ceutorhynchus obstrictus Marsham) and the brassica pod midge (Dasineura brassicae Winn.) are of main economic impact to the yield. While females of C. obstrictus are infesting oilseed rape by depositing single eggs into young pods, the females of D. brassicae are using this local damage in the pod for their own oviposition, while depositing batches of 20-30 eggs. Without a former infestation by the cabbage seed weevil, a significant yield reduction by the brassica pod midge can be denied. Based on long-term, multisided field experiments, a comprehensive data-set on pest migration to crops of B. napus has been built up in the last ten years. Five observational test sides, situated in different climatic regions in Luxembourg were controlled between February until the end of May twice a week. Pest migration was recorded by using yellow water pan-traps. Caught insects were identified in the laboratory according to species specific identification keys. By a combination of pest observations and corresponding meteorological observations, the set-up of models to predict the migration periods of the seed-damaging pests was possible. This approach is the basis for a computer-based decision support tool, to assist the farmer in identifying the appropriate time point of pesticide application. In addition, the derived algorithms of that decision support tool can be combined with climate change projections in order to assess the future potential threat caused by the seed-damaging pest species. Regional climate change effects for Luxembourg have been intensively studied in recent years. Significant changes to wetter winters and drier summers, as well as a prolongation of the vegetation period mainly caused by higher spring temperature, have also been reported. We used the COSMO-CLM model to perform a time slice experiment for Luxembourg with a spatial resolution of 1.3 km. Three ten year time slices were calculated: The reference time span (1991-2000), the near (2041-2050) and the far future (2091-2100). Our results projected a significant shift of pest migration to an earlier onset of the year. In addition, a prolongation of the possible migration period could be observed. Because D. brassiace is depending on the former oviposition activity by C. obstrictus to infest its host plant successfully, the future dependencies of both pest species will be assessed. Based on this approach the future risk potential of both seed-damaging pests is calculated and the status as pest species is characterized.

Keywords: CORDEX projections, decision support tool, Brassica napus, pests

Procedia PDF Downloads 367