Search results for: agriculture yield prediction

Authors: Ahmed Kayad Abdourazak, Abderafi Souad, Zejli Driss, Idriss Abdoulkader Ibrahim

Abstract:

In this paper Artificial Neural Network (ANN) is used to predict the solar irradiation in Djibouti for the first Time that is useful to the integration of Concentrating Solar Power (CSP) and sites selections for new or future solar plants as part of solar energy development. An ANN algorithm was developed to establish a forward/reverse correspondence between the latitude, longitude, altitude and monthly solar irradiation. For this purpose the German Aerospace Centre (DLR) data of eight Djibouti sites were used as training and testing in a standard three layers network with the back propagation algorithm of Lavenber-Marquardt. Results have shown a very good agreement for the solar irradiation prediction in Djibouti and proves that the proposed approach can be well used as an efficient tool for prediction of solar irradiation by providing so helpful information concerning sites selection, design and planning of solar plants.

Keywords: artificial neural network, solar irradiation, concentrated solar power, Lavenberg-Marquardt

Procedia PDF Downloads 352

Authors: Paria Soleimani, Arezoo Neshati

Abstract:

Myocardial infarction is one of the leading causes of ‎death in the world. Some of these deaths occur even before the patient ‎reaches the hospital. Myocardial infarction occurs as a result of ‎impaired blood supply. Because the most of these deaths are due to ‎coronary artery disease, hence the awareness of the warning signs of a ‎heart attack is essential. Some heart attacks are sudden and intense, but ‎most of them start slowly, with mild pain or discomfort, then early ‎detection and successful treatment of these symptoms is vital to save ‎them. Therefore, importance and usefulness of a system designing to ‎assist physicians in the early diagnosis of the acute heart attacks is ‎obvious.‎ The purpose of this study is to determine how well a predictive ‎model would perform based on the only patient-reportable clinical ‎history factors, without using diagnostic tests or physical exams. This ‎type of the prediction model might have application outside of the ‎hospital setting to give accurate advice to patients to influence them to ‎seek care in appropriate situations. For this purpose, the data were ‎collected on 711 heart patients in Iran hospitals. 28 attributes of clinical ‎factors can be reported by patients; were studied. Three logistic ‎regression models were made on the basis of the 28 features to predict ‎the risk of heart attacks. The best logistic regression model in terms of ‎performance had a C-index of 0.955 and with an accuracy of 94.9%. ‎The variables, severe chest pain, back pain, cold sweats, shortness of ‎breath, nausea, and vomiting were selected as the main features.‎

Keywords: Coronary heart disease, Acute heart attacks, Prediction, Logistic ‎regression‎

Procedia PDF Downloads 447

Authors: Maria Valeria De Bonis, Gianpaolo Ruocco

Abstract:

Nowadays, computational modeling is paving new design and verification ways in a number of industrial sectors. The technology is ripe to challenge some case in the Bioengineering and Medicine frameworks: for example, looking at the strategical and ethical importance of oncology research, efforts should be made to yield new and powerful resources to tumor knowledge and understanding. With these driving motivations, we approach this gigantic problem by using some standard engineering tools such as the mathematics behind the biomass transfer. We present here some bacterial colonization studies in complex structures. As strong analogies hold with some tumor proliferation, we extend our study to a benchmark case of solid tumor. By means of a commercial software, we model biomass and energy evolution in arbitrary media. The approach will be useful to cast virtualization cases of cancer growth in human organs, while augmented reality tools will be used to yield for a realistic aid to informed decision in treatment and surgery.

Keywords: bacteria, simulation, tumor, precision medicine

Procedia PDF Downloads 333

Authors: Emon Chairote, Panatda Jannoey, Griangsak Chairote

Abstract:

A strain of Monascus purpureus CMU001 was used to prepared red yeast rice from Thai glutinous rice Korkor 6 (RD 6). Adding of different amounts of histidine (156, 312, 625, and 1250 mg in 100 g of rice grains)) under aerobic and air limitation (air-lock) condition were used in solid fermentation. Determination of the yield as well as monacolin K content was done. Citrinin content was also determined in order to confirm the safety use of prepared red yeast rice. It was found that under air-lock condition with 1250 mg of histidine addition gave the highest yield of 37.40 g of dried red yeast rice prepared from 100 g of rice. Highest 5.72 mg content of monacolin K was obtained under air-lock condition with 312 mg histidine addition. In the other hand, citrinin content was found to be less than 24462 ng/g of all dried red yeast rice samples under the experimental methods used in this work.

Keywords: red yeast rice, Thai glutinous rice, monacolin K., citrinin

Procedia PDF Downloads 244

Authors: Piotr A. Kowalski, Kasper Sapala, Wiktor Warchalowski

Abstract:

PM10 is a suspended dust that primarily has a negative effect on the respiratory system. PM10 is responsible for attacks of coughing and wheezing, asthma or acute, violent bronchitis. Indirectly, PM10 also negatively affects the rest of the body, including increasing the risk of heart attack and stroke. Unfortunately, Poland is a country that cannot boast of good air quality, in particular, due to large PM concentration levels. Therefore, based on the dense network of Airly sensors, it was decided to deal with the problem of prediction of suspended particulate matter concentration. Due to the very complicated nature of this issue, the Machine Learning approach was used. For this purpose, Convolution Neural Network (CNN) neural networks have been adopted, these currently being the leading information processing methods in the field of computational intelligence. The aim of this research is to show the influence of particular CNN network parameters on the quality of the obtained forecast. The forecast itself is made on the basis of parameters measured by Airly sensors and is carried out for the subsequent day, hour after hour. The evaluation of learning process for the investigated models was mostly based upon the mean square error criterion; however, during the model validation, a number of other methods of quantitative evaluation were taken into account. The presented model of pollution prediction has been verified by way of real weather and air pollution data taken from the Airly sensor network. The dense and distributed network of Airly measurement devices enables access to current and archival data on air pollution, temperature, suspended particulate matter PM1.0, PM2.5, and PM10, CAQI levels, as well as atmospheric pressure and air humidity. In this investigation, PM2.5, and PM10, temperature and wind information, as well as external forecasts of temperature and wind for next 24h served as inputted data. Due to the specificity of the CNN type network, this data is transformed into tensors and then processed. This network consists of an input layer, an output layer, and many hidden layers. In the hidden layers, convolutional and pooling operations are performed. The output of this system is a vector containing 24 elements that contain prediction of PM10 concentration for the upcoming 24 hour period. Over 1000 models based on CNN methodology were tested during the study. During the research, several were selected out that give the best results, and then a comparison was made with the other models based on linear regression. The numerical tests carried out fully confirmed the positive properties of the presented method. These were carried out using real ‘big’ data. Models based on the CNN technique allow prediction of PM10 dust concentration with a much smaller mean square error than currently used methods based on linear regression. What's more, the use of neural networks increased Pearson's correlation coefficient (R²) by about 5 percent compared to the linear model. During the simulation, the R² coefficient was 0.92, 0.76, 0.75, 0.73, and 0.73 for 1st, 6th, 12th, 18th, and 24th hour of prediction respectively.

Keywords: air pollution prediction (forecasting), machine learning, regression task, convolution neural networks

Procedia PDF Downloads 148

Authors: Amadou Wurry Jallow, Adama N. S. Bah, Karamo Bah, Shih-Ye Wang, Kuo-Chung Chu, Chien-Yeh Hsu

Abstract:

Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predicting and preventing complications of chronic kidney disease (CKD). This study aimed to develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and effective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportional hazard regression analyses were performed to determine the variables with high prognostic values for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laboratory, laboratory, and metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, body mass index (BMI), and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have demonstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The machine learning models are simple to use and flexible because they work even with incomplete data and can be applied in any clinical setting, including settings where laboratory data is difficult to obtain.

Keywords: chronic kidney disease, glomerular filtration rate, creatinine, novel metabolic indices, machine learning, risk prediction

Procedia PDF Downloads 105

Authors: Sandija Zeverte-Rivza, Irina Pilvere, Baiba Rivza

Abstract:

The use of renewable energy sources incl. biogas has become topical in accordance with the increasing demand for energy, decrease of fossil energy resources and the efforts to reduce greenhouse gas emissions as well as to increase energy independence from the territories where fossil energy resources are available. As the technologies of biogas production from agricultural biomass develop, risk assessment and risk management become necessary for farms producing such a renewable energy. The need for risk assessments has become particularly topical when discussions on changing the biogas policy in the EU take place, which may influence the development of the sector in the future, as well as the operation of existing biogas facilities and their income level. The current article describes results of the risk assessment for farms producing biomass from agriculture biomass in Latvia, the risk assessment system included 24 risks, that affect the whole biogas production process and the obtained results showed the high significance of political and production risks.

Keywords: biogas production, risks, risk assessment, biosystems engineering

Procedia PDF Downloads 414

Authors: Abdulla D. Alblooshi

Abstract:

The stock market is a representation of human behavior and psychology, such as fear, greed, and discipline. Those are manifested in the form of price movements during the trading sessions. Therefore, predicting the stock movement and prices is a challenging effort. However, those trading sessions produce a large amount of data that can be utilized to train an AI agent for the purpose of predicting the stock movement. Predicting the stock market price action will be advantageous. In this paper, the stock movement data of three DFM listed stocks are studied using historical price movements and technical indicators value and used to train an agent using KNN and SVM methods to predict the future price movement. MATLAB Toolbox and a simple script is written to process and classify the information and output the prediction. It will also compare the different learning methods and parameters s using metrics like RMSE, MAE, and R².

Keywords: KNN, ANN, style, SVM, stocks, technical indicators, RSI, MACD, moving averages, RMSE, MAE

Procedia PDF Downloads 168

Authors: Leandro Moura da Silva

Abstract:

The international community has strived to create legal mechanisms to protect their biodiversity, but this can represent, sometimes, particularly in the case of regulatory regime on access to genetic resources, an excessive nationalism which transforms itself into a significant obstacle to scientific progress causing damages to the country and to local farmers. Although it has been poorly publicized in the media, the international legal system was marked, in 2014, by the entry into force of the Nagoya Protocol, which regulates the access and benefit sharing of genetic resources of the States Party to that legal instrument. However, it’s not reasonable to think of regulating access to genetic resources without reflecting on the links of this important subject with other related issues, such as family farming and agribusiness, food safety, food security, intellectual property rights (on seeds, genetic material, new plant varieties, etc.), environmental sustainability, biodiversity, and biosafety.

Keywords: international law, regulation on agriculture, agronomy techniques, sustainability, genetic resources and new crop varieties, CBD, Nagoya Protocol, ITPGRFA

Procedia PDF Downloads 499

Authors: Shatha A. Yousif, Qasim M. Zamil, Hasan Y. Al Muhi, Jamal A. Al Shammari

Abstract:

Potato (Solanum tuberosum L.) is one of the major vegetable crops that are grown world wide because of its economic importance. This experiment investigated the effect of local sands (River Base, Al-Ekader and Karbala) on number and total weight of mini tubers. Statistical analysis revealed that there were no significant differences among sand cultures in number of stem/plant, chlorophyll index and tubers dry weight. River Base sand had the highest plant height (74.9 cm), leaf number/plant number (39.3), leaf area (84.4 dcm2⁄plant), dry weight/plant (26.31), tubers number/plant (8.5), tubers weight/plant (635.53 gm) and potato tuber yields/trove (28.60 kg), whereas the Karbala sand had lower performance. All the characters had positive and significant correlation with yields except the traits number of stem and tuber dry weight.

Keywords: correlation, potato, sand culture, yield

Procedia PDF Downloads 474

Authors: Yilmaz Bayhan, Emin Güzel, Ömer Barış Özlüoymak, Ahmet İnce, Abdullah Sessiz

Abstract:

In this study, determining of reduced tillage techniques in green bean farming as a second crop after harvesting wheat was targeted. To this aim, four different soil tillage methods namely, heavy-duty disc harrow (HD), rotary tiller (ROT), heavy-duty disc harrow plus rotary tiller (HD+ROT) and no-tillage (NT) (seeding by direct drill) were examined. Experiments were arranged in a randomized block design with three replications. The highest green beans yields were obtained in HD+ROT and NT as 5,862.1 and 5,829.3 Mg/ha, respectively. The lowest green bean yield was found in HD as 3,076.7 Mg/ha. The highest fuel consumption was measured 30.60 L ha^-1 for HD+ROT whereas the lowest value was found 7.50 L ha^-1 for NT. No tillage method gave the best results for fuel consumption and effective power requirement. It is concluded that no-tillage method can be used in second crop green bean in the Thrace Region due to economic and erosion conditions.

Keywords: green bean, soil tillage, yield, vegetative

Procedia PDF Downloads 372

Authors: Sisuwan Kaseamsawat, Sivapan Choo-In

Abstract:

This study aimed to evaluated the surface water quality for agriculture and consumption in the district. Surface water quality parameters in this study in cluding water temperature, turbidity, conductivity. salinity, pH, dissolved oxygen, BOD, nitrate, Suspended solids, phosphorus. Total dissolve solids, iron, copper, zinc, manganese, lead and cadmium. Water samples were collected from small excavation, Lychee, Pomelo, and Coconut orchard for 3 season during January to December 2011. The surface water quality from small excavation, Lychee, pomelo, and coconut orchard are meet the type III of surface water quality standard issued by the National Environmental Quality Act B. E. 1992. except the concentration of heavy metal. And did not differ significantly at 0.05 level, except dissolved oxygen. The water is suitable for consumption by the usual sterile and generally improving water quality through the process before. And is suitable for agriculture.

Keywords: water quality, surface water quality, Thailand, water

Procedia PDF Downloads 355

Authors: S. Rida, O. Saadani Hassani, Q. R’zina, N. Saadaoui, K. Fares

Abstract:

Wastewater treatment stations create large quantities of sludge, whose treatment is poorly underestimated in the draft installation. However, chemical analysis of sludge reveals their important concentration in fertilizer elements including nitrogen and phosphorus. The direct application of sludge can reveal contamination of the food chain because of their chemical and organic micropollutants load. Therefore, there is a need of treatment process before use. The treatment by composting of this sludge mixed with three different proportions of sugar beet lime sludge (0%, 20%,30%) and green waste permits to obtain a stable compost rich in mineral elements, having a pleasant smell and relatively hygienic. In addition, the use of compost in agriculture positively affects the plant-soil system. Thus, this study shows that the supply of compost improves the physical properties of the soil and its agronomic quality, which results in an increase in the biomass of cultivated radish plants and a larger crop.

Keywords: agriculture, composting, soil, sugar beet lime, wastewater

Procedia PDF Downloads 322

Authors: Sunny Arora, Meghal A. Desai

Abstract:

An impetus towards green consumerism and implementation of sustainable techniques, consumption of natural products and utilization of environment friendly techniques have gained accelerated acceptance. Butein, a natural colorant, has many medicinal properties apart from its use in dyeing industries. Extraction of butein from the flowers of flame of forest was carried out using ultrasonication bath. Solid loading (2-6 g), extraction time (30-50 min), volume of solvent (30-50 mL) and types of solvent (methanol, ethanol and water) have been studied to maximize the yield of butein using the Taguchi method. The highest yield of butein 4.67% (w/w) was obtained using 4 g of plant material, 40 min of extraction time and 30 mL volume of methanol as a solvent. The present method provided a greater reduction in extraction time compared to the conventional method of extraction. Hence, the outcome of the present investigation could further be utilized to develop the method at a higher scale.

Keywords: butein, flowers of Flame of the Forest, Taguchi method, ultrasonic bath

Procedia PDF Downloads 474

Authors: Ewa Krasnodębska

Abstract:

The article presents the costs and income maize cultivation for grain four selected varieties with different numbers of FAO in 2013-2014. Results of the experiments are derived from a field experiment conducted in indywidulnym farm specializing in the production plant located in the eastern part of Mazowieckie voivodship. The experiment examined the profitability of four varieties of maize cultivation: medium early: P8400 (FAO 240) and P8589 (FAO 250), and an average of late: PR38N86 (FAO 270) and P9027 (FAO 260). In order to evaluate the profitability of grain maize production was calculated income from 1 ha of crops in zł and profitability index taking into account the direct payments up to 1 ha. Analyzing the value of crop production can be concluded that the value of the total production of each variety was very much varied and very much depend on the sales price and yield of maize obtained from 1 ha of cultivation. The largest average seed yield of two years at a moisture content of 15% was achieved in a variety PR38N86, which amounted to 12.1 t / ha and the lowest in the variety P8400 - 9.8 t / ha. Income from 1 ha of crops including EU subsidies ranged from 4916.4 zł / ha in 2013 for variety and only 528.7 PR38N86 zł / ha for a variety of P8400 in 2014. Profitability index reached the highest average late PR38N86 variety of FAO 290 over the entire two-year period under study, and the lowest rate of profitability achieved P8400 medium early variety of FAO 240. The profitability of production ranged from 8964.0 zł / ha in 2013 for a variety of PR38N86 to 5616.0 zł / ha for a variety of P8400 in 2014. Cultivation of maize for grain production is attractive and does not require large amounts of work, but its economic rationale is based primarily on the resulting yield and the price of buying.

Keywords: corn, grain, income, profitability

Procedia PDF Downloads 389

Authors: Jossitt Williams Vargas Cruz, Aura Jazmín Pérez Ríos

Abstract:

The variations of solar dynamics have become a relevant topic of study due to the effects of climate changes generated on the earth. One of the most disconcerting aspects is the variability that the sun has on the climate is the role played by sunspots (extra-atmospheric variable) in the modulation of the Cosmic Rays CR (extra-atmospheric variable). CRs influence the earth's climate by affecting cloud formation (atmospheric variable), and solar cycle influence is associated with the presence of solar storms, and the magnetic activity is greater, resulting in less CR entering the earth's atmosphere. The different methods of climate prediction in Colombia do not take into account the extra-atmospheric variables. Therefore, correlations between atmospheric and extra-atmospheric variables were studied in order to implement a Python code based on neural networks to make the prediction of the extra-atmospheric variable with the highest correlation.

Keywords: correlations, cosmic rays, sun, sunspots and variations.

Procedia PDF Downloads 70

Authors: Ercan Ceyhan, Mehmet Ali Avci

Abstract:

This research was made during the 2015 growing periods in the trial filed of ‘Research Station for Department of Field Crops, Agricultural Faculty, Selcuk University’ according to ‘Randomized Blocks Design’ with 3 replications. Research material was the following pea lines; PS16, PS18, PS21, PS23, PS24, PS25, PS36, PS47, PS49, PS51, PS54, PS58, PS67, PS69, PS71, PS73, PS83, PS84, PS87 and PSKY and three cultivars and other 2 commercial varieties named as Bolero, Rondo and Ultrello. Some agronomical characteristics such as plant height (cm) number of pod per plant number of seed per pod number of seed per plant 100 seed weight (g) and seed yield (kg ha-1) were determined. Results of the research implicated that the new developed lines were superior compared with the control (commercial) varieties by means of most of the characteristics. Nevertheless, similar researches should be continued in different locations and years.

Keywords: agricultural characters, pea, Pisum sativum, seed yield

Procedia PDF Downloads 238

Authors: Dhahri Maher, Aouinet Hana

Abstract:

The presence of bubbles in the boundary layer introduces corrections into the log law, which must be taken into account. In this work, a logarithmic wall law was presented for bubbly two phase flows. The wall law presented in this work was based on the postulation of additional turbulent viscosity associated with bubble wakes in the boundary layer. The presented wall law contained empirical constant accounting both for shear induced turbulence interaction and for non-linearity of bubble. This constant was deduced from experimental data. The wall friction prediction achieved with the wall law was compared to the experimental data, in the case of a turbulent boundary layer developing on a vertical flat plate in the presence of millimetric bubbles. A very good agreement between experimental and numerical wall friction prediction was verified. The agreement was especially noticeable for the low void fraction when bubble induced turbulence plays a significant role.

Keywords: bubbly flows, log law, boundary layer, CFD

Procedia PDF Downloads 277

Authors: St. Subaedah, A. Takdir, Netty, D. Hidrawati

Abstract:

Maize development at the rainfed lowland after rice is often confronted with the occurrence of drought stress at the time of entering the generative phase, which will cause be hampered crop production. Consequently, in the utilization of the rainfed lowland areas optimally, an effort that can be done using the varieties of early maturity to minimize crop failures due to its short rainy season. The aim of this research was evaluating the potential yield of genotypes of candidates of maize early maturity in the rainfed lowland areas. The study was conducted during May to August 2016 at South Sulawesi, Indonesia. The study used randomized block design to compare 12 treatments and consists of 8 genotypes namely CH1, CH2, CH3, CH4, CH5, CH6, CH7, CH8 and the use of four varieties, namely Bima 3, Bima 7, Lamuru and Gumarang. The results showed that genotype of CH2, CH3, CH5, CH 6, CH7 and CH8 harvesting has less than 90 days. There are two genotypes namely genotypes of CH7 and CH8 that have a fairly high production respectively of 7.16 tons / ha and 8.11 tons/ ha and significantly not different from the superior varieties Bima3.

Keywords: evaluation, early maturity, maize, yield potential

Procedia PDF Downloads 192

Authors: Sandra Mitrovic, Gaurav Singh

Abstract:

In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

Procedia PDF Downloads 314

Authors: Emad A. Mohammed

Abstract:

Utilizing the capabilities of Data Mining and Artificial Intelligence in the prediction of the minimum miscibility pressure (MMP) required for multi-contact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding using Fuzzy Logic models and Artificial Neural Network models will help a lot in giving accurate results. The factors affecting the (MMP) as it is proved from the literature and from the dataset are as follows: XC2-6: Intermediate composition in the oil-containing C2-6, CO2 and H2S, in mole %, XC1: Amount of methane in the oil (%),T: Temperature (°C), MwC7+: Molecular weight of C7+ (g/mol), YC2+: Mole percent of C2+ composition in injected gas (%), MwC2+: Molecular weight of C2+ in injected gas. Fuzzy Logic and Neural Networks have been used widely in prediction and classification, with relatively high accuracy, in different fields of study. It is well known that the Fuzzy Inference system can handle uncertainty within the inputs such as in our case. The results of this work showed that our proposed models perform better with higher performance indices than other emprical correlations.

Keywords: MMP, gas flooding, artificial intelligence, correlation

Procedia PDF Downloads 143

Authors: Jahanzaib

Abstract:

The rural economy can be uplifted through agricultural extension education for female as the majority is uneducated. The present study was carried out in five districts (Bahawalpur, Lodhran, Raheem Yar Khan, Bahawalnagr, and Vehari) of southern Punjab, Pakistan. The ten females were selected from each district, poor economic background for agricultural training. The training was provided free of cost, through Punjab skills development program. After six month training, the trainees were awarded with certificates and a tool kit. After completion of training data was recorded and analyzed, the results indicate that, female trainees were in a better economic position than the females of nearby districts without training. From this study, we can conclude that agricultural education for female can not only improve the economy of the individual family but also improve the agriculture of Pakistan on the sustainable basis as the majority of workers are female in rural areas of Pakistan.

Keywords: agricultural extension education, sustainable rural development, agriculture, rural development in Pakistan

Procedia PDF Downloads 237

Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

Abstract:

Rainfall and runoff phenomenon is a chaotic and complex outcome of nature which requires sophisticated modelling and simulation methods for explanation and use. Time Series modelling allows runoff data analysis and can be used as forecasting tool. In the paper attempt is made to model river runoff data and predict the future behavioural pattern of river based on annual past observations of annual river runoff. The river runoff analysis and predict are done using ARIMA model. For evaluating the efficiency of prediction to hydrological events such as rainfall, runoff and etc., we use the statistical formulae applicable. The good agreement between predicted and observation river runoff coefficient of determination (R²) display that the ARIMA (4,1,1) is the suitable model for predicting Karkheh River runoff at Iran.

Keywords: time series modelling, ARIMA model, river runoff, Karkheh River, CLS method

Procedia PDF Downloads 337

Authors: Sondos M. Hammad, Sherin M. ElGokhy, Mahmoud M. Fahmy, Elsayed A. Sallam

Abstract:

In this paper, an ensemble-based Support Vector Machine (SVM) classification approach is proposed. It is used for miRNA prediction. Three problems, commonly associated with previous approaches, are alleviated. These problems arise due to impose assumptions on the secondary structural of premiRNA, imbalance between the numbers of the laboratory checked miRNAs and the pseudo-hairpins, and finally using a training data set that does not consider all the varieties of samples in different species. We aggregate the predicted outputs of three well-known SVM classifiers; namely, Triplet-SVM, Virgo and Mirident, weighted by their variant features without any structural assumptions. An additional SVM layer is used in aggregating the final output. The proposed approach is trained and then tested with balanced data sets. The results of the proposed approach outperform the three base classifiers. Improved values for the metrics of 88.88% f-score, 92.73% accuracy, 90.64% precision, 96.64% specificity, 87.2% sensitivity, and the area under the ROC curve is 0.91 are achieved.

Keywords: MiRNAs, SVM classification, ensemble algorithm, assumption problem, imbalance data

Procedia PDF Downloads 347

Authors: Bhojan Dhakal, Naba Raj Devkota, Chet Raj Upreti, Maheshwar Sapkota

Abstract:

A study was conducted to find out the production potential, nutrient composition, and the variability of the most commonly available fodder trees along with the varying altitude to help optimize the dry matter requirement during winter lean period. The study was carried out from March to June, 2012 in Lho and Prok Village Development Committee of Manaslu Conservation Area (MCA), located in Gorkha district of Nepal. The other objective of the research was to learn the impact of climate change on livestock production linking it with feed availability. The study was conducted in two parts: social and biological. Accordingly, a households (HHs) survey was conducted to collect primary data from 70 HHs, focusing on the perception of respondents on impacts of climatic variability on the feeding management. The next part consisted of understanding yield potential and nutrient composition of the four most commonly available fodder trees (M. azedirach, M. alba, F. roxburghii, F. nemoralis), within two altitudes range: (1500-2000 masl and 2000-2500 masl) by using a RCB design in 2*4 factorial combination of treatments, each replicated four times. Results revealed that majority of the farmers perceived the change in climatic phenomenon more severely within the past five years. Farmers were using different adaptation technologies such as collection of forage from jungle, reducing unproductive animals, fodder trees utilization, and crop by product feeding at feed scarcity period. Ranking of the different fodder trees on the basis of indigenous knowledge and experiences revealed that F. roxburghii was the best-preferred fodder tree species (index value 0.72) in terms overall preferability whereas M. azedirach had highest growth and productivity (index value 0.77), F. roxburghii had highest adoptability (index value 0.69) and palatability (index value 0.69) as well. Similarly, fresh yield and dry matter yield of the each fodder trees was significant (P < 0.01) between the altitude and within species. Fodder trees yield analysis revealed that the highest dry matter (DM) yield (28 kg/tree) was obtained for F. roxburghii but that remained statistically similar (P > 0.05) to the other treatment. On the other hand, most of the parameters: ether extract (EE), acid detergent lignin (ADL), acid detergent fibre (ADF), cell wall digestibility (CWD), relative digestibility (RD), digestible nutrient (TDN), and Calcium (Ca) among the treatments were highly significant (P < 0.01). This indicates the scope of introducing productive and nutritive fodder trees species even at the high altitude to help reduce fodder scarcity problem during winter. The finding also revealed the scope of promoting all available local fodder trees species as crude protein content of these species were similar.

Keywords: fodder trees, yield potential, climate change, nutrient composition

Procedia PDF Downloads 308

Authors: Tania Vazquez, Aida Huerta

Abstract:

Way to get our food has changed over the time, and it is a daily necessity. Nowadays we found a lot of problems involved with the economy, environment, and society, which affect the agrifood system. Some problems as construction of big cities and growing population have been increasing demand food directly. Due to the countryside are far away from the city, another alternative systems have come from, such as Urban Agriculture (UA). UA system offers food production into the cities, products with characteristics as quality, healthy and good prices, close to the customers, recycling culture and the promote environmental education. Last years in Mexico City urban gardens have taken strongly in various politic delegations. There are establishment’s public and private initiatives. Moreover, these places have had different issues like low income, many activities, few workers, low production, lack of training and advice, devaluation of your work and low sales, all these shortcomings generate the devaluation of their work. The aim of this paper is to evaluate a business plan in Mexico City´s urban gardens that contribute to ensuring economic, environmental and social sustainability; to adjust business plan for this places so that they reach viability over time. As a part of soft systems methodology developed of Peter Checkland, we interviewed owners of urban gardens and we found that recurring problem was lack planning manager activities and a master plan about their business. We evaluate the business plan based on “Ten principles in sustainable food value chain development” proposed for Food and Agriculture Organization of the United Nations (FAO). With this study was possible measure, understand and improve performance of business plan in the three pillars of the sustainability in addition to this it allowed us to fit in with the needs of urban gardens.

Keywords: business plan, Mexico City, urban agriculture, urban gardens

Procedia PDF Downloads 395

Authors: Kaoutar Abbahaddou, Mohammed Salah Chiadmi

Abstract:

The need to find a relevant way to predict the next-day price of a stock index is a real concern for many financial stakeholders and researchers. We have known across years the proliferation of several methods. Nevertheless, among all these methods, the most controversial one is a machine learning algorithm that claims to be reliable, namely neural networks. Thus, the purpose of this article is to study the prediction power of neural networks in the particular case of Islamic finance as it is an under-looked area. In this article, we will first briefly present a review of the literature regarding neural networks and Islamic finance. Next, we present the architecture and principles of artificial neural networks most commonly used in finance. Then, we will show its empirical application on two Islamic stock indexes. The accuracy rate would be used to measure the performance of the algorithm in predicting the right price the next day. As a result, we can conclude that artificial neural networks are a reliable method to predict the next-day price for Islamic indices as it is claimed for conventional ones.

Keywords: Islamic finance, stock price prediction, artificial neural networks, machine learning

Procedia PDF Downloads 236

Authors: Ognen Kostovski, Svetozar Antovic, Rubens Jovanovic, Irena Kostovska, Nikola Jankulovski

Abstract:

Introduction:Colorectal carcinoma (CRC) is one of the most common malignancies in the world. The cancer stem cell (CSC) markers are associated with aggressive cancer types and poor prognosis. The aim of study was to determine whether the expression of colorectal cancer stem cell markers CD133 and CD44 could be significant in prediction of clinically aggressive form of CRC. Materials and methods: Our study included ninety patients (n=90) with CRC. Patients were divided into two subgroups: with metatstatic CRC and non-metastatic CRC. Tumor samples were analyzed with standard histopathological methods, than was performed immunohistochemical analysis with monoclonal antibodies against CD133 and CD44 stem cell markers. Results: High coexpression of CD133 and CD44 was observed in 71.4% of patients with metastatic disease, compared to 37.9% in patients without metastases. Discordant expression of both markers was found in 8% of the subgroup with metastatic CRC, and in 13.4% of the subgroup without metastatic CRC. Statistical analyses showed a significant association of increased expression of CD133 and CD44 with the disease stage, T - category and N - nodal status. With multiple regression analysis the stage of disease was designate as a factor with the greatest statistically significant influence on expression of CD133 (p <0.0001) and CD44 (p <0.0001). Conclusion: Our results suggest that the coexpression of CD133 and CD44 have an important role in prediction of clinically aggressive form of CRC. Both stem cell markers can be routinely implemented in standard pathohistological diagnostics and can be useful markers for pre-therapeutic oncology screening.

Keywords: colorectal carcinoma, stem cells, CD133+, CD44+

Procedia PDF Downloads 146

Authors: Iman Janghorban Esfahani

Abstract:

Due to the increased use of irrigation in agriculture, the importance and need for highly reliable water pumping systems have significantly increased. The pumping of the groundwater is essential to provide water for both drip and furrow irrigation to increase the agricultural yield, especially in arid regions that suffer from scarcities of surface water. The most common irrigation pumping systems (IPS) consume conventional energies through the use of electric motors and generators or connecting to the electricity grid. Due to the shortage and transportation difficulties of fossil fuels, and unreliable access to the electricity grid, especially in the rural areas, and the adverse environmental impacts of fossil fuel usage, such as greenhouse gas (GHG) emissions, the need for renewable energy sources such as photovoltaic systems (PVS) as an alternative way of powering irrigation pumping systems is urgent. Integration of the photovoltaic systems with irrigation pumping systems as the Photovoltaic Powered-Irrigation Pumping System (PVP-IPS) can avoid fossil fuel dependency and the subsequent greenhouse gas emissions, as well as ultimately lower energy costs and improve efficiency, which made PVP-IPS systems as an environmentally and economically efficient solution for agriculture irrigation in every region. The greatest problem faced by integration of PVP with IPS systems is matching the intermittence of the energy supply with the dynamic water demand. The best solution to overcome the intermittence is to incorporate a storage system into the PVP-IPS to provide water-on-demand as a highly reliable stand-alone irrigation pumping system. The water storage tank (WST) is the most common storage device for PVP-IPS systems. In the integrated PVP-IPS with a water storage tank (PVP-IPS-WST), a water storage tank stores the water pumped by the IPS in excess of the water demand and then delivers it when demands are high. The Freshwater pinch analysis (FWaPA) as an alternative to mathematical modeling was used by other researchers for retrofitting the off-grid battery less photovoltaic-powered reverse osmosis system. However, the Freshwater pinch analysis has not been used to integrate the photovoltaic systems with irrigation pumping system with water storage tanks. In this study, FWaPA graphical and numerical tools were used for retrofitting an existing PVP-IPS system located in Salahadin, Republic of Iraq. The plant includes a 5 kW submersible water pump and 7.5 kW solar PV system. The Freshwater Composite Curve as the graphical tool and Freashwater Storage Cascade Table as the numerical tool were constructed to determine the minimum required outsourced water during operation, optimal amount of delivered electricity to the water pump, and optimal size of the water storage tank for one-year operation data. The results of implementing the FWaPA on the case study show that the PVP-IPS system with a WST as the reliable system can reduce outsourced water by 95.41% compare to the PVP-IPS system without storage tank.

Keywords: irrigation, photovoltaic, pinch analysis, pumping, solar energy

Procedia PDF Downloads 137

Authors: Sarika Singh, Moin Uddin, M. Masroor A. Khan, Aman Sobia Chishti, Sangram Singh, Urooj Hassan Bhatt

Abstract:

Mentha arvensis L. (Japanese mint) is a perennial plant carrying medicinal, aromatic, antiseptic, and anaesthetic properties. Plant-derived smoke-water (PDSW) plays a significant role in seed germination, seedling growth, and other physiological attributes. To ascertain the effect of PDSW and karrikinolide on Mentha arvensis L., a rhizome-soaking experiment was conducted on Mentha arvensis. Prior to planting, mint rhizomes were soaked for 24 hours with aqueous solutions of various concentrations of PDSW (1:125v/v, 1:250 v/v, 1:500 v/v, and 1:1000 v/v), karrikinolide (10-6M, 10⁻⁷M, 10⁻⁸M, and 10⁻⁹M) using double distilled water as control treatment. Rhizome soaking with 1:500 v/v concentration of PDSW and 10⁻⁸M concentration of KAR1 increased the growth attributes, including plant height, fresh weight, dry, leaf area, and leaf yield per plant of Mentha arvensis. Leaf physiological-parameters, viz. chlorophyll fluorescence, PSII activity, and total chlorophyll and carotenoid content, were also increased as a result of the application of this treatment PDSW (1:500 v/v) and KAR1 (10⁻⁸M). In addition, treatment with 1:500 v/v and 10⁻⁸M significantly increased the essential oil yield and active constituents of Mentha arvensis compared to the control. Results indicated that PDSW, being a cheap source of karrikins, might be successfully used to augment mint essential oil production.

Keywords: active constituents, essential oil, medicinal plant, mentha arvensis L

Procedia PDF Downloads 88