Search results for: tree algorithms

Authors: Md. Wasiul Islam, Md. Jamius Shams Sowrov

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The Sundarbans is the largest single tract of mangrove forest in the world. This is located in the southwest corner of Bangladesh. This is a unique ecosystem which is a great breeding and nursing ground for a great biodiversity. It supports the livelihood of about 3.5 million coastal dwellers and also protects the coastal belt and inland areas from various natural calamities. Historically, the management of the Sundarbans was controlled by the Bangladesh Forest Department following top-down approach without the involvement of local communities. Such fence and fining-based blue-print approach was not effective to protect the forest which caused Sundarbans to degrade severely in the recent past. Fifty percent of the total tree cover has been lost in the last 30 years. Therefore, local multi-stakeholder based bottom-up co-management approach was introduced at some of the parts of the Sundarbans in 2006 to improve the biodiversity status by enhancing the protection level of the forest. Various co-management organizations were introduced under co-management approach where the local community people could actively involve in various activities related to the management and welfare of the Sundarbans including the decision-making process to achieve the goal. From this backdrop, the objective of the study was to assess the performance of co-management organizations to facilitate sustainable management of the Sundarbans mangrove forests. The qualitative study followed face-to-face interview to collect data using two sets of semi-structured questionnaires. A total of 40 respondents participated in the research that was from eight villagers under two forest ranges. 32 representatives from the local communities as well as 8 official representatives involved in co-management approach were interviewed using snowball sampling technique. The study shows that the co-management approach improved governance system of the Sundarbans through active participation of the local community people and their interactions with the officials via the platform of co-management organizations. It facilitated accountability and transparency system to some extent through following some formal and informal rules and regulations. It also improved the power structure of the management process by fostering local empowerment process particularly the women. Moreover, people were able to learn from their interactions with and within the co-management organizations as well as interventions improved environmental awareness and promoted social learning. The respondents considered good governance as the most important factor for achieving the goal of sustainable management and biodiversity conservation of the Sundarbans. The success of co-management planning process also depends on the active and functional participation of different stakeholders including the local communities where co-management organizations were considered as the most functional platform. However, the governance system was also facing various challenges which resulted in barriers to the sustainable management of the Sundarbans mangrove forest. But still there were some members involved in illegal forest operations and created obstacles against sustainable management of the Sundarbans. Respondents recommended greater patronization from the government, financial and logistic incentives for alternative income generation opportunities with effective participatory monitoring and evaluation system to improve sustainable management of the Sundarbans.

Keywords: Bangladesh, co-management approach, co-management organizations, governance, Sundarbans, sustainable management

Procedia PDF Downloads 157

Authors: Sana Hamdi, Emna Bouazizi, Sami Faiz

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In recent years, real-time spatial applications, like location-aware services and traffic monitoring, have become more and more important. Such applications result dynamic environments where data as well as queries are continuously moving. As a result, there is a tremendous amount of real-time spatial data generated every day. The growth of the data volume seems to outspeed the advance of our computing infrastructure. For instance, in real-time spatial Big Data, users expect to receive the results of each query within a short time period without holding in account the load of the system. But with a huge amount of real-time spatial data generated, the system performance degrades rapidly especially in overload situations. To solve this problem, we propose the use of data partitioning as an optimization technique. Traditional horizontal and vertical partitioning can increase the performance of the system and simplify data management. But they remain insufficient for real-time spatial Big data; they can’t deal with real-time and stream queries efficiently. Thus, in this paper, we propose a novel data partitioning approach for real-time spatial Big data named VPA-RTSBD (Vertical Partitioning Approach for Real-Time Spatial Big data). This contribution is an implementation of the Matching algorithm for traditional vertical partitioning. We find, firstly, the optimal attribute sequence by the use of Matching algorithm. Then, we propose a new cost model used for database partitioning, for keeping the data amount of each partition more balanced limit and for providing a parallel execution guarantees for the most frequent queries. VPA-RTSBD aims to obtain a real-time partitioning scheme and deals with stream data. It improves the performance of query execution by maximizing the degree of parallel execution. This affects QoS (Quality Of Service) improvement in real-time spatial Big Data especially with a huge volume of stream data. The performance of our contribution is evaluated via simulation experiments. The results show that the proposed algorithm is both efficient and scalable, and that it outperforms comparable algorithms.

Keywords: real-time spatial big data, quality of service, vertical partitioning, horizontal partitioning, matching algorithm, hamming distance, stream query

Procedia PDF Downloads 146

Authors: S. Mamoucha, N. Tsafantakis, Α. Ioannidis, S. Chatzipanagiotou, C. Nikolaou, L. Skaltsounis, N. Fokialakis, N. Christodoulakis

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Introduction: Plant secondary metabolites (SM) can be grouped into three chemically distinct groups: terpenes, phenolics, and nitrogen-containing compounds. For many years the adaptive significance of SM was unknown. They were thought to be functionless end-products. Currently it is accepted that many secondary metabolites (also known as natural products) have important ecological roles in plants. For instance, they serve as attractants (odor, color, taste) for pollinators and seed-dispersing animals. Moreover, they protect plants from herbivores, microbial pathogens and from environmental stress (high and low temperatures, drought, alkalinity, salinity, radiation etc). It is well known that both biotic and abiotic stress often increase the accumulation of SM. The local climatic conditions, seasonal changes, external factors such as light, temperature, humidity affect the biosynthesis and composition of secondary metabolites. A well known dioecious evergreen plant, Pistacia lentiscus L. (mastic tree), was selected in order to study the metabolic variations occur in response to the different climate conditions, due to the seasonal variation and its effect on the biosynthesis of bioactive compounds. Materials-methods: Young and mature leaves were collected in January and July 2014, dried and extracted by accelerated solvent extraction (Dionex ASE™ 350) using solvents of increased polarity (DCM, MeOH, and H2O). GC-MS and UHPLC-HRMS analysis were carried out in order to define the nature and the relative abundance of SM. The antibacterial activity was evaluated by using the Agar Disc Diffusion Assay against ATCC and clinical isolates strains: Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Candida albicans, Streptococcus mutans and Klebsiella pneumoniae. All tests were carried out in duplicate and the average radii of the inhibition zones were calculated for each extract. Results: According to the phytochemical profile obtained from each extract, the biosynthesis of SM varied both qualitatively and quantitatively under the two different types of seasonal stress. With exception of the biologically inactive nonpolar DCM extract of July, all extracts inhibited the growth of most of the investigated microorganisms. A clear positive correlation has been observed between the relative abundance of SM and the bioactivity of the DCM extracts of January and July. Observed changes during phytochemical analysis were mainly focused on the triterpenoid content. On the other hand, the bioactivity of the polar extracts (MeOH and H2O) of January and July resulted practically invariable against most of the microorganisms, besides the significant variation of the SM content due to the seasonal variation. Conclusion: Our results clearly confirmed the hypothesis of abiotic stress as an important regulating factor that significantly affects the biosynthesis of secondary metabolites and thus the presence of bioactive compounds. Acknowledgment: This work was supported by IKY - State Scholarship Foundation, Athens, Greece.

Keywords: antibacterial screening, phytochemical profile, Pistacia lentiscus, abiotic stress

Procedia PDF Downloads 232

Authors: Benbiao Song, Yan Gao, Zhuo Liu

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Geostatistical modeling is the key technic for reservoir characterization, the quality of geological models will influence the prediction of reservoir performance greatly, but few studies have been done to quantify the factors impacting geostatistical reservoir modeling accuracy. In this study, 16 fluvial prototype models have been established to represent different geological complexity, 6 cases range from 16 to 361 wells were defined to reproduce all those 16 prototype models by different methodologies including SIS, object-based and MPFS algorithms accompany with different constraint parameters. Modeling accuracy ratio was defined to quantify the influence of each factor, and ten realizations were averaged to represent each accuracy ratio under the same modeling condition and parameters association. Totally 5760 simulations were done to quantify the relative contribution of each factor to the simulation accuracy, and the results can be used as strategy guide for facies modeling in the similar condition. It is founded that data density, geological trend and geological complexity have great impact on modeling accuracy. Modeling accuracy may up to 90% when channel sand width reaches up to 1.5 times of well space under whatever condition by SIS and MPFS methods. When well density is low, the contribution of geological trend may increase the modeling accuracy from 40% to 70%, while the use of proper variogram may have very limited contribution for SIS method. It can be implied that when well data are dense enough to cover simple geobodies, few efforts were needed to construct an acceptable model, when geobodies are complex with insufficient data group, it is better to construct a set of robust geological trend than rely on a reliable variogram function. For object-based method, the modeling accuracy does not increase obviously as SIS method by the increase of data density, but kept rational appearance when data density is low. MPFS methods have the similar trend with SIS method, but the use of proper geological trend accompany with rational variogram may have better modeling accuracy than MPFS method. It implies that the geological modeling strategy for a real reservoir case needs to be optimized by evaluation of dataset, geological complexity, geological constraint information and the modeling objective.

Keywords: fluvial facies, geostatistics, geological trend, modeling strategy, modeling accuracy, variogram

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Authors: Nanadcha Sinjindawong

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This article discusses the design process of a community mall called Castle of The Mall-dern. The concept behind this mall is to combine elements of a medieval castle with modern architecture. The author aims to create a building that fits into the surroundings while also providing users with the vibes of the ancient era. The total area used for the mall is 4,000 square meters, with three floors. The first floor is 1,500 square meters, the second floor is 1,750 square meters, and the third floor is 750 square meters. Research Aim: The aim of this research is to design a community mall that sells ancient clothes and accessories, and to combine sustainable architectural design with the ideas of ancient architecture in an urban area with convenient transportation. Methodology: The research utilizes qualitative research methods in architectural design. The process begins with calculating the given area and dividing it into different zones. The author then sketches and draws the plan of each floor, adding the necessary rooms based on the floor areas mentioned earlier. The program "SketchUp" is used to create an online 3D model of the community mall, and a physical model is built for presentation purposes on A1 paper, explaining all the details. Findings: The result of this research is a community mall with various amenities. The first floor includes retail shops, clothing stores, a food center, and a service zone. Additionally, there is an indoor garden with a fountain and a tree for relaxation. The second and third floors feature a void in the middle, with a few stores, cafes, restaurants, and studios on the second floor. The third floor is home to the administration and security control room, as well as a community gathering area designed as a public library with a café inside. Theoretical Importance: This research contributes to the field of sustainable architectural design by combining ancient architectural ideas with modern elements. It showcases the potential for creating buildings that blend historical aesthetics with contemporary functionality. Data Collection and Analysis Procedures: The data for this research is collected through a combination of area calculation, sketching, and building a 3D model. The analysis involves evaluating the design based on the allocated area, zoning, and functional requirements for a community mall. Question Addressed: The research addresses the question of how to design a community mall with a theme of ancient Medieval and Victorian eras. It explores how to combine sustainable architectural design principles with historical aesthetics to create a functional and visually appealing space. Conclusion: In conclusion, this research successfully designs a community mall called “Castle of The Mall-dern” that incorporates elements of Medieval and Victorian architecture. The building encompasses various zones, including retail shops, restaurants, community gathering areas, and service zones. It also features an interior garden and a public library within the mall. The research contributes to the field of sustainable architectural design by showcasing the potential for combining ancient architectural ideas with modern elements in an urban setting.

Keywords: 3D model, community mall, modern architecture, medieval architecture

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Authors: Kottaridi Klimentia, Demopoulos Vasilis, Sidiropoulos Anastasios, Ihara Diego, Nikolaidis Vasileios, Antonopoulos Dimitrios

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Aflatoxins are highly poisonous and carcinogenic compounds produced by species of the genus Aspergillus spp. that can infect a variety of agricultural foods, including dried figs. Biological and environmental factors, such as population, pathogenicity, and aflatoxinogenic capacity of the strains, topography, soil, and climate parameters of the fig orchards, are believed to have a strong effect on aflatoxin levels. Existing methods for aflatoxin detection and measurement, such as high performance liquid chromatography (HPLC), and enzyme-linked immunosorbent assay (ELISA), can provide accurate results, but the procedures are usually time-consuming, sample-destructive, and expensive. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the health and financial impact of a contaminated crop. Consequently, there is interest in developing a tool that predicts aflatoxin levels based on topography and soil analysis data of fig orchards. This paper describes the development of a risk assessment tool for the contamination of aflatoxin on dried figs, based on the location and altitude of the fig orchards, the population of the fungus Aspergillus spp. in the soil, and soil parameters such as pH, saturation percentage (SP), electrical conductivity (EC), organic matter, particle size analysis (sand, silt, clay), the concentration of the exchangeable cations (Ca, Mg, K, Na), extractable P, and trace of elements (B, Fe, Mn, Zn and Cu), by employing machine learning methods. In particular, our proposed method integrates three machine learning techniques, i.e., dimensionality reduction on the original dataset (principal component analysis), metric learning (Mahalanobis metric for clustering), and k-nearest neighbors learning algorithm (KNN), into an enhanced model, with mean performance equal to 85% by terms of the Pearson correlation coefficient (PCC) between observed and predicted values.

Keywords: aflatoxins, Aspergillus spp., dried figs, k-nearest neighbors, machine learning, prediction

Procedia PDF Downloads 164

Authors: Tammeen Siraj, Wenxing Zhou, Terry Huang, Mohammad Al-Amin

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The high resolution inline inspection (ILI) tool is used extensively in the pipeline industry to identify, locate, and measure metal-loss corrosion anomalies on buried oil and gas steel pipelines. Corrosion anomalies may occur singly (i.e. individual anomalies) or as clusters (i.e. a colony of corrosion anomalies). Although the ILI technology has advanced immensely, there are measurement errors associated with the sizes of corrosion anomalies reported by ILI tools due limitations of the tools and associated sizing algorithms, and detection threshold of the tools (i.e. the minimum detectable feature dimension). Quantifying the measurement error in the ILI data is crucial for corrosion management and developing maintenance strategies that satisfy the safety and economic constraints. Studies on the measurement error associated with the length of the corrosion anomalies (in the longitudinal direction of the pipeline) has been scarcely reported in the literature and will be investigated in the present study. Limitations in the ILI tool and clustering process can sometimes cause clustering error, which is defined as the error introduced during the clustering process by including or excluding a single or group of anomalies in or from a cluster. Clustering error has been found to be one of the biggest contributory factors for relatively high uncertainties associated with ILI reported anomaly length. As such, this study focuses on developing a consistent and comprehensive framework to quantify the measurement errors in the ILI-reported anomaly length by comparing the ILI data and corresponding field measurements for individual and clustered corrosion anomalies. The analysis carried out in this study is based on the ILI and field measurement data for a set of anomalies collected from two segments of a buried natural gas pipeline currently in service in Alberta, Canada. Data analyses showed that the measurement error associated with the ILI-reported length of the anomalies without clustering error, denoted as Type I anomalies is markedly less than that for anomalies with clustering error, denoted as Type II anomalies. A methodology employing data mining techniques is further proposed to classify the Type I and Type II anomalies based on the ILI-reported corrosion anomaly information.

Keywords: clustered corrosion anomaly, corrosion anomaly assessment, corrosion anomaly length, individual corrosion anomaly, metal-loss corrosion, oil and gas steel pipeline

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Authors: Lukas Vierus, Thomas Schuster

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A general framework is examined for dynamic tensor field tomography within an inhomogeneous medium characterized by refraction and absorption, treated as an inverse source problem concerning the associated transport equation. Guided by Fermat’s principle, the Riemannian metric within the specified domain is determined by the medium's refractive index. While considerable literature exists on the inverse problem of reconstructing a tensor field from its longitudinal ray transform within a static Euclidean environment, limited inversion formulas and algorithms are available for general Riemannian metrics and time-varying tensor fields. It is established that tensor field tomography, akin to an inverse source problem for a transport equation, persists in dynamic scenarios. Framing dynamic tensor tomography as an inverse source problem embodies a comprehensive perspective within this domain. Ensuring well-defined forward mappings necessitates establishing existence and uniqueness for the underlying transport equations. However, the bilinear forms of the associated weak formulations fail to meet the coercivity condition. Consequently, recourse to viscosity solutions is taken, demonstrating their unique existence within suitable Sobolev spaces (in the static case) and Sobolev-Bochner spaces (in the dynamic case), under a specific assumption restricting variations in the refractive index. Notably, the adjoint problem can also be reformulated as a transport equation, with analogous results regarding uniqueness. Analytical solutions are expressed as integrals over geodesics, facilitating more efficient evaluation of forward and adjoint operators compared to solving partial differential equations. Certainly, here's the revised sentence in English: Numerical experiments are conducted using a Nesterov-accelerated Landweber method, encompassing various fields, absorption coefficients, and refractive indices, thereby illustrating the enhanced reconstruction achieved through this holistic modeling approach.

Keywords: attenuated refractive dynamic ray transform of tensor fields, geodesics, transport equation, viscosity solutions

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Authors: Elżbieta Turska

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Introduction: One of the most difficult times in a person’s life is adolescence. The experiences in this period may shape the future life of this person to a large extent. This is the reason why many young people experience sadness, dejection, hopelessness, sense of worthlessness, as well as losing interest in various activities and social relationships, all of which are often classified as mood disorders. As many as 15-40% adolescents experience depressed moods and for most of them they resolve and are not carried into adulthood. However, (5-6%) of those affected by mood disorders develop the depressive syndrome and as many as (1-3%) develop full-blown clinical depression. Materials: A large questionnaire was given to 2508 students, aged 13–16 years old, and one of its parts was the Burns checklist, i.e. the standard test for identifying depressed mood. The questionnaire asked about many aspects of the student’s life, it included a total of 53 questions, most of which had subquestions. It is important to note that the data suffered from many problems, the most important of which were missing data and collinearity. Aim: In order to identify the correlates of mood disorders we built predictive models which were then trained and validated. Our aim was not to be able to predict which students suffer from mood disorders but rather to explore the factors influencing mood disorders. Methods: The problems with data described above practically excluded using all classical statistical methods. For this reason, we attempted to use the following Artificial Intelligence (AI) methods: classification trees with surrogate variables, random forests and xgboost. All analyses were carried out with the use of the mlr package for the R programming language. Resuts: The predictive model built by classification trees algorithm outperformed the other algorithms by a large margin. As a result, we were able to rank the variables (questions and subquestions from the questionnaire) from the most to least influential as far as protection against mood disorder is concerned. Thirteen out of twenty most important variables reflect the relationships with parents. This seems to be a really significant result both from the cognitive point of view and also from the practical point of view, i.e. as far as interventions to correct mood disorders are concerned.

Keywords: mood disorders, adolescents, family, artificial intelligence

Procedia PDF Downloads 86

Authors: Selim M. Khan

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Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage and lung cancer and is a worsening issue in North American and European built environments, having increased over time within newer housing stocks as a function of as yet unclear variables. Indoor air radon concentration can be influenced by a wide range of environmental, structural, and behavioral factors. As some of these factors are quantitative while others are qualitative, no single statistical model can determine indoor radon level precisely while simultaneously considering all these variables across a complex and highly diverse dataset. The ability of AI- machine (deep) learning to simultaneously analyze multiple quantitative and qualitative features makes it suitable to predict radon with a high degree of precision. Using Canadian and Swedish long-term indoor air radon exposure data, we are using artificial deep neural network models with random weights and polynomial statistical models in MATLAB to assess and predict radon health risk to human as a function of geospatial, human behavioral, and built environmental metrics. Our initial artificial neural network with random weights model run by sigmoid activation tested different combinations of variables and showed the highest prediction accuracy (>96%) within the reasonable iterations. Here, we present details of these emerging methods and discuss strengths and weaknesses compared to the traditional artificial neural network and statistical methods commonly used to predict indoor air quality in different countries. We propose an artificial deep neural network with random weights as a highly effective method for assessing and predicting indoor radon.

Keywords: radon, radiation protection, lung cancer, aI-machine deep learnng, risk assessment, risk prediction, Europe, North America

Procedia PDF Downloads 81

Authors: Carolina Zambrana, Grover Zurita

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The food loss production that occurs in deficient agricultural production is one of the major problems worldwide. This puts the population's food security and the efficiency of farming investments at risk. It is to be expected that this food security will be achieved with the own and efficient production of each country. It will have an impact on the well-being of its population and, thus, also on food sovereignty. The production losses in quantity and quality occur due to the lack of efficient detection of diseases at an early stage. It is very difficult to solve the agriculture efficiency using traditional methods since it takes a long time to be carried out due to detection imprecision of the main diseases, especially when the production areas are extensive. Therefore, the main objective of this research study is to perform a systematic literature review, of the latest five years, of Precision Agriculture (PA) to be able to understand the state of the art of the set of new technologies, procedures, and optimization processes with Artificial Intelligence (AI). This study will focus on Corns, Potatoes, and Rice diagnostic diseases. The extensive literature review will be performed on Elsevier, Scopus, and IEEE databases. In addition, this research will focus on advanced digital imaging processing and the development of software and hardware for PA. The convolution neural network will be handling special attention due to its outstanding diagnostic results. Moreover, the studied data will be incorporated with artificial intelligence algorithms for the automatic diagnosis of crop quality. Finally, precision agriculture with technology applied to the agricultural sector allows the land to be exploited efficiently. This system requires sensors, drones, data acquisition cards, and global positioning systems. This research seeks to merge different areas of science, control engineering, electronics, digital image processing, and artificial intelligence for the development, in the near future, of a low-cost image measurement system that allows the optimization of crops with AI.

Keywords: precision agriculture, convolutional neural network, deep learning, artificial intelligence

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Authors: Rudolf Hrach, Stanislav Novak, Vera Hrachova

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Composite and nanocomposite films represent the class of promising materials and are often objects of the study due to their mechanical, electrical and other properties. The most interesting ones are probably the composite metal/dielectric structures consisting of a metal component embedded in an oxide or polymer matrix. Behaviour of composite films varies with the amount of the metal component inside what is called filling factor. The structures contain individual metal particles or nanoparticles completely insulated by the dielectric matrix for small filling factors and the films have more or less dielectric properties. The conductivity of the films increases with increasing filling factor and finally a transition into metallic state occurs. The behaviour of composite films near a percolation threshold, where the change of charge transport mechanism from a thermally-activated tunnelling between individual metal objects to an ohmic conductivity is observed, is especially important. Physical properties of composite films are given not only by the concentration of metal component but also by the spatial and size distributions of metal objects which are influenced by a technology used. In our contribution, a study of composite structures with the help of methods of computational physics was performed. The study consists of two parts: -Generation of simulated composite and nanocomposite films. The techniques based on hard-sphere or soft-sphere models as well as on atomic modelling are used here. Characterizations of prepared composite structures by image analysis of their sections or projections follow then. However, the analysis of various morphological methods must be performed as the standard algorithms based on the theory of mathematical morphology lose their sensitivity when applied to composite films. -The charge transport in the composites was studied by the kinetic Monte Carlo method as there is a close connection between structural and electric properties of composite and nanocomposite films. It was found that near the percolation threshold the paths of tunnel current forms so-called fuzzy clusters. The main aim of the present study was to establish the correlation between morphological properties of composites/nanocomposites and structures of conducting paths in them in the dependence on the technology of composite films.

Keywords: composite films, computer modelling, image analysis, nanocomposite films

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Authors: Thomai Voulgari, Vasilis Vasilopoulos, Antonis Skamnakis

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The aim of this study is to investigate the effect of the European election campaigns (May 23-26, 2019) on Twitter achieving with artificial intelligence tools such as troll factories and automated inauthentic accounts. Our research focuses on the last European Parliamentary elections that took place between 23 and 26 May 2019 specifically in Italy, Greece, Germany and France. It is difficult to estimate how many Twitter users are actually bots (Echeverría, 2017). Detection for fake accounts is becoming even more complicated as AI bots are made more advanced. A political bot can be programmed to post comments on a Twitter account for a political candidate, target journalists with manipulated content or engage with politicians and artificially increase their impact and popularity. We analyze variables related to 1) the scope of activity of automated bots accounts and 2) degree of coherence and 3) degree of interaction taking into account different factors, such as the type of content of Twitter messages and their intentions, as well as the spreading to the general public. For this purpose, we collected large volumes of Twitter accounts of party leaders and MEP candidates between 10th of May and 26th of July based on content analysis of tweets based on hashtags while using an innovative network analysis tool known as MediaWatch.io (https://mediawatch.io/). According to our findings, one of the highest percentage (64.6%) of automated “bot” accounts during 2019 European election campaigns was in Greece. In general terms, political bots aim to proliferation of misinformation on social media. Targeting voters is a way that it can be achieved contribute to social media manipulation. We found that political parties and individual politicians create and promote purposeful content on Twitter using algorithmic tools. Based on this analysis, online political advertising play an important role to the process of spreading misinformation during elections campaigns. Overall, inauthentic accounts and social media algorithms are being used to manipulate political behavior and public opinion.

Keywords: artificial intelligence tools, human-bot interactions, political manipulation, social networking, troll factories

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Authors: Lou J. Pino, Mark Campbell, Matthew J. Kennedy, Ashleigh C. Kennedy

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A concussion is complex and nuanced, with cognitive rest being a key component of recovery. Cognitive overexertion during rehabilitation from a concussion is associated with delayed recovery. However, daily living imposes cognitive demands that may be unavoidable and difficult to quantify. Therefore, a portable tool capable of alerting patients before cognitive overexertion occurs could allow patients to maintain their quality of life while preventing symptoms and recovery setbacks. EEG allows for a sensitive measure of cognitive exertion. Clinical 32-lead EEG headsets are not practical for day-to-day concussion rehabilitation management. However, there are now commercially available and affordable portable EEG headsets. Thus, these headsets can potentially be used to continuously monitor cognitive exertion during mental tasks to alert the wearer of overexertion, with the aim of preventing the occurrence of symptoms to speed recovery times. The objective of this study was to test an algorithm for predicting cognitive exertion from EEG data collected from a portable headset. EEG data were acquired from 10 participants (5 males, 5 females). Each participant wore a portable 4 channel EEG headband while completing 10 tasks: rest (eyes closed), rest (eyes open), three levels of the increasing difficulty of logic puzzles, three levels of increasing difficulty in multiplication questions, rest (eyes open), and rest (eyes closed). After each task, the participant was asked to report their perceived level of cognitive exertion using the NASA Task Load Index (TLX). Each participant then completed a second session on a different day. A customized machine learning model was created using data from the first session. The performance of each model was then tested using data from the second session. The mean correlation coefficient between TLX scores and predicted cognitive exertion was 0.75 ± 0.16. The results support the efficacy of the algorithm for predicting cognitive exertion. This demonstrates that the algorithms developed in this study used with portable EEG devices have the potential to aid in the concussion recovery process by monitoring and warning patients of cognitive overexertion. Preventing cognitive overexertion during recovery may reduce the number of symptoms a patient experiences and may help speed the recovery process.

Keywords: cognitive activity, EEG, machine learning, personalized recovery

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Authors: Mehrnoosh Abouzari, Shahrokh Sahraei

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Today, with the increase in quantity and quality and variety of crimes, the discussion of crime prevention has faced a serious challenge that human resources alone and with traditional methods will not be effective. One of the developments in the modern world is the presence of artificial intelligence in various fields, including criminal law. In fact, the use of artificial intelligence in criminal investigations and fighting crime is a necessity in today's world. The use of artificial intelligence is far beyond and even separate from other technologies in the struggle against crime. Second, its application in criminal science is different from the discussion of prevention and it comes to the prediction of crime. Crime prevention in terms of the three factors of the offender, the offender and the victim, following a change in the conditions of the three factors, based on the perception of the criminal being wise, and therefore increasing the cost and risk of crime for him in order to desist from delinquency or to make the victim aware of self-care and possibility of exposing him to danger or making it difficult to commit crimes. While the presence of artificial intelligence in the field of combating crime and social damage and dangers, like an all-seeing eye, regardless of time and place, it sees the future and predicts the occurrence of a possible crime, thus prevent the occurrence of crimes. The purpose of this article is to collect and analyze the studies conducted on the use of artificial intelligence in predicting and preventing crime. How capable is this technology in predicting crime and preventing it? The results have shown that the artificial intelligence technologies in use are capable of predicting and preventing crime and can find patterns in the data set. find large ones in a much more efficient way than humans. In crime prediction and prevention, the term artificial intelligence can be used to refer to the increasing use of technologies that apply algorithms to large sets of data to assist or replace police. The use of artificial intelligence in our debate is in predicting and preventing crime, including predicting the time and place of future criminal activities, effective identification of patterns and accurate prediction of future behavior through data mining, machine learning and deep learning, and data analysis, and also the use of neural networks. Because the knowledge of criminologists can provide insight into risk factors for criminal behavior, among other issues, computer scientists can match this knowledge with the datasets that artificial intelligence uses to inform them.

Keywords: artificial intelligence, criminology, crime, prevention, prediction

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Authors: Yasir E. Mohieldeen

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Qatar is one of the most water scarce countries in the World. In 2014, the average per capita rainfall was less than 29 m3/y/ca, while the global average is 6,000 m3/y/ca. However, the per capita water consumption in Qatar is among the highest in the World: more than 500 liters per person per day, whereas the global average is 160 liters per person per day. Since the early 2000s, Qatar has been relying heavily on desalinated water from the Arabian Gulf as the main source of fresh water. In 2009, about 99.9% of the total potable water produced was desalinated. Reliance on desalinated water makes Qatar very vulnerable to water related natural disasters, such as the red-tide phenomenon. Qatar’s strategic water reserve lasts for only 7 days. In case of red-tide outbreak, the country would not be able to desalinate water for days, let alone the months that this disaster would bring about (as it clogs the desalination equipment). The 2008-09 red-tide outbreak, for instance, lasted for more than eight months and forced the closure of desalination plants in the region for weeks. This study aims at identifying favorite conditions for red-tide outbreaks, using satellite data along with in-situ measurements. This identification would allow the prediction of these outbreaks and their hotspots. Prediction and monitoring of outbreaks are crucial to water security in the country, as different measures could be put in place in advance to prevent an outbreak and mitigate its impact if it happened. Red-tide outbreaks are detected using different algorithms for chlorophyll concentration in the Gulf waters. Vegetation indices, such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were used along with Surface Algae Bloom Index (SABI) to detect known outbreaks. MODIS (or Moderate Resolution Imaging Spectroradiometer) bands are used to calculate these indices. A red-tide outbreaks atlas in the Arabian Gulf is being produced. Prediction of red-tide outbreaks ahead of their occurrences would give critical information on possible water-shortage in the country. Detecting known outbreaks in the past few decades and related parameters (e.g. water salinity, water surface temperature, nutrition, sandstorms, … etc) enables the identification of favorite conditions of red-tide outbreak that are key to the prediction of these outbreaks.

Keywords: Arabian Gulf, MODIS, red-tide detection, strategic water reserve, water desalination

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Authors: R. G. Ariyawansa, M. A. N. R. M. Perera

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“House” is a powerful symbol of socio-economic background of individuals and families. In fact, housing provides all types of needs/wants from basic needs to self-actualization needs. This phenomenon can be realized only having analyzed hidden motives of buyers and sellers of the housing market. Hence, the aim of this study is to examine the socio-economic insight of the secondary housing market in Colombo suburbs. This broader aim was achieved via analyzing the general pattern of the secondary housing market, identifying socio-economic motives of sellers of the secondary housing market, and reviewing sellers’ experience of buyer behavior. A purposive sample of 50 sellers from popular residential areas in Colombo such as Maharagama, Kottawa, Piliyandala, Punnipitiya, and Nugegoda was used to collect primary data instead of relevant secondary data from published and unpublished reports. The sample was limited to selling price ranging from Rs15 million to Rs25 million, which apparently falls into middle and upper-middle income houses in the context. Participatory observation and semi-structured interviews were adopted as key data collection tools. Data were descriptively analyzed. This study found that the market is mainly handled by informal agents who are unqualified and unorganized. People such as taxi/tree-wheel drivers, boutique venders, security personals etc. are engaged in housing brokerage as a part time career. Few fulltime and formally organized agents were found but they were also not professionally qualified. As far as housing quality is concerned, it was observed that 90% of houses was poorly maintained and illegally modified. They are situated in poorly maintained neighborhoods as well. Among the observed houses, 2% was moderately maintained and 8% was well maintained and modified. Major socio-economic motives of sellers were “migrating foreign countries for education and employment” (80% and 10% respectively), “family problems” (4%), and “social status” (3%). Other motives were “health” and “environmental/neighborhood problems” (3%). This study further noted that the secondary middle income housing market in the area directly related with the migrants who motivated for education in foreign countries, mainly Australia, UK and USA. As per the literature, families motivated for education tend to migrate Colombo suburbs from remote areas of the country. They are seeking temporary accommodation in lower middle income housing. However, the secondary middle income housing market relates with the migration from Colombo to major global cities. Therefore, final transaction price of this market may depend on migration related dates such as university deadlines, visa and other agreements. Hence, it creates a buyers’ market lowering the selling price. Also it was revealed that the buyers tend to trust more on this market as far as the quality of construction of houses is concerned than brand new houses which are built for selling purpose.

Keywords: informal housing market, hidden motives of buyers and sellers, secondary housing market, socio-economic insight

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Authors: Filip Rusak, Yulia Arzhaeva, Dadong Wang

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Machine learning based Computer-Aided Diagnosis (CAD) is gaining much popularity in medical imaging and diagnostic radiology. However, it requires a large amount of high quality and labeled training image datasets. The training images may come from different sources and be acquired from different radiography machines produced by different manufacturers, digital or digitized copies of film radiographs, with various sizes as well as different pixel intensity distributions. In this paper, a content-aware image augmentation method is presented to deal with these variations. The results of the proposed method have been validated graphically by plotting the removed and added seams of pixels on original images. Two different chest X-ray (CXR) datasets are used in the experiments. The CXRs in the datasets defer in size, some are digital CXRs while the others are digitized from analog CXR films. With the proposed content-aware augmentation method, the Seam Carving algorithm is employed to resize CXRs and the corresponding labels in the form of image masks, followed by histogram matching used to normalize the pixel intensities of digital radiography, based on the pixel intensity values of digitized radiographs. We implemented the algorithms, resized the well-known Montgomery dataset, to the size of the most frequently used Japanese Society of Radiological Technology (JSRT) dataset and normalized our digital CXRs for testing. This work resulted in the unified off-the-shelf CXR dataset composed of radiographs included in both, Montgomery and JSRT datasets. The experimental results show that even though the amount of augmentation is large, our algorithm can preserve the important information in lung fields, local structures, and global visual effect adequately. The proposed method can be used to augment training and testing image data sets so that the trained machine learning model can be used to process CXRs from various sources, and it can be potentially used broadly in any medical imaging applications.

Keywords: computer-aided diagnosis, image augmentation, lung segmentation, medical imaging, seam carving

Procedia PDF Downloads 195

Authors: V. Markogianni, D. Kalivas, G. Petropoulos, E. Dimitriou

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Lake water quality monitoring in combination with the use of earth observation products constitutes a major component in many water quality monitoring programs. Landsat 8 images of Trichonis Lake (Greece) acquired on 30/10/2013 and 30/08/2014 were used in order to explore the possibility of Landsat 8 to estimate water quality parameters and particularly CDOM absorption at specific wavelengths, chlorophyll-a and nutrient concentrations in this oligotrophic freshwater body, characterized by inexistent quantitative, temporal and spatial variability. Water samples have been collected at 22 different stations, on late August of 2014 and the satellite image of the same date was used to statistically correlate the in-situ measurements with various combinations of Landsat 8 bands in order to develop algorithms that best describe those relationships and calculate accurately the aforementioned water quality components. Optimal models were applied to the image of late October of 2013 and the validation of the results was conducted through their comparison with the respective available in-situ data of 2013. Initial results indicated the limited ability of the Landsat 8 sensor to accurately estimate water quality components in an oligotrophic waterbody. As resulted by the validation process, ammonium concentrations were proved to be the most accurately estimated component (R = 0.7), followed by chl-a concentration (R = 0.5) and the CDOM absorption at 420 nm (R = 0.3). In-situ nitrate, nitrite, phosphate and total nitrogen concentrations of 2014 were measured as lower than the detection limit of the instrument used, hence no statistical elaboration was conducted. On the other hand, multiple linear regression among reflectance measures and total phosphorus concentrations resulted in low and statistical insignificant correlations. Our results were concurrent with other studies in international literature, indicating that estimations for eutrophic and mesotrophic lakes are more accurate than oligotrophic, owing to the lack of suspended particles that are detectable by satellite sensors. Nevertheless, although those predictive models, developed and applied to Trichonis oligotrophic lake are less accurate, may still be useful indicators of its water quality deterioration.

Keywords: landsat 8, oligotrophic lake, remote sensing, water quality

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Authors: Arsyam Mawardi, Sony Suhandono, Azzania Fibriani, Fifi Fitriyah Masduki

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Malaria is an infectious disease caused by Plasmodium sp. This disease has a high prevalence in Indonesia, especially in Jayapura. The vaccine that is currently being developed has not been effective in overcoming malaria. This is due to the high polymorphism in the Plasmodium genome especially in areas that encode Plasmodium surface proteins. Merozoite Surface Protein 1 (MSP1) Plasmodium falciparum is a surface protein that plays a role in the invasion process in human erythrocytes through the interaction of Glycophorin A protein receptors and sialic acid in erythrocytes with Reticulocyte Binding Proteins (RBP) and Duffy Adhesion Protein (DAP) ligands in merozoites. MSP1 can be targeted to be a specific antigen and predicted epitope area which will be used for the development of diagnostic and malaria vaccine therapy. MSP1 consists of 17 blocks, each block is dimorphic, and has been marked as the K1 and MAD20 alleles. Exceptions only in block 2, because it has 3 alleles, among others K1, MAD20 and RO33. These polymorphisms cause allelic variations and implicate the severity of patients infected P. falciparum. In addition, polymorphism of MSP1 in Jayapura isolates has not been reported so it is interesting to be further identified and projected as a specific antigen. Therefore, in this study, we analyzed the allele polymorphism as well as detected the MSP1 epitope antigen candidate on block 2 P. falciparum. Clinical samples of selected malaria patients followed the consecutive sampling method, examining malaria parasites with blood preparations on glass objects observed through a microscope. Plasmodium DNA was isolated from the blood of malarial positive patients. The block 2 MSP1 gene was amplified using PCR method and cloned using the pGEM-T easy vector then transformed to TOP'10 E.coli. Positive colonies selection was performed with blue-white screening. The existence of target DNA was confirmed by PCR colonies and DNA sequencing methods. Furthermore, DNA sequence analysis was done through alignment and formation of a phylogenetic tree using MEGA 6 software and insilico analysis using IEDB software to predict epitope candidate for P. falciparum. A total of 15 patient samples have been isolated from Plasmodium DNA. PCR amplification results show the target gene size about ± 1049 bp. The results of MSP1 nucleotide alignment analysis reveal that block 2 MSP1 genes derived from the sample of malarial patients were distributed in four different allele family groups, K1 (7), MAD20 (1), RO33 (0) and MSP1_Jayapura (10) alleles. The most commonly appears of the detected allele is MSP1_Jayapura single allele. There was no significant association between sex variables, age, the density of parasitemia and alel variation (Mann Whitney, U > 0.05), while symptomatic signs have a significant difference as a trigger of detectable allele variation (U < 0.05). In this research, insilico study shows that there is a new epitope antigen candidate from the MSP1_Jayapura allele and it is predicted to be recognized by B cells with 17 amino acid lengths in the amino acid sequence 187 to 203.

Keywords: epitope candidate, insilico analysis, MSP1 P. falciparum, polymorphism

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Authors: Mokhlisur Rahman

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Ocean inhibits one of the most complex ecosystems on the planet that regulates the earth's climate and weather by providing us with compatible weather to live. Ocean provides food by extending various ways of lifestyles that are dependent on it, transportation by accommodating the world's biggest carriers, recreation by offering its beauty in many moods, and home to countless species. At the essence of receiving various forms of entertainment, consumers choose to be close to the ocean while performing many fun activities. Which, at some point, upsets the stomach of the ocean by threatening marine life and the environment. Consumers throw the waste into the ocean after using it. Most of them are plastics that float over the ocean and turn into thousands of micro pieces that are hard to observe with the naked eye but easily eaten by the sea species. Eventually, that conflicts with the natural consumption process of any living species, making them sick. This information is not known by most consumers who go to the sea or seashores occasionally to spend time, nor is it widely discussed, which creates an information gap among consumers. However, advertising is a powerful tool to educate people about ocean pollution. This abstract analyzes three major ocean-saving advertisement campaigns that use innovative and advanced technology to get maximum exposure. The study collects data from the selected campaigns' websites and retrieves all available content related to messages, videos, and images. First, the SeaLegacy campaign uses stunning images to create awareness among the people; they use social media content, videos, and other educational content. They create content and strategies to build an emotional connection among the consumers that encourage them to move on an action. All the messages in their campaign empower consumers by using powerful words. Second, Ocean Conservancy Campaign uses social media marketing, events, and educational content to protect the ocean from various pollutants, including plastics, climate change, and overfishing. They use powerful images and videos of marine life. Their mission is to create evidence-based solutions toward a healthy ocean. Their message includes the message regarding the local communities along with the sea species. Third, ocean clean-up is a campaign that applies strategies using innovative technologies to remove plastic waste from the ocean. They use social media, digital, and email marketing to reach people and raise awareness. They also use images and videos to evoke an emotional response to take action. These tree advertisements use realistic images, powerful words, and the presence of living species in the imagery presentation, which are eye-catching and can grow emotional connection among the consumers. Identifying the effectiveness of the messages these advertisements carry and their strategies highlights the knowledge gap of mass people between real pollution and its consequences, making the message more accessible to the mass of people. This study aims to provide insights into the effectiveness of ocean-saving advertisement campaigns and their impact on the public's awareness of ocean conservation. The findings from this study help shape future campaigns.

Keywords: advertising-campaign, content-creation, images ocean-saving technology, videos

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Authors: Sujoy Khan

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There is a rising concern with increasing allergies affecting both adults and children in rural and urban India. Recent report on adults in a densely populated North Indian city showed sensitization rates for house dust mite, parthenium, and cockroach at 60%, 40% and 18.75% that is now comparable to allergy prevalence in cities in the United States. Data from patients residing in the eastern part of India is scarce. A retrospective study (over 2 years) was done on patients with allergic rhinitis and asthma where allergen-specific IgE levels were measured to see the aero-allergen sensitization pattern in a large metropolitan city of East India. Total IgE and allergen-specific IgE levels were measured using ImmunoCAP (Phadia 100, Thermo Fisher Scientific, Sweden) using region-specific aeroallergens: Dermatophagoides pteronyssinus (d1); Dermatophagoides farinae (d2); cockroach (i206); grass pollen mix (gx2) consisted of Cynodon dactylon, Lolium perenne, Phleum pratense, Poa pratensis, Sorghum halepense, Paspalum notatum; tree pollen mix (tx3) consisted of Juniperus sabinoides, Quercus alba, Ulmus americana, Populus deltoides, Prosopis juliflora; food mix 1 (fx1) consisted of Peanut, Hazel nut, Brazil nut, Almond, Coconut; mould mix (mx1) consisted of Penicillium chrysogenum, Cladosporium herbarum, Aspergillus fumigatus, Alternaria alternate; animal dander mix (ex1) consisted of cat, dog, cow and horse dander; and weed mix (wx1) consists of Ambrosia elatior, Artemisia vulgaris, Plantago lanceolata, Chenopodium album, Salsola kali, following manufacturer’s instructions. As the IgE levels were not uniformly distributed, median values were used to represent the data. 92 patients with allergic rhinitis and asthma (united airways disease) were studied over 2 years including 21 children (age < 12 years) who had total IgE and allergen-specific IgE levels measured. The median IgE level was higher in 2016 than in 2015 with 60% of patients (adults and children) being sensitized to house dust mite (dual positivity for Dermatophagoides pteronyssinus and farinae). Of 11 children in 2015, whose total IgE ranged from 16.5 to >5000 kU/L, 36% of children were polysensitized (≥4 allergens), and 55% were sensitized to dust mites. Of 10 children in 2016, total IgE levels ranged from 37.5 to 2628 kU/L, and 20% were polysensitized with 60% sensitized to dust mites. Mould sensitivity was 10% in both of the years in the children studied. A consistent finding was that ragweed sensitization (molecular homology to Parthenium hysterophorus) appeared to be increasing across all age groups, and throughout the year, as reported previously by us where 25% of patients were sensitized. In the study sample overall, sensitizations to dust mite, cockroach, and parthenium were important risks in our patients with moderate to severe asthma that reinforces the importance of controlling indoor exposure to these allergens. Sensitizations to dust mite, cockroach and parthenium allergens are important predictors of asthma morbidity not only among children but also among adults in Eastern India.

Keywords: aAeroallergens, asthma, dust mite, parthenium, rhinitis

Procedia PDF Downloads 180

Authors: Zafer Yüce, Paşa Yayla, Alev Taşkın

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There are heaps of analytical methods to estimate the crack growth life of a component. Soft computing methods have an increasing trend in predicting fatigue life. Their ability to build complex relationships and capability to handle huge amounts of data are motivating researchers and industry professionals to employ them for challenging problems. This study focuses on soft computing methods, especially random forest regressors and artificial neural networks with hyperparameter optimization algorithms such as grid search and random grid search, to estimate the crack growth life of an aircraft wing splice joint under variable amplitude loading. TensorFlow and Scikit-learn libraries of Python are used to build the machine learning models for this study. The material considered in this work is 7050-T7451 aluminum, which is commonly preferred as a structural element in the aerospace industry, and regarding the crack type; corner crack is used. A finite element model is built for the joint to calculate fastener loads and stresses on the structure. Since finite element model results are validated with analytical calculations, findings of the finite element model are fed to AFGROW software to calculate analytical crack growth lives. Based on Fighter Aircraft Loading Standard for Fatigue (FALSTAFF), 90 unique fatigue loading spectra are developed for various load levels, and then, these spectrums are utilized as inputs to the artificial neural network and random forest regression models for predicting crack growth life. Finally, the crack growth life predictions of the machine learning models are compared with analytical calculations. According to the findings, a good correlation is observed between analytical and predicted crack growth lives.

Keywords: aircraft, fatigue, joint, life, optimization, prediction.

Procedia PDF Downloads 152

Authors: Hung-Yuan Chang, Wen-Lung Chiang, Kuo-Liang Wu, Chen-Tsung Lin

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National Space Organization (NSPO) has completed in 2014 the development of a space-borne GPS receiver, including design, manufacture, comprehensive functional test, environmental qualification test and so on. The main performance of this receiver include 8-meter positioning accuracy, 0.05 m/sec speed-accuracy, the longest 90 seconds of cold start time, and up to 15g high dynamic scenario. The receiver will be integrated in the autonomous FORMOSAT-7 NSPO-Built satellite scheduled to be launched in 2019 to execute pre-defined scientific missions. The flight model of this receiver manufactured in early 2015 will pass comprehensive functional tests and environmental acceptance tests, etc., which are expected to be completed by the end of 2015. The space-borne GPS receiver is a pure software design in which all GPS baseband signal processing are executed by a digital signal processor (DSP), currently only 50% of its throughput being used. In response to the booming global navigation satellite systems, NSPO will gradually expand this receiver to become a multi-mode, multi-band, high-precision navigation receiver, and even a science payload, such as the reflectometry receiver of a global navigation satellite system. The fundamental purpose of this extension study is to port some software algorithms such as signal acquisition and correlation, reused code and large amount of computation load to the FPGA whose processor is responsible for operational control, navigation solution, and orbit propagation and so on. Due to the development and evolution of the FPGA is pretty fast, the new system architecture upgraded via an FPGA should be able to achieve the goal of being a multi-mode, multi-band high-precision navigation receiver, or scientific receiver. Finally, the results of tests show that the new system architecture not only retains the original overall performance, but also sets aside more resources available for future expansion possibility. This paper will explain the detailed DSP/FPGA architecture, development, test results, and the goals of next development stage of this receiver.

Keywords: space-borne, GPS receiver, DSP, FPGA, multi-mode multi-band

Procedia PDF Downloads 354

Authors: Roberto Cabezas H

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The current work shows the methodology developed to create narrative lighting spaces for the multimedia performance piece 'cluster: the vanished paradise.' This empirical research is focused on exploring unconventional roles for machines in subjective creative processes, by delving into the semantics of data and machine intelligence algorithms in hybrid technological, creative contexts to expand epistemic domains trough human-machine cooperation. The creative process in scenic and performing arts is guided mostly by intuition; from that idea, we developed an approach to embed collective intuition in computational creative systems, by joining the properties of Generative Adversarial Networks (GAN’s) and Fuzzy Clustering based on a semi-supervised data creation and analysis pipeline. The model makes use of GAN’s to learn from phenomenological data (data generated from experience with lighting scenography) and algorithmic design data (augmented data by procedural design methods), fuzzy logic clustering is then applied to artificially created data from GAN’s to define narrative transitions built on membership index; this process allowed for the creation of simple and complex spaces with expressive capabilities based on position and light intensity as the parameters to guide the narrative. Hybridization comes not only from the human-machine symbiosis but also on the integration of different techniques for the implementation of the aided design system. Machine intelligence tools as proposed in this work are well suited to redefine collaborative creation by learning to express and expand a conglomerate of ideas and a wide range of opinions for the creation of sensory experiences. We found in GAN’s and Fuzzy Logic an ideal tool to develop new computational models based on interaction, learning, emotion and imagination to expand the traditional algorithmic model of computation.

Keywords: fuzzy clustering, generative adversarial networks, human-machine cooperation, hybrid collective data, multimedia performance

Procedia PDF Downloads 127

Authors: Bhupendra Kumar Poudel, Sadhana Amatya, Tirtha Maiya Shrestha, Bharatmani Pokhrel, Mohan Prasad Amatya

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Background: H. salicifolia is a dense, branched, multipurpose, deciduous, nitrogen fixing, thorny willow-like small to moderate tree, restricted to the Himalaya. Among the two species of Nepal (Hippophae salicifolia and H. tibetana), it has been traditionally used as food additive, anticancer (bark), and treating toothache, tooth inflammation (anti-inflammatory) and radiation injury; while people of Western Nepal have largely undermined its veiled treasure by using it for fuel, wood and soil stabilization only. Therefore, the main objective of this study was to explore biological properties (analgesic, antidiabetic, cytotoxic and anti-inflammatory properties of this plant. Methodology: The transverse section of leaves and stems were viewed under microscope. Extracts obtained from soxhlation subjected to tests for phytochemical and biological studies. Rats (used to study antidiabetic and anti-inflammatory properties) and mice (used to study analgesic, CNS depressant, muscle relaxant and locomotor properties) were assumed to be normally distributed; then ANOVA and post hoc tukey test was used to find significance. The data obtained were analyzed by SPSS 17 and Excel 2007. Results and Conclusion: Pharmacognostical analysis revealed the presence of long stellate trichomes, double layered vascular bundle 5-6 in number and double layered compact sclerenchyma. The preliminary phytochemical screening of the extracts was found to exhibit the positive reaction tests for glycoside, steroid, tannin, flavonoid, saponin, coumarin and reducing sugar. The brine shrimp lethality bioassay tested in 1000, 100 and 10 ppm revealed cytotoxic activity inherent in methanol, water, chloroform and ethyl acetate extracts with LC50 (μg/ml) values of 61.42, 99.77, 292.72 and 277.84 respectively. The cytotoxic activity may be due to presence of tannins in the constituents. Antimicrobial screening of the extracts by cup diffusion method using Staphylococcus aereus, Escherichia coli and Pseudomonas aeruginosa against standard antibiotics (oxacillin, gentamycin and amikacin respectively) portrayed no activity against the microorganisms tested. The methanol extract of the stems and leaves showed various pharmacological properties: and antidiabetic, anti-inflammatory, analgesic [chemical writhing method], CNS depressant, muscle relaxant and locomotor activities in a dose-dependent fashion, indicating the possibility of the presence of different constituents in the stems and leaves responsible for these biological activities. All the effects when analyzed by post hoc tukey test were found to be significant at 95% confidence level. The antidiabetic activity was presumed to be due to flavonoids present in extract. Therefore, it can be concluded that this plant’s secondary metabolites possessed strong antidiabetic, anti-inflammatory and cytotoxic activity which could be isolated for further investigation.

Keywords: Hippophae salicifolia, constituents, antidiabetic, inflammatory, brine shrimp

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Authors: Khin Nyunt Yee, Mu Mu Thein

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Detection survey of mango seed and Pulp weevils was undertaken at major mango production areas, Yat Sauk, Taunggyi, Nyaung Shwe and Hopong Townships, in Shan State (South) of Myanmar on two mango cultivars of Sein Ta Lone and Yinkwe from May to August 2016 to coincide with fruiting season to conduct a survey of mango seed and pulp weevils population. The total numbers of 6300 fruits of both mango cultivars were sampled. Among them, 2900 fruits from 5674 fruit bearing plants were collected for Sein Ta Lone cultivar of five well managed, one unmanaged orchards and Urban in Yatsauk Twonship, 400 fruits from only one well managed orchard in Taunggyi Township, 400 fruits from two managed orchards in Nyaung Shwe Township and 400 fruits from one managed orchard in Hopong Township from May to June. 2200 fruits were collected from 4043 fruit bearing plants for Yinkwe Cultivar of four well managed orchards, one unmanaged orchards and one wild tree only in Yat Sauk Township from July to August, 2016. Fruit sample size was 200 fruits /orchard, / wild or /volunteer trees as minimum number. The pulps of all randomly sampling fruits were longitudinal cut open into three slices on each side of fruit and seed were cut longitudinally to inspect the presence of mango weevils. The collected weevils were identified up to species level at Plant Quarantine Laboratory, Plant Protection Division, Department of Agriculture, Ministry of Agriculture, Livestock and Irrigation, Yangon, Myanmar. Mango Pulp and Seed weevils were found on Sein Ta Lone Mango Cultivar in three out of four surveyed Townships except Hopong with the level of infestation ranged from 0.0% to 3.5% of fruits per Township with 0.0% to 39.0% of fruits per orchard. The highest infestation rate per township was 3.5% of fruits (n=400 fruits) in Nyaung Shwe, then, at Yat Suak, the rate was 2.47% (n=2900 fruits). A well-managed orchard at Taung Gyi had 0.75% (n=400 fruits) whereas Hopong was free 0.0% (n=400). The weevils were also recorded on Yinkwe Mango Cultivar in Yatsauk Township where the infestation level was 12.63% of fruits (n=2200) with 0.0% to 67.0% of fruits per orchard. This high level of infestation was obtained by including an absolutely non Integrated Pest Management (non IPM) orchards in both survey with the infestation rates 63.0% of fruits (n=200) and 67.0% of fruits (n=200) respectively on Yinkwe cultivar. Two different species; mango pulp weevil, Sternochetus frigitus, and mango seed weevil Sternochetus olivieri (Faust) of family Curculionidae under the order Coleoptera were recorded. Sternochetus mangiferae was not found during these surveys. Three different developmental stages of mango seed and pulp weevils: larva, pupa and adult were first detected since the first survey in 3rd week of May and mostly were recorded as adult stages in the following surveys in June, July and August The number of Mango pulp weevil was statistically higher than that of mango seed weevils at P < 0.001%. More precise surveys should be carried out national wide to detect the mango weevils.

Keywords: mango pulp weevil, Sternochetus frigitus, mango seed weevil Sternochetus olivieri, faust, Sternochetus mangiferae, fabricius, Sein Ta Lone, Yinkwe mango cultivars, Shan State (South) Myanmar

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Authors: Philipp Galkin

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Government policy is a critical factor in the understanding of energy markets. Regardless, it is rarely approached systematically from a research perspective. Gaining a precise understanding of what policies exist, their intended outcomes, geographical extent, duration, evolution, etc. would enable the research community to answer a variety of questions that, for now, are either oversimplified or ignored. Policy, on its surface, also seems a rather unstructured and qualitative undertaking. There may be quantitative components, but incorporating the concept of policy analysis into quantitative analysis remains a challenge. The KAPSARC Energy Policy Database (KEPD) is intended to address these two energy policy research limitations. Our approach is to represent policies within a quantitative library of the specific policy measures contained within a set of legal documents. Each of these measures is recorded into the database as a single entry characterized by a set of qualitative and quantitative attributes. Initially, we have focused on the major laws at the national level that regulate coal in China. However, KAPSARC is engaged in various efforts to apply this methodology to other energy policy domains. To ensure scalability and sustainability of our project, we are exploring semantic processing using automated computer algorithms. Automated coding can provide a more convenient input data for human coders and serve as a quality control option. Our initial findings suggest that the methodology utilized in KEPD could be applied to any set of energy policies. It also provides a convenient tool to facilitate understanding in the energy policy realm enabling the researcher to quickly identify, summarize, and digest policy documents and specific policy measures. The KEPD captures a wide range of information about each individual policy contained within a single policy document. This enables a variety of analyses, such as structural comparison of policy documents, tracing policy evolution, stakeholder analysis, and exploring interdependencies of policies and their attributes with exogenous datasets using statistical tools. The usability and broad range of research implications suggest a need for the continued expansion of the KEPD to encompass a larger scope of policy documents across geographies and energy sectors.

Keywords: China, energy policy, policy analysis, policy database

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

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In the field of veterinary medicine, there is a growing application of artificial intelligence (AI) for diagnosing bovine mastitis, a prevalent inflammatory disease in dairy cattle. AI technologies, such as automated milking systems, have streamlined the assessment of key metrics crucial for managing cow health during milking and identifying prevalent diseases, including mastitis. These automated milking systems empower farmers to implement automatic mastitis detection by analyzing indicators like milk yield, electrical conductivity, fat, protein, lactose, blood content in the milk, and milk flow rate. Furthermore, reports highlight the integration of somatic cell count (SCC), thermal infrared thermography, and diverse systems utilizing statistical models and machine learning techniques, including artificial neural networks, to enhance the overall efficiency and accuracy of mastitis detection. According to a review of 15 publications, machine learning technology can predict the risk and detect mastitis in cattle with an accuracy ranging from 87.62% to 98.10% and sensitivity and specificity ranging from 84.62% to 99.4% and 81.25% to 98.8%, respectively. Additionally, machine learning algorithms and microarray meta-analysis are utilized to identify mastitis genes in dairy cattle, providing insights into the underlying functional modules of mastitis disease. Moreover, AI applications can assist in developing predictive models that anticipate the likelihood of mastitis outbreaks based on factors such as environmental conditions, herd management practices, and animal health history. This proactive approach supports farmers in implementing preventive measures and optimizing herd health. By harnessing the power of artificial intelligence, the diagnosis of bovine mastitis can be significantly improved, enabling more effective management strategies and ultimately enhancing the health and productivity of dairy cattle. The integration of artificial intelligence presents valuable opportunities for the precise and early detection of mastitis, providing substantial benefits to the dairy industry.

Keywords: artificial insemination, automatic milking system, cattle, machine learning, mastitis

Procedia PDF Downloads 43

Authors: Anudeep Appe, Bhanu Poluparthi, Lakshmi Kasivajjula, Udai Mv, Sobha Bagadi, Punya Modi, Aditya Singh, Hemanth Gunupudi, Spenser Troiano, Jeff Paul, Justin Stovall, Justin Yamamoto

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The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data is considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since its data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for ex. quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP (SHapley Additive exPlanations), a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: competition, DAGs, facility, healthcare, machine learning, market share, random forest, SHAP

Procedia PDF Downloads 75