Search results for: flavor forecasting

Authors: Hassan Gholibeigian, Kazem Gholibeigian

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In this paper, our goal is determination of loading versus time in crust. For this goal, we present a computational procedure to propose a cumulative strain energy time profile which can be used to predict the approximate location and time of the next major earthquake (M > 4.5) along a specific fault, which we believe, is more accurate than many of the methods presently in use. In the coming pages, after a short review of the research works presently going on in the area of earthquake analysis and prediction, earthquake mechanisms in both the jerk and sequence earthquake direction is discussed, then our computational procedure is presented using differential equations of equilibrium which govern the nonlinear dynamic response of a system of finite elements, modified with an extra term to account for the jerk produced during the quake. We then employ Von Mises developed model for the stress strain relationship in our calculations, modified with the addition of an extra term to account for thermal effects. For calculation of the strain energy the idea of Pulsating Mantle Hypothesis (PMH) is used. This hypothesis, in brief, states that the mantle is under diurnal cyclic pulsating loads due to unbalanced gravitational attraction of the sun and the moon. A brief discussion is done on the Denali fault as a case study. The cumulative strain energy is then graphically represented versus time. At the end, based on some hypothetic earthquake data, the final results are verified.

Keywords: pulsating mantle hypothesis, inner core’s dislocation, outer core’s bulge, constitutive model, transient hydro-magneto-thermo-mechanical load, diurnal stress, jerk, fault behaviour

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Authors: K. G. Sorokozherdyev, E. A. Efimov

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The socio-economic development of regions and countries is an important research issue. Today, in the face of many negative events in the global and regional economies, it is especially important to identify those areas that can serve as sources of economic growth and the basis for the well-being of the population. This study aims to identify the most important sectors of the economy of the Arkhangelsk region that can contribute to the socio-economic development of the region as a whole. For research, the Arkhangelsk region was taken as one of the typical Russian regions that do not have significant reserves of hydrocarbons nor there are located any large industrial complexes. In this regard, the question of possible origins of economic growth seems especially relevant. The basis of this study constitutes the distributed lag regression model (ADL model) developed by the authors, which is based on quarterly data on the socio-economic development of the Arkhangelsk region for the period 2004-2016. As a result, we obtained three equations reflecting the dynamics of three indicators of the socio-economic development of the region -the average wage, the regional GRP, and the birth rate. The influencing factors are the shares in GRP of such sectors as agriculture, mining, manufacturing, construction, wholesale and retail trade, hotels and restaurants, as well as the financial sector. The study showed that the greatest influence on the socio-economic development of the region is exerted by such industries as wholesale and retail trade, construction, and industrial sectors. The study can be the basis for forecasting and modeling the socio-economic development of the Arkhangelsk region in the short and medium term. It also can be helpful while analyzing the effectiveness of measures aimed at stimulating those or other industries of the region. The model can be used in developing a regional development strategy.

Keywords: regional economic development, regional sectoral structure, ADL model, Arkhangelsk region

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Authors: Meng Su

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High-dimensional data analysis often presents challenges in capturing the complex, nonlinear relationships and manifold structures inherent to the data. This article presents a novel approach that leverages the strengths of two powerful techniques, Diffusion Maps and Diffusion Probabilistic Models (DPMs), to address these challenges. By integrating the dimensionality reduction capability of Diffusion Maps with the data modeling ability of DPMs, the proposed method aims to provide a comprehensive solution for analyzing and generating high-dimensional data. The Diffusion Map technique preserves the nonlinear relationships and manifold structure of the data by mapping it to a lower-dimensional space using the eigenvectors of the graph Laplacian matrix. Meanwhile, DPMs capture the dependencies within the data, enabling effective modeling and generation of new data points in the low-dimensional space. The generated data points can then be mapped back to the original high-dimensional space, ensuring consistency with the underlying manifold structure. Through a detailed example implementation, the article demonstrates the potential of the proposed hybrid approach to achieve more accurate and effective modeling and generation of complex, high-dimensional data. Furthermore, it discusses possible applications in various domains, such as image synthesis, time-series forecasting, and anomaly detection, and outlines future research directions for enhancing the scalability, performance, and integration with other machine learning techniques. By combining the strengths of Diffusion Maps and DPMs, this work paves the way for more advanced and robust data analysis methods.

Keywords: diffusion maps, diffusion probabilistic models (DPMs), manifold learning, high-dimensional data analysis

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Authors: Jang kyun Cho, Jeong-dong Lee

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The advent of the Internet, mobile communications, and social network services has stimulated social interactions among consumers, allowing people to affect one another’s innovation adoptions by exchanging information more frequently and more quickly. Previous diffusion models, such as the Bass model, however, face limitations in reflecting such recent phenomena in society. These models are weak in their ability to model interactions between agents; they model aggregated-level behaviors only. The agent based model, which is an alternative to the aggregate model, is good for individual modeling, but it is still not based on an economic perspective of social interactions so far. This study assumes the presence of social utility from other consumers in the adoption of innovation and investigates the effect of individual interactions on innovation diffusion by developing a new model called the interaction-based diffusion model. By comparing this model with previous diffusion models, the study also examines how the proposed model explains innovation diffusion from the perspective of economics. In addition, the study recommends the use of a small-world network topology instead of cellular automata to describe innovation diffusion. This study develops a model based on individual preference and heterogeneous social interactions using utility specification, which is expandable and, thus, able to encompass various issues in diffusion research, such as reservation price. Furthermore, the study proposes a new framework to forecast aggregated-level market demand from individual level modeling. The model also exhibits a good fit to real market data. It is expected that the study will contribute to our understanding of the innovation diffusion process through its microeconomic theoretical approach.

Keywords: innovation diffusion, agent based model, small-world network, demand forecasting

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Authors: Roien Qiam, Shoshi Mizokami

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Parking demand in the Central Business District (CBD) has enlarged with the increase of the number of private vehicles due to rapid economic growth, lack of an efficient public transport and traffic management system. This has resulted in low mobility, poor accessibility, serious congestion, high rates of traffic accident fatalities and injuries and air pollution, mainly because people have to drive slowly around to find a vacant spot. With parking pricing and enforcement policy, considerable advancement could be found, and on-street parking spaces could be managed efficiently and effectively. To evaluate parking demand and making parking policy, it is required to understand the current parking condition and driver’s behavior, understand how drivers choose their parking type and location as well as their behavior toward finding a vacant parking spot under parking charges and search times. This study illustrates the result from an observational, revealed and stated preference surveys and experiment. Attained data shows that there is a gap between supply and demand in parking and it has maximized. For the modeling of the parking decision, a choice model was constructed based on discrete choice modeling theory and multinomial logit model estimated by using SP survey data; the model represents the choice of an alternative among different alternatives which are priced on-street, off-street, and illegal parking. Individuals choose a parking type based on their preference concerning parking charges, searching times, access times and waiting times. The parking assignment model was obtained directly from behavioral model and is used in parking simulation. The study concludes with an evaluation of parking policy.

Keywords: CBD, parking demand forecast, parking policy, parking choice model

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Authors: John Okanda Okwaro

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Carbon (IV) oxide (CO₂) is emitted majorly from fossil fuel combustion and industrial production. The sources of interest of carbon (IV) oxide in the study area are mining activities, transport systems, and industrial processes. This study is aimed at building models that will help in monitoring the emissions within the study area. Three scenarios were discussed, namely: pessimistic scenario, business-as-usual scenario, and optimistic scenario. The result showed that there was a reduction in carbon dioxide concentration by approximately 50.5 ppm between March 2020 and January 2021 inclusive. This is majorly due to reduced human activities that led to decreased consumption of energy. Also, the CO₂ concentration trend follows the business-as-usual scenario (BAU) path. From the models, the pessimistic, business-as-usual, and optimistic scenarios give CO₂ concentration of about 545.9 ppm, 408.1 ppm, and 360.1 ppm, respectively, on December 31st, 2021. This research helps paint the picture to the policymakers of the relationship between energy sources and CO₂ emissions. Since the reduction in CO₂ emission was due to decreased use of fossil fuel as there was a decrease in economic activities, then if Kenya relies more on green energy than fossil fuel in the post-COVID-19 period, there will be more CO₂ emission reduction. That is, the CO₂ concentration trend is likely to follow the optimistic scenario path, hence a reduction in CO₂ concentration of about 48 ppm by the end of the year 2021. This research recommends investment in solar energy by energy-intensive companies, mine machinery and equipment maintenance, investment in electric vehicles, and doubling tree planting efforts to achieve the 10% cover.

Keywords: forecasting, greenhouse gas, green energy, hierarchical data format

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Authors: L. Krpalkova, N. O' Mahony, A. Carvalho, S. Campbell, G. Corkery, E. Broderick, J. Walsh

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Farm management and operations will drastically change due to access to real-time data, real-time forecasting, and tracking of physical items in combination with Internet of Things developments to further automate farm operations. Dairy farms have embraced technological innovations and procured vast amounts of permanent data streams during the past decade; however, the integration of this information to improve the whole farm-based management and decision-making does not exist. It is now imperative to develop a system that can collect, integrate, manage, and analyse on-farm and off-farm data in real-time for practical and relevant environmental and economic actions. The developed systems, based on machine learning and artificial intelligence, need to be connected for useful output, a better understanding of the whole farming issue, and environmental impact. Evolutionary computing can be very effective in finding the optimal combination of sets of some objects and, finally, in strategy determination. The system of the future should be able to manage the dairy farm as well as an experienced dairy farm manager with a team of the best agricultural advisors. All these changes should bring resilience and sustainability to dairy farming as well as improving and maintaining good animal welfare and the quality of dairy products. This review aims to provide an insight into the state-of-the-art of big data applications and evolutionary computing in relation to smart dairy farming and identify the most important research and development challenges to be addressed in the future. Smart dairy farming influences every area of management, and its uptake has become a continuing trend.

Keywords: big data, evolutionary computing, cloud, precision technologies

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Authors: Asmamaw Yehun, Tsegaye Kassa, Addisu Hunegnaw, Martin Vermeer

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There are various models to estimate the neutral atmospheric parameter values, such as in-suite and reanalysis datasets from numerical models. Accurate estimated values of the atmospheric parameters are useful for weather forecasting and, climate modeling and monitoring of climate change. Recently, Global Navigation Satellite System (GNSS) measurements have been applied for atmospheric sounding due to its robust data quality and wide horizontal and vertical coverage. The Global Positioning System (GPS) solutions that includes tropospheric parameters constitute a reliable set of data to be assimilated into climate models. The objective of this paper is, to estimate the neutral atmospheric parameters such as Wet Zenith Delay (WZD), Precipitable Water Vapour (PWV) and Total Zenith Delay (TZD) using six selected GPS stations in the equatorial regions, more precisely, the Ethiopian GPS stations from 2012 to 2015 observational data. Based on historic estimated GPS-derived values of PWV, we forecasted the PWV from 2015 to 2030. During data processing and analysis, we applied GAMIT-GLOBK software packages to estimate the atmospheric parameters. In the result, we found that the annual averaged minimum values of PWV are 9.72 mm for IISC and maximum 50.37 mm for BJCO stations. The annual averaged minimum values of WZD are 6 cm for IISC and maximum 31 cm for BDMT stations. In the long series of observations (from 2012 to 2015), we also found that there is a trend and cyclic patterns of WZD, PWV and TZD for all stations.

Keywords: atmosphere, GNSS, neutral atmosphere, precipitable water vapour

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Authors: Ana C. V. Freitas, Luciana B. M. Pires, Joao P. Martins

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In recent years, fire dynamics in deforestation areas of tropical forests have received considerable attention because of their relationship to climate change. Climate models project great increases in the frequency and area of drought in the Amazon region, which may increase the occurrence of fires. This study analyzes the historical record number of fire outbreaks in 2017 using satellite-derived data sets of active fire detections, burned area, precipitation, and data of the Fire Program from the Center for Weather Forecasting and Climate Studies (CPTEC/INPE). A downward trend in the number of fire outbreaks occurred in the first half of 2017, in relation to the previous year. This decrease can be related to the fact that 2017 was not an El Niño year and, therefore, the observed rainfall and temperature in the Amazon region was close to normal conditions. Meanwhile, the worst period in history for fire outbreaks began with the subsequent arrival of the dry season. September of 2017 exceeded all monthly records for number of fire outbreaks per month in the entire series. This increase was mainly concentrated in Bolivia and in the states of Amazonas, northeastern Pará, northern Rondônia and Acre, regions with high densities of rural settlements, which strongly suggests that human action is the predominant factor, aggravated by the lack of precipitation during the dry season allowing the fires to spread and reach larger areas. Thus, deforestation in the Amazon is primarily a human-driven process: climate trends may be providing additional influences.

Keywords: Amazon forest, climate change, deforestation, human-driven process, fire outbreaks

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Authors: Eyup Dogan

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One criticism of the energy-growth-environment literature, to the best of our knowledge, is that only a few studies analyze the influence of tourism on CO₂ emissions even though tourism sector is closely related to the environment. The other criticism is the selection of methodology. Panel estimation techniques that fail to consider both heterogeneity and cross-sectional dependence across countries can cause forecasting errors. To fulfill the mentioned gaps in the literature, this study analyzes the impacts of real GDP, renewable energy and tourism on the levels of carbon dioxide (CO₂) emissions for the top 10 most-visited countries around the world. This study focuses on the top 10 touristic (most-visited) countries because they receive about the half of the worldwide tourist arrivals in late years and are among the top ones in 'Renewables Energy Country Attractiveness Index (RECAI)'. By looking at Pesaran’s CD test and average growth rates of variables for each country, we detect the presence of cross-sectional dependence and heterogeneity. Hence, this study uses second generation econometric techniques (cross-sectionally augmented Dickey-Fuller (CADF), and cross-sectionally augmented IPS (CIPS) unit root test, the LM bootstrap cointegration test, and the DOLS and the FMOLS estimators) which are robust to the mentioned issues. Therefore, the reported results become accurate and reliable. It is found that renewable energy mitigates the pollution whereas real GDP and tourism contribute to carbon emissions. Thus, regulatory policies are necessary to increase the awareness of sustainable tourism. In addition, the use of renewable energy and the adoption of clean technologies in tourism sector as well as in producing goods and services play significant roles in reducing the levels of emissions.

Keywords: air pollution, tourism, renewable energy, income, panel data

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Authors: Agada N. Ihuoma, Nagata Yasunori

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Artificial intelligence (AI) techniques play a crucial role in predicting the expected energy outcome and its performance, analysis, modeling, and control of renewable energy. Renewable energy is becoming more popular for economic and environmental reasons. In the face of global energy consumption and increased depletion of most fossil fuels, the world is faced with the challenges of meeting the ever-increasing energy demands. Therefore, incorporating artificial intelligence to predict solar radiation outcomes from the intermittent sunlight is crucial to enable a balance between supply and demand of energy on loads, predict the performance and outcome of solar energy, enhance production planning and energy management, and ensure proper sizing of parameters when generating clean energy. However, one of the major problems of forecasting is the algorithms used to control, model, and predict performances of the energy systems, which are complicated and involves large computer power, differential equations, and time series. Also, having unreliable data (poor quality) for solar radiation over a geographical location as well as insufficient long series can be a bottleneck to actualization. To overcome these problems, this study employs the anaconda Navigator (Jupyter Notebook) for machine learning which can combine larger amounts of data with fast, iterative processing and intelligent algorithms allowing the software to learn automatically from patterns or features to predict the performance and outcome of Solar Energy which in turns enables the balance of supply and demand on loads as well as enhance production planning and energy management.

Keywords: artificial Intelligence, backward elimination, linear regression, solar energy

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Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

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What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: political tendency, prediction, sentiment analysis, Twitter

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Authors: Luis C. Parra

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The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm.

Keywords: significant wave height, machine learning optimization, multilayer perceptron neural networks, evolutionary algorithms

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Authors: Tomasz Borowski, Dawid Sołoducha, Agata Markowska-Szczupak, Aneta Wesołowska, Marian Kordas, Rafał Rakoczy

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Essential oils (EOs) are fragrant volatile oils obtained from plants. These are used for cooking (for flavor and aroma), cleaning, beauty (e.g., rosemary essential oil is used to promote hair growth), health (e.g. thyme essential oil cures arthritis, normalizes blood pressure, reduces stress on the heart, cures chest infection and cough) and in the food industry as preservatives and antioxidants. Rosemary and thyme essential oils are considered the most eminent herbs based on their history and medicinal properties. They possess a wide range of activity against different types of bacteria and fungi compared with the other oils in both in vitro and in vivo studies. However, traditional uses of EOs are limited due to rosemary and thyme oils in high concentrations can be toxic. In light of the accessible data, the following hypothesis was put forward: Low frequency rotating magnetic field (RMF) increases the antimicrobial potential of EOs. The aim of this work was to investigate the antimicrobial activity of commercial Salvia Rosmarinus L. and Thymus vulgaris L. essential oil from Polish company Avicenna-Oil under Rotating Magnetic Field (RMF) at f = 25 Hz. The self-constructed reactor (MAP) was applied for this study. The chemical composition of oils was determined by gas chromatography coupled with mass spectrometry (GC-MS). Model bacteria Escherichia coli K12 (ATCC 25922) was used. Minimum inhibitory concentrations (MIC) against E. coli were determined for the essential oils. Tested oils in very small concentrations were prepared (from 1 to 3 drops of essential oils per 3 mL working suspensions). From the results of disc diffusion assay and MIC tests, it can be concluded that thyme oil had the highest antibacterial activity against E. coli. Moreover, the study indicates the exposition to the RMF, as compared to the unexposed controls causing an increase in the efficacy of antibacterial properties of tested oils. The extended radiation exposure to RMF at the frequency f= 25 Hz beyond 160 minutes resulted in a significant increase in antibacterial potential against E. coli. Bacteria were killed within 40 minutes in thyme oil in lower tested concentration (1 drop of essential oils per 3 mL working suspension). Rapid decrease (>3 log) of bacteria number was observed with rosemary oil within 100 minutes (in concentration 3 drops of essential oils per 3 mL working suspension). Thus, a method for improving the antimicrobial performance of essential oil in low concentrations was developed. However, it still remains to be investigated how bacteria get killed by the EOs treated by an electromagnetic field. The possible mechanisms relies on alteration in the permeability of ionic channels in ionic channels in the bacterial cell walls that transport in the cells was proposed. For further studies, it is proposed to examine other types of essential oils and other antibiotic-resistant bacteria (ARB), which are causing a serious concern throughout the world.

Keywords: rotating magnetic field, rosemary, thyme, essential oils, Escherichia coli

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Authors: Jackson Simon Lusagalika, Lai Hsin-Chih, Dai Yu-Tung

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Particles with an aerodynamic diameter of 2.5 meters or less are referred to as "fine particulate matter" (PM₂.₅) are easily inhaled and can go deeper into the lungs than other particles in the atmosphere, where it may have detrimental health consequences. In this study, we use a hybrid model that combined CMAQ and AERMOD as well as initial meteorological fields from the Weather Research and Forecasting (WRF) model to study the impact of power plant PM₂.₅ emissions in South Taiwan since it frequently experiences higher PM₂.₅ levels. A specific date of March 3, 2022, was chosen as a result of a power outage that prompted the bulk of power plants to shut down. In some way, it is not conceivable anywhere in the world to turn off the power for the sole purpose of doing research. Therefore, this catastrophe involving a power outage and the shutdown of power plants offers a great occasion to evaluate the impact of air pollution driven by this power sector. As a result, four numerical experiments were conducted in the study using the Continuous Emission Data System (CEMS), assuming that the power plants continued to function normally after the power outage. The hybrid model results revealed that power plants have a minor impact in the study region. However, we examined the accumulation of PM₂.₅ in the study and discovered that once the vortex at 925hPa was established and moved to the north of Taiwan's coast, the study region experienced higher observed PM₂.₅ concentrations influenced by meteorological factors. This study recommends that decision-makers take into account not only control techniques, specifically emission reductions, but also the atmospheric and meteorological implications for future investigations.

Keywords: PM₂.₅ concentration, powerplants, hybrid air quality model, CEMS, Vorticity

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Authors: Thewodros K. Geberemariam

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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

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Authors: Monika Trzaskowska, Dorota Zielinska, Anna Lepecka, Katarzyna Neffe-Skocinska, Beata Bilska, Marzena Tomaszewska, Danuta Kolozyn-Krajewska

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Microbiological quality and food safety are important food characteristics. Regulation (EU) No 1169/2011 of the European Parliament and of the Council on the provision of food information to consumers introduces the obligation to provide information on the 'use-by' date or the date of minimum durability (DMD). The second term is the date until which the properly stored or transported foodstuff retains its physical, chemical, microbiological and organoleptic properties. The date should be preceded by 'best before'. It is used for durable products, e.g., pasta. In relation to reducing food losses, the question may be asked whether products with the date of minimum durability currently declared retain quality and safety beyond this. The aim of the study was to assess the sensory quality and microbiological safety of selected cereal products, i.e., pasta and millet after DMD. The scope of the study was to determine the markers of microbiological quality, i.e., the total viable count (TVC), the number of bacteria from the Enterobacteriaceae family and the number of yeast and mold (TYMC) on the last day of DMD and after 1 and 3 months of storage. In addition, the presence of Salmonella and Listeria monocytogenes was examined on the last day of DMD. The sensory quality of products was assessed by quantitative descriptive analysis (QDA), the intensity of 14 differentiators and overall quality were defined and determined. In the tested samples of millet and pasta, no pathogenic bacteria Salmonella and Listeria monocytogenes were found. The value of the distinguishing features of selected quality and microbiological safety indicators on the last DMD day was in the range of about 3-1 log cfu/g. This demonstrates the good microbiological quality of the tested food. Comparing the products, a higher number of microorganisms was found in the samples of millet. After 3 months of storage, TVC decreased in millet, while in pasta, it was found to increase in value. In both products, the number of bacteria from the Enterobacretiaceae family decreased. In contrast, the number of TYMCs increased in samples of millet, and in pasta decreased. The intensity of sensory characteristic in the studied period varied. It remained at a similar level or increased. Millet was found to increase the intensity and flavor of 'cooked porridge' 3 months after DMD. Similarly, in the pasta, the smell and taste of 'cooked pasta' was more intense. To sum up, the researched products on the last day of the minimum durability date were characterized by very good microbiological and sensory quality, which was maintained for 3 months after this date. Based on these results, the date of minimum durability of tested products could be extended. The publication was financed on the basis of an agreement with the National Center for Research and Development No. Gospostrateg 1/385753/1/NCBR/2018 for the implementation and financing of the project under the strategic research and development program 'social and economic development of Poland in the conditions of globalizing markets – GOSPOSTRATEG - acronym PROM'.

Keywords: date of minimum durability, food losses, food quality and safety, millet, pasta

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Authors: Milca F. Coelho, K. Bousson, Kawser Ahmed

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To accurately track an aerospace vehicle in a time-critical situation and in a highly nonlinear environment, is one of the strongest interests within the aerospace community. The tracking is achieved by estimating accurately the state of a moving target, which is composed of a set of variables that can provide a complete status of the system at a given time. One of the main ingredients for a good estimation performance is the use of efficient estimation algorithms. A well-known framework is the Kalman filtering methods, designed for prediction and estimation problems. The success of the Kalman Filter (KF) in engineering applications is mostly due to the Extended Kalman Filter (EKF), which is based on local linearization. Besides its popularity, the EKF presents several limitations. To address these limitations and as a possible solution to tracking problems, this paper proposes the use of the Ensemble Kalman Filter (EnKF). Although the EnKF is being extensively used in the context of weather forecasting and it is being recognized for producing accurate and computationally effective estimation on systems with a very high dimension, it is almost unknown by the tracking community. The EnKF was initially proposed as an attempt to improve the error covariance calculation, which on the classic Kalman Filter is difficult to implement. Also, in the EnKF method the prediction and analysis error covariances have ensemble representations. These ensembles have sizes which limit the number of degrees of freedom, in a way that the filter error covariance calculations are a lot more practical for modest ensemble sizes. In this paper, a realistic simulation of a radar tracking was performed, where the EnKF was applied and compared with the Extended Kalman Filter. The results suggested that the EnKF is a promising tool for tracking applications, offering more advantages in terms of performance.

Keywords: Kalman filter, nonlinear state estimation, optimal tracking, stochastic environment

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Authors: María Luisa Gonzalez-SanJose, Javier Garcia-Lomillo, Raquel Del Pino, Miriam Ortega-Heras, Maria Dolores Rivero-Perez, Pilar Muñiz-Rodriguez

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One of the most preoccupant problems related to the diet of the occidental societies is the high salt intake. In Spain, salt intake is almost twice as recommended by the World Health Organization (WHO). A lot of negative health effects of high sodium intake have been described being the hypertension, cardiovascular and coronary diseases ones of the most important. Due to this fact, government and other institutions are working on the gradual reduction of this consumption. Intake of meat products have been described as the main processed products that bring salt to the diet, followed by snacks and savory crackers. However, fortunately, the food industry has also raised awareness of this problem and is working intensely, and in recent years attempts to reduce the salt content in processed products, and is developing special lines with low sodium content. It is important to consider that processed food are the main source of sodium in occidental countries. One of the possible strategies to reduce the salt content in food is to find substitutes that can emulate their taste properties without adding much sodium or products that mask or substitute salty sensations with other flavors and aromas. In this sense, multiple products have been proposed and used until now. Potassium salts produce similar salty sensations without bring sodium, however their intake should be also limited, by healthy reasons. Furthermore, some potassium salts shows some better notes. Other alternatives are the use of flavor enhancers, spices, aromatic herbs, sea-plant derivate products, etc. The wine pomace is rich in potassium salts, content organic acid and other flavored substances, therefore it could be an interesting raw material to obtain derived products that could be useful as alternative ‘seasonings’. Considering previous comments, the main aim of this study was to evaluate the possible use of a natural seasoning, made from red wine pomace, in two different foods, crackers and burgers. The seasoning was made in the pilot plant of food technology of the University of Burgos, where the studied crackers and patties were also made. Different members of the University, students, docent and administrative personal, taste the products, and a trained panel evaluated salty intensity. The seasoning in addition to potassium contain significant levels of dietary fiber and phenolic compounds, which also makes it interesting as a functional ingredient. Both burgers and crackers made with the seasoning showed better taste that those without salt. Obviously, they showed lower sodium content than normal formulation, and were richer in potassium, antioxidant and fiber. Then, they showed lower values of the relation Na/K. All these facts are correlated with more ‘healthy’ products especially to that people with hypertension and other coronary dysfunctions.

Keywords: healthy foods, low salt, seasoning, wine pomace

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Authors: Korpapa Srisamai, Pawee Siriruk

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The purpose of this study is to forecast product demands and develop appropriate and adequate procurement plans to meet customer needs and reduce costs. When the product exceeds customer demands or does not move, it requires the company to support insufficient storage spaces. Moreover, some items, when stored for a long period of time, cause deterioration to dead stock. A case study of the wholesale company of electronic equipment and components, which has uncertain customer demands, is considered. The actual purchasing orders of customers are not equal to the forecast provided by the customers. In some cases, customers have higher product demands, resulting in the product being insufficient to meet the customer's needs. However, some customers have lower demands for products than estimates, causing insufficient storage spaces and dead stock. This study aims to reduce the loss of sales opportunities and the number of remaining goods in the warehouse, citing 30 product samples of the company's most popular products. The data were collected during the duration of the study from January to October 2022. The methods used to forecast are simple moving averages, weighted moving average, and exponential smoothing methods. The economic ordering quantity and reorder point are used to calculate to meet customer needs and track results. The research results are very beneficial to the company. The company can reduce the loss of sales opportunities by 20% so that the company has enough products to meet customer needs and can reduce unused products by up to 10% dead stock. This enables the company to order products more accurately, increasing profits and storage space.

Keywords: demand forecast, reorder point, lost sale, dead stock

Procedia PDF Downloads 89

Authors: Sujata Kulkarni, Udhav Bhosle, Vijaykumar T.

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Due to the closeness between seismic signals and non-seismic signals, it is vital to detect earthquakes using conventional methods. In order to distinguish between seismic events and non-seismic events depending on their amplitude, our study processes the data that come from seismic sensors. The authors suggest a robust noise suppression technique that makes use of a bandpass filter, an IIR Wiener filter, recursive short-term average/long-term average (STA/LTA), and Carl short-term average (STA)/long-term average for event identification (LTA). The trigger ratio used in the proposed study to differentiate between seismic and non-seismic activity is determined. The proposed work focuses on significant feature extraction for machine learning-based seismic event detection. This serves as motivation for compiling a dataset of all features for the identification and forecasting of seismic signals. We place a focus on feature vector dimension reduction techniques due to the temporal complexity. The proposed notable features were experimentally tested using a machine learning model, and the results on unseen data are optimal. Finally, a presentation using a hybrid dataset (captured by different sensors) demonstrates how this model may also be employed in a real-time setting while lowering false alarm rates. The planned study is based on the examination of seismic signals obtained from both individual sensors and sensor networks (SN). A wideband seismic signal from BSVK and CUKG station sensors, respectively located near Basavakalyan, Karnataka, and the Central University of Karnataka, makes up the experimental dataset.

Keywords: Carl STA/LTA, features extraction, real time, dataset, machine learning, seismic detection

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Authors: Asma Naim, Warda Mushtaq

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Staphylococcus species are the most versatile and adaptive organism. They are widespread and naturally found on the skin, mucosa and nose in humans. Among these, Staphylococcus aureus is the most important species. These organisms act as opportunistic pathogens and can infect various organs of the host, causing minor skin infection to severe toxin mediated diseases, and life threatening nosocomial infections. Staphylococcus aureus has acquired resistance against β-lactam antibiotics by the production of β-lactamase, and Methicillin-Resistant Staphylococcus aureus (MRSA) strains have also been reported with increasing frequency. MRSA strains have been associated with nosocomial as well as community acquired infections. Medicinal plants have enormous potential as antimicrobial substances and have been used in traditional medicine. Search for medicinally valuable plants with antimicrobial activity is being emphasized due to increasing antibiotic resistance in bacteria. In the present study, the antibacterial potential of Rosmarinus officinalis (Rosemary) and Murraya koenigii (curry leaves) was evaluated. These are common household herbs used in food as enhancer of flavor and aroma. The crude aqueous infusion, decoction and ethanolic extracts of curry leaves and rosemary and essential oil of rosemary were investigated in the present study for antibacterial activity against multi-drug resistant Staphylococcus strains using well diffusion method. In the present study, 60 Multi-drug resistant clinical isolates of S. aureus (43) and Coagulase Negative Staphylococci (CoNS) (17) were screened against different concentrations of crude extracts of Rosmarinus officinalis and Murraya koenigii. Out of these 60 isolates, 43 were sensitive to the aqueous infusion of rosemary; 23 to aqueous decoction and 58 to ethanolic extract whereas, 24 isolates were sensitive to the essential oil. In the case of the curry leaves, no antibacterial activity was observed in aqueous infusion and decoction while only 14 isolates were sensitive to the ethanolic extract. The aqueous infusion of rosemary (50% concentration) exhibited a zone of inhibition of 21(±5.69) mm. against CoNS and 17(±4.77) mm. against S. aureus, the zone of inhibition of 50% concentration of aqueous decoction of rosemary was also larger against CoNS 17(±5.78) mm. then S. aureus 13(±6.91) mm. and the 50% concentrated ethanolic extract showed almost similar zone of inhibition in S. aureus 22(±3.61) mm. and CoNS 21(±7.64) mm. whereas, the essential oil of rosemary showed greater zone of inhibition against S. aureus i.e., 16(±4.67) mm. while CoNS showed 15(±6.94) mm. These results show that ethanolic extract of rosemary has significant antibacterial activity. Aqueous infusion and decoction of curry leaves revealed no significant antibacterial potential against all Staphylococcal species and ethanolic extract also showed only a weak response. Staphylococcus strains were susceptible to crude extracts and essential oil of rosemary in a dose depend manner, where the aqueous infusion showed highest zone of inhibition and ethanolic extract also demonstrated antistaphylococcal activity. These results demonstrate that rosemary possesses antistaphylococcal activity.

Keywords: antibacterial activity, curry leaves, multidrug resistant, rosemary, S. aureus

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Authors: Pakize Ozlem Kurt Polat

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GMO (genetically modified organism) is the result of a laboratory process where genes from the DNA of one species are extracted and artificially forced into the genes of an unrelated plant or animal. This technology includes; nucleic acid hybridization, recombinant DNA, RNA, PCR, cell culture and gene cloning techniques. The studies are divided into three groups of properties transferred to the transgenic plant. Up to 59% herbicide resistance characteristic of the transfer, 28% resistance to insects and the virus seems to be related to quality characteristics of 13%. Transgenic crops are not included in the commercial production of each product; mostly commercial plant is soybean, maize, canola, and cotton. Day by day increasing GMO interest can be listed as follows; Use in the health area (Organ transplantation, gene therapy, vaccines and drug), Use in the industrial area (vitamins, monoclonal antibodies, vaccines, anti-cancer compounds, anti -oxidants, plastics, fibers, polyethers, human blood proteins, and are used to produce carotenoids, emulsifiers, sweeteners, enzymes , food preservatives structure is used as a flavor enhancer or color changer),Use in agriculture (Herbicide resistance, Resistance to insects, Viruses, bacteria, fungi resistance to disease, Extend shelf life, Improving quality, Drought , salinity, resistance to extreme conditions such as frost, Improve the nutritional value and quality), we explain all this methods step by step in this research. GMO has advantages and disadvantages, which we explain all of them clearly in full text, because of this topic, worldwide researchers have divided into two. Some researchers thought that the GMO has lots of disadvantages and not to be in use, some of the researchers has opposite thought. If we look the countries law about GMO, we should know Biosafety law for each country and union. For this Biosecurity reasons, the problems caused by the transgenic plants, including Turkey, to minimize 130 countries on 24 May 2000, ‘the United Nations Biosafety Protocol’ signed nudes. This protocol has been prepared in addition to Cartagena Biosafety Protocol entered into force on September 11, 2003. This protocol GMOs in general use by addressing the risks to human health, biodiversity and sustainable transboundary movement of all GMOs that may affect the prevention, transit covers were dealt and used. Under this protocol we have to know the, ‘US Regulations GMO’, ‘European Union Regulations GMO’, ‘Turkey Regulations GMO’. These three different protocols have different applications and rules. World population increasing day by day and agricultural fields getting smaller for this reason feeding human and animal we should improve agricultural product yield and quality. Scientists trying to solve this problem and one solution way is molecular biotechnology which is including the methods of GMO too. Before decide to support or against the GMO, should know the GMO protocols and it effects.

Keywords: biotechnology, GMO (genetically modified organism), molecular marker

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Authors: Nkechi Neboh, Josiah Adeyemo, Abimbola Enitan, Oludayo Olugbara

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Evolutionary algorithms are techniques extensively used in the planning and management of water resources and systems. It is useful in finding optimal solutions to water resources problems considering the complexities involved in the analysis. River basin management is an essential area that involves the management of upstream, river inflow and outflow including downstream aspects of a reservoir. Water as a scarce resource is needed by human and the environment for survival and its management involve a lot of complexities. Management of this scarce resource is necessary for proper distribution to competing users in a river basin. This presents a lot of complexities involving many constraints and conflicting objectives. Evolutionary algorithms are very useful in solving this kind of complex problems with ease. Evolutionary algorithms are easy to use, fast and robust with many other advantages. Many applications of evolutionary algorithms, which are population based search algorithm, are discussed. Different methodologies involved in the modeling and simulation of water management problems in river basins are explained. It was found from this work that different evolutionary algorithms are suitable for different problems. Therefore, appropriate algorithms are suggested for different methodologies and applications based on results of previous studies reviewed. It is concluded that evolutionary algorithms, with wide applications in water resources management, are viable and easy algorithms for most of the applications. The results suggested that evolutionary algorithms, applied in the right application areas, can suggest superior solutions for river basin management especially in reservoir operations, irrigation planning and management, stream flow forecasting and real-time applications. The future directions in this work are suggested. This study will assist decision makers and stakeholders on the best evolutionary algorithm to use in varied optimization issues in water resources management.

Keywords: evolutionary algorithm, multi-objective, reservoir operation, river basin management

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Authors: Thomas A. Fuller

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Over the past fifteen years, virtual schooling has been introduced and implemented in varying degrees throughout the public education system in the United States. It is possible in some states for students to voluntarily take all of their course load online, without ever having to step in a classroom. Experts foresee a dramatic rise in the number of courses taken online by public school students in the United States, with some predicting that by 2019 as many as 50% of public high school courses will be delivered online. This electronic delivery of public education offers tremendous potential to the scientific community because it calls for innovation and is funded by public school revenue. Public accountability provides a ready supply of statistical data for measuring the progress of virtual schools as they are implemented into the public school arena. This allows for a survey of the current use of virtual schooling through examination of past statistical data, as well as forecasting forward for future years based upon this past data. Virtual schooling is on the rise in the United States, but its growth has been tempered by practical problems of implementation. The greatest and best use of virtual schooling thus far has been to supplement the courses offered by public schools (e.g., offering unique language courses, elective courses, and games-based math and science courses). The weaknesses of virtual schooling lay in the problematic accountability in allowing students to take courses online at home and the lack of supportive infrastructure in the public school arena. Virtual schooling holds great promise for the public school education system in the United States, as well as the scientific community. Online courses allow students access to a much greater catalog of courses than is offered through classroom instruction in their local public school. This promising sector needs assistance from the scientific community in implementing new pedagogical methodologies.

Keywords: virtual schools, online classroom, electronic delivery, technological innovation

Procedia PDF Downloads 357

Authors: Kateryna Zharan, Jan C. Bongaerts

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In recent years, economic attractiveness of renewable energy (RE) for the mining industry, especially for off-grid mines, and a negative environmental impact of fossil energy are stimulating to use RE for mining needs. Being that remote area mines have higher energy expenses than mines connected to a grid, integration of RE may give a mine economic benefits. Regarding the literature review, there is a lack of business models for adopting of RE at mine. The main aim of this paper is to develop an optimized model of RE integration into the German mining industry (GMI). Hereby, the GMI with amount of around 800 mill. t. annually extracted resources is included in the list of the 15 major mining country in the world. Accordingly, the mining potential of Germany is evaluated in this paper as a perspective market for RE implementation. The GMI has been classified in order to find out the location of resources, quantity and types of the mines, amount of extracted resources, and access of the mines to the energy resources. Additionally, weather conditions have been analyzed in order to figure out where wind and solar generation technologies can be integrated into a mine with the highest efficiency. Despite the fact that the electricity demand of the GMI is almost completely covered by a grid connection, the hybrid energy system (HES) based on a mix of RE and fossil energy is developed due to show environmental and economic benefits. The HES for the GMI consolidates a combination of wind turbine, solar PV, battery and diesel generation. The model has been calculated using the HOMER software. Furthermore, the demonstrated HES contains a forecasting model that predicts solar and wind generation in advance. The main result from the HES such as CO2 emission reduction is estimated in order to make the mining processing more environmental friendly.

Keywords: diesel generation, German mining industry, hybrid energy system, hybrid optimization model for electric renewables, optimized model, renewable energy

Procedia PDF Downloads 319

Authors: Ivanor Zardo, Fernanda Walper Da Cunha, Júlia Sarkis, Ligia Damasceno Ferreira Marczak

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Lipid oxidation is one of the most important deteriorating processes in oil industry, resulting in the losses of nutritional value of oils as well as changes in color, flavor and other physiological properties. Autoxidation of lipids occurs naturally between molecular oxygen and the unsaturation of fatty acids, forming fat-free radicals, peroxide free radicals and hydroperoxides. In order to avoid the lipid oxidation in vegetable oils, synthetic antioxidants such as butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT) and tertiary butyl hydro-quinone (TBHQ) are commonly used. However, the use of synthetic antioxidants has been associated with several health side effects and toxicity. The use of natural antioxidants as stabilizers of vegetable oils is being suggested as a sustainable alternative to synthetic antioxidants. The alternative that has been studied is the use of natural extracts obtained mainly from fruits, vegetables and seeds, which have a well-known antioxidant activity related mainly to the presence of phenolic compounds. The sunflower seed cake is rich in phenolic compounds (1 4% of the total mass), being the chlorogenic acid the major constituent. The aim of this study was to evaluate the in vitro application of the phenolic extract obtained from the sunflower seed cake as a retarder of the lipid oxidation reaction in soybean oil and to compare the results with a synthetic antioxidant. For this, the soybean oil, provided from the industry without any addition of antioxidants, was subjected to an accelerated storage test for 17 days at 65 °C. Six samples with different treatments were submitted to the test: control sample, without any addition of antioxidants; 100 ppm of synthetic antioxidant BHT; mixture of 50 ppm of BHT and 50 ppm of phenolic compounds; and 100, 500 and 1200 ppm of phenolic compounds. The phenolic compounds concentration in the extract was expressed in gallic acid equivalents. To evaluate the oxidative changes of the samples, aliquots were collected after 0, 3, 6, 10 and 17 days and analyzed for the peroxide, diene and triene conjugate values. The soybean oil sample initially had a peroxide content of 2.01 ± 0.27 meq of oxygen/kg of oil. On the third day of the treatment, only the samples treated with 100, 500 and 1200 ppm of phenolic compounds showed a considerable oxidation retard compared to the control sample. On the sixth day of the treatment, the samples presented a considerable increase in the peroxide value (higher than 13.57 meq/kg), and the higher the concentration of phenolic compounds, the lower the peroxide value verified. From the tenth day on, the samples had a very high peroxide value (higher than 55.39 meq/kg), where only the sample containing 1200 ppm of phenolic compounds presented significant oxidation retard. The samples containing the phenolic extract were more efficient to avoid the formation of the primary oxidation products, indicating effectiveness to retard the reaction. Similar results were observed for dienes and trienes. Based on the results, phenolic compounds, especially chlorogenic acid (the major phenolic compound of sunflower seed cake), can be considered as a potential partial or even total substitute for synthetic antioxidants.

Keywords: chlorogenic acid, natural antioxidant, vegetables oil deterioration, waste valorization

Procedia PDF Downloads 238

Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Bhenjamin Jordan L. Ona

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Weather modification is an act of altering weather systems that catches interest on scientific studies. Cloud seeding is a common form of weather alteration. On the same principle, tropical cyclone mitigation experiment follows the methods of cloud seeding with intensity to account for. This study will present the effects of aerosol to tropical cyclone cloud microphysics and intensity. The framework of Weather Research and Forecasting (WRF) model incorporated with Thompson aerosol-aware scheme is the prime host to support the aerosol-cloud microphysics calculations of cloud condensation nuclei (CCN) ingested into the tropical cyclones before making landfall over the Philippine coast. The coupled microphysical and radiative effects of aerosols will be analyzed using numerical data conditions of Tropical Storm Ketsana (2009), Tropical Storm Washi (2011), and Typhoon Haiyan (2013) associated with varying CCN number concentrations per simulation per typhoon: clean maritime, polluted, and very polluted having 300 cm-3, 1000 cm-3, and 2000 cm-3 aerosol number initial concentrations, respectively. Aerosol species like sulphates, sea salts, black carbon, and organic carbon will be used as cloud nuclei and mineral dust as ice nuclei (IN). To make the study as realistic as possible, investigation during the biomass burning due to forest fire in Indonesia starting October 2015 as Typhoons Mujigae/Kabayan and Koppu/Lando had been seeded with aerosol emissions mainly comprises with black carbon and organic carbon, will be considered. Emission data that will be used is from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). The physical mechanism/s of intensification or deintensification of tropical cyclones will be determined after the seeding experiment analyses.

Keywords: aerosol, CCN, IN, tropical cylone

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Authors: Sylvester Akpah, Selasi Vondee

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Unmanned Aerial Vehicles (UAV) as they are also known, drones have attracted increasing attention in recent years due to their ubiquitous nature and boundless applications in the areas of communication, surveying, aerial photography, weather forecasting, medical delivery, surveillance amongst others. Operated remotely in real-time or pre-programmed, drones can fly autonomously or on pre-defined routes. The application of these aerial vehicles has successfully penetrated the world due to technological evolution, thus a lot more businesses are utilizing their capabilities. Unfortunately, while drones are replete with the benefits stated supra, they are riddled with some problems, mainly attributed to the complexities in learning how to master drone flights, collision avoidance and enterprise security. Additional challenges, such as the analysis of flight data recorded by sensors attached to the drone may take time and require expert help to analyse and understand. This paper presents an autonomous drone control system using a chatbot. The system allows for easy control of drones using conversations with the aid of Natural Language Processing, thus to reduce the workload needed to set up, deploy, control, and monitor drone flight missions. The results obtained at the end of the study revealed that the drone connected to the chatbot was able to initiate flight missions with just text and voice commands, enable conversation and give real-time feedback from data and requests made to the chatbot. The results further revealed that the system was able to process natural language and produced human-like conversational abilities using Artificial Intelligence (Natural Language Understanding). It is recommended that radio signal adapters be used instead of wireless connections thus to increase the range of communication with the aerial vehicle.

Keywords: artificial ntelligence, chatbot, natural language processing, unmanned aerial vehicle

Procedia PDF Downloads 119