Search results for: indigenous machine

Authors: Cristina Vatamanu, Doina Cosovan, Dragos Gavrilut, Henri Luchian

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In the past few years, the amount of malicious software increased exponentially and, therefore, machine learning algorithms became instrumental in identifying clean and malware files through semi-automated classification. When working with very large datasets, the major challenge is to reach both a very high malware detection rate and a very low false positive rate. Another challenge is to minimize the time needed for the machine learning algorithm to do so. This paper presents a comparative study between different machine learning techniques such as linear classifiers, ensembles, decision trees or various hybrids thereof. The training dataset consists of approximately 2 million clean files and 200.000 infected files, which is a realistic quantitative mixture. The paper investigates the above mentioned methods with respect to both their performance (detection rate and false positive rate) and their practicability.

Keywords: ensembles, false positives, feature selection, one side class algorithm

Procedia PDF Downloads 263

Authors: Alaa El-Din Rezk

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In Egypt, management of crops still away from what is being used today by utilizing the advances of mechanical design capabilities, sensing and electronics technology. These technologies have been introduced in many places and recorm, for Straight Path, Curved Path, Sine Wave ded high accuracy in different field operations. So, an autonomous robotic platform based on machine vision has been developed and constructed to be implemented in Egyptian conditions as self-propelled mobile vehicle for carrying tools for inter/intra-row crop management based on different control modules. The experiments were carried out at plant protection research institute (PPRI) during 2014-2015 to optimize the accuracy of agricultural robotic platform control using machine vision in term of the autonomous navigation and performance of the robot’s guidance system. Results showed that the robotic platform' guidance system with machine vision was able to adequately distinguish the path and resisted image noise and did better than human operators for getting less lateral offset error. The average error of autonomous was 2.75, 19.33, 21.22, 34.18, and 16.69 mm. while the human operator was 32.70, 4.85, 7.85, 38.35 and 14.75 mm Path, Offset Discontinuity and Angle Discontinuity respectively.

Keywords: autonomous robotic, Hough transform, image processing, machine vision

Procedia PDF Downloads 286

Authors: Rohita Gupta, G. S. Brah, R. Verma, C. S. Mukhopadhayay

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Although chicken strains show differences in susceptibility to a number of diseases, the underlying immunological basis is yet to be elucidated. In the present study, heterophils were subjected to LPS stimulation and total RNA extraction, further differential gene expression was studied in broiler, layer and indigenous Aseel strain by Real Time RT-PCR at different time periods before and after induction. The expression of the 14 AvBDs and chTLR 1, 2, 3, 4, 5, 7, 15 and 21 was detectable in heterophils. The expression level of most of the AvBDs significantly increased (P<0.05) 3 hours post in vitro lipopolysaccharide challenge. Higher expression level and stronger activation of most AvBDs, NFkB-1 and IRF-3 in heterophils was observed with the stimulation of LPS in layer compared to broiler, and in Aseel compared to both layer and broiler. This investigation will allow more refined interpretation of immuno-genetic basis of the variable disease resistance/susceptibility in divergent stock of chicken including indigenous breed. Moreover, this study will be helpful in formulation of strategy for isolation of antimicrobial peptides from heterophils.

Keywords: differential expression, heterophils, cytokines, defensin, TLR

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Authors: Jian Wang, Lu Yang, Jiong Peng

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Internal circulation two-platen clamping system for injection molding machine (IMM) has many potential advantages on energy-saving. In order to estimate its properties, experiments in this paper were carried out. Displacement and pressure of the components were measured. In comparison, the model of hydraulic clamping system was established by using AMESim. The related parameters as well as the energy consumption could be calculated. According to the analysis, the hydraulic system was optimized in order to reduce the energy consumption.

Keywords: AMESim, energy-saving, injection molding machine, internal circulation

Procedia PDF Downloads 520

Authors: G. Fouché, J. N. Eloff, K. Wellington

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Acaricides are commonly used to control ticks but are toxic, harmful to the environment and too expensive to resource-limited farmers. Traditionally, many communities in South Africa rely on a wide range of indigenous practices to keep their livestock healthy. One of these health care practices includes the use of medicinal plants and this offers an alternative to conventional medicine. An investigation was conducted at the CSIR in South Africa, and selected indigenous plants used in communities were scientifically evaluated for the management of ticks in animals. 17 plants were selected from 239 plants used traditionally in South Africa. Two different organic extracts were prepared from the 17 samples, resulting in 34 plant samples. These were tested for efficacy against two tick species, namely Rhipicephalus microplus and Rhipicephalus turanicus. The plant extracts were also screened against Vero cells and most were found to have low cytotoxicity. This study has shown that there is potential for the development of botanicals as natural acaricides against ticks that are non-toxic and environmentally benign.

Keywords: South Africa, ticks, plant extracts, Rhipicephalus (Boophilus) microplus

Procedia PDF Downloads 196

Authors: Kang-Mo Jung

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Least squares support vector machine (LS-SVM) is a penalized regression which considers both fitting and generalization ability of a model. However, the squared loss function is very sensitive to even single outlier. We proposed a weighted absolute deviation loss function for the robustness of the estimates in least absolute deviation support vector machine. The proposed estimates can be obtained by a quadratic programming algorithm. Numerical experiments on simulated datasets show that the proposed algorithm is competitive in view of robustness to outliers.

Keywords: least absolute deviation, quadratic programming, robustness, support vector machine, weight

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Authors: Ratun Rahman, Rafid Islam, Akin Ahmed, Kamrul Hasan, Hasan Mahmud

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Malware is becoming more prevalent, and several threat categories have risen dramatically in recent years. This paper provides a bird's-eye view of the world of malware analysis. The efficiency of five different machine learning methods (Naive Bayes, K-Nearest Neighbor, Decision Tree, Random Forest, and TensorFlow Decision Forest) combined with features picked from the retrieval of Android permissions to categorize applications as harmful or benign is investigated in this study. The test set consists of 1,168 samples (among these android applications, 602 are malware and 566 are benign applications), each consisting of 948 features (permissions). Using the permission-based dataset, the machine learning algorithms then produce accuracy rates above 80%, except the Naive Bayes Algorithm with 65% accuracy. Of the considered algorithms TensorFlow Decision Forest performed the best with an accuracy of 90%.

Keywords: android malware detection, machine learning, malware, malware analysis

Procedia PDF Downloads 128

Authors: T. A. Rosy, M. R. I. Talukdar, N. S. Juyena, F. Y. Bari, M. N. Islam

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The ovarian follicular dynamics, vaginal electrical resistance (VER) and progesterone (P4) and estrogen (E2) profiles were investigated during estrus cycle in four indigenous ewes. Daily VER values were recorded with heat detector. The follicles were observed and measured by trans-rectal ultrasonography. Blood was collected daily for hormonal profiles. Results showed a significant variation in VER values (P<0.05) at estrus in regards to ewes and cycles. The day difference between two successive lower values in VER waves ranged from 13-17 days which might indicate the estrus cycle in indigenous ewes. Trans-rectal ultrasonography of ovaries revealed the presence of two to four waves of follicular growth during the study period. Results also showed that follicular diameter was negatively correlated with VER values. Study of hormonal profiles by ELISA revealed a positive correlation between E2 concentration and development of follicle and negative correlation between P4 concentration and development of follicle. The concentrations of estradiol increased at the time of estrus and then fall down in a basal level. Development of follicular size was accompanied by an increase in the concentration of serum estradiol. Inversely, when follicles heed to ovulation concentration of progesterone starts to fall down and after ovulation it turns its way to the zenith and remains at this state until next ovulatory follicle comes to its maximum diameter. This study could help scientists to set up a manipulative reproductive technique for improving genetic values of sheep in Bangladesh.

Keywords: ovarian follicle, hormonal profile, sheep, ultrasonography, vaginal electrical resistance

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Authors: Bhargavi G. Iyer, Ojaswi Bhagat

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In recent years, there has been a lot of discussion about ethnic imbalance and diversity in the international context. Arbitration is now subject to the hegemony of a small number of people who are constantly reappointed. When a court system becomes exclusionary, the quality of adjudication suffers significantly. In such a framework, there is a misalignment between adjudicators' preconceived views and the interests of the parties, resulting in a biased view of the proceedings. The world is currently witnessing a slew of intellectual property battles around cultural appropriation. The term "cultural appropriation" refers to the industrial west's theft of indigenous culture, usually for fashion, aesthetic, or dramatic purposes. Selena Gomez exemplifies cultural appropriation by commercially using the “bindi,” which is sacred to Hinduism, as a fashion symbol. In another case, Victoria's Secret insulted indigenous peoples' genocide by stealing native Indian headdresses. In the case of yoga, a similar process can be witnessed, with Vedic philosophy being reduced to a type of physical practice. Such a viewpoint is problematic since indigenous groups have worked hard for generations to ensure the survival of their culture, and its appropriation by the western world for purely aesthetic and theatrical purposes is upsetting to those who practise such cultures. Because such conflicts involve numerous jurisdictions, they must be resolved through international arbitration. However, these conflicts are already being litigated, and the aggrieved parties, namely developing nations, do not believe it prudent to use the World Intellectual Property Organization's (WIPO) already established arbitration procedure. This practise, it is suggested in this study, is the outcome of Europe's exclusionary arbitral system, which fails to recognise the non-legal and non-commercial nature of indigenous culture issues. This research paper proposes a more comprehensive, inclusive approach that recognises the non-legal and non-commercial aspects of IP disputes involving cultural appropriation, which can only be achieved through an ethnically balanced arbitration structure. This paper also aspires to expound upon the benefits of arbitration and other means of alternative dispute resolution (ADR) in the context of disputes pertaining to cultural issues; positing that inclusivity is a solution to the existing discord between international practices and localised cultural points of dispute. This paper also hopes to explicate measures that will facilitate ensuring inclusion and ideal practices in the domain of arbitration law, particularly pertaining to cultural heritage and indigenous expression.

Keywords: arbitration law, cultural appropriation, dispute resolution, heritage, intellectual property

Procedia PDF Downloads 119

Authors: Te Kīpa Kēpa B. Morgan, Oliver W. Mcmillan, Dylan N. Taute, Tumanako N. Fa'aui

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Epistemologies are ways of knowing. Indigenous Peoples are aware that they do not perceive and experience the world in the same way as others. So it is important when empowering Indigenous epistemologies, such as that of the New Zealand Māori, to also be able to represent a scientific understanding within the same analysis. A geothermal development assessment tool has been developed by adapting the Mauri Model Decision Making Framework. Mauri is a metric that is capable of representing the change in the life-supporting capacity of things and collections of things. The Mauri Model is a method of grouping mauri indicators as dimension averages in order to allow holistic assessment and also to conduct sensitivity analyses for the effect of worldview bias. R-shiny is the coding platform used for this Vision Mātauranga research which has created an expert decision support tool (DST) that combines a stakeholder assessment of worldview bias with an impact assessment of mauri-based indicators to determine the sustainability of proposed geothermal development. The initial intention was to develop guidelines for quantifying mātauranga Māori impacts related to geothermal resources. To do this, three typical scenarios were considered: a resource owner wishing to assess the potential for new geothermal development; another party wishing to assess the environmental and cultural impacts of the proposed development; an assessment that focuses on the holistic sustainability of the resource, including its surface features. Indicator sets and measurement thresholds were developed that are considered necessary considerations for each assessment context and these have been grouped to represent four mauri dimensions that mirror the four well-being criteria used for resource management in Aotearoa, New Zealand. Two case studies have been conducted to test the DST suitability for quantifying mātauranga Māori and other biophysical factors related to a geothermal system. This involved estimating mauri0meter values for physical features such as temperature, flow rate, frequency, colour, and developing indicators to also quantify qualitative observations about the geothermal system made by Māori. A retrospective analysis has then been conducted to verify different understandings of the geothermal system. The case studies found that the expert DST is useful for geothermal development assessment, especially where hapū (indigenous sub-tribal grouping) are conflicted regarding the benefits and disadvantages of their’ and others’ geothermal developments. These results have been supplemented with evaluations for the cumulative impacts of geothermal developments experienced by different parties using integration techniques applied to the time history curve of the expert DST worldview bias weighted plotted against the mauri0meter score. Cumulative impacts represent the change in resilience or potential of geothermal systems, which directly assists with the holistic interpretation of change from an Indigenous Peoples’ perspective.

Keywords: decision support tool, holistic geothermal assessment, indigenous knowledge, mauri model decision-making framework

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Authors: Saba Ebrahimi, Hedieh Ashrafi

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Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations.

Keywords: classification methods, support vector machine, decision tree, forest cover-type dataset

Procedia PDF Downloads 184

Authors: Reginawanti Hindersah, Widiya Septiani Perdanawati, Dewi Azizah Sulaksana, Hidiyah Ayu Ma’rufah

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Maize in some region in East Nusa Tenggara Indonesia bordering Republic Democratic of Timor Leste is important local food crop and commonly cultivated using conventional method without appropriate plant nutrition system so that productivity is still low. A way to enhance local corn yield is adding biofertilizer containing nitrogen (N2) fixing bacteria such as Azotobacter. The purpose of this research was to determine N2 fixation, cytokinin as well as exopolysachharide production capacity of six indigenous Azotobacter strains in pure culture. The N2 fixation capacities of native 3 day old Azotobacter strains added to Ashby Media varied from 0.01 to 0.39 µM/g/hour. Cytokinin production of these strain in liquid culture of N-free Media was 0.11 to 40.04 ppm while exopolysachharide content in liquid culture of Vermani Media varied from 0.4 to 27.3 g/L. This results demonstrate that some local Azotobacter strains might be used as biofertilizer.

Keywords: azotobacter, local isolate, N fixation, phythohormone, exopolysaccaride

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Authors: Nicholas Milburn, Yu Liang, Dalei Wu

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Athlete performance scoring within the climbing do-main presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: classification, climbing, data imbalance, data scarcity, machine learning, time sequence

Procedia PDF Downloads 122

Authors: Asmiaty Sahur, Ambo Ala, Baharuddin Patanjengi, Elkawakib Syam'un

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This study aimed to isolate and characterize the indigenous Rhizosphere bacteria producing Gibberellin Acid as plant growth isolated from local soybean of three different areas in South Sulawesi, Indonesia. Several soil samples of soybean plants were collected from the Rhizosphere of local soybeans in three different areas of South Sulawesi such as Soppeng, Bone and Takalar. There were 56 isolates of bacteria were isolated and grouped into gram-positive bacteria and gram negative bacteria .There are 35 isolates produce a thick slime or slimy when cultured on media Natrium Broth and the remaining of those produced spores. The results showed that of potential bacterial isolated produced Gibberellin Acid in high concentration. The best isolate of Rhizosphere bacteria for the production of Gibberellin Acid is with concentration 2%. There are 4 isolates that had higher concentration are AKB 19 (4.67 mg/ml) followed by RKS 17 (3.80 mg/ml), RKS 25 (3.70 mg / ml) and RKS 24 (3.29 mg/ml) respectively.

Keywords: rhizosphere, bacteria, gibberellin acid, soybeans

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Authors: Nabil Shawkat, Jamil Saquer

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Social media has accelerated our ability to engage with others and eliminated many communication barriers. On the other hand, the widespread use of social media resulted in an increase in online hate speech. This has drastic impacts on vulnerable individuals and societies. Therefore, it is critical to detect hate speech to prevent innocent users and vulnerable communities from becoming victims of hate speech. We investigate the performance of different deep learning and machine learning algorithms on three different datasets. Our results show that the BERT model gives the best performance among all the models by achieving an F1-score of 90.6% on one of the datasets and F1-scores of 89.7% and 88.2% on the other two datasets.

Keywords: hate speech, machine learning, deep learning, abusive words, social media, text classification

Procedia PDF Downloads 108

Authors: Saad Chahba, Rabia Sehab, Ahmad Akrad, Cristina Morel

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Nowadays, the electric machines used in urban electric vehicles are, in most cases, three-phase electric machines with or without a magnet in the rotor. Permanent Magnet Synchronous Machine (PMSM) and Induction Machine (IM) are the main components of drive trains of electric and hybrid vehicles. These machines have very good performance in healthy operation mode, but they are not redundant to ensure safety in faulty operation mode. Faced with the continued growth in the demand for electric vehicles in the automotive market, improving the reliability of electric vehicles is necessary over the lifecycle of the electric vehicle. Multiphase electric machines respond well to this constraint because, on the one hand, they have better robustness in the event of a breakdown (opening of a phase, opening of an arm of the power stage, intern-turn short circuit) and, on the other hand, better power density. In this work, a diagnosis approach using a neural network for an open circuit fault or more of a five-phase induction machine is developed. Validation on the simulator of the vehicle drivetrain, at reduced power, is carried out, creating one and more open circuit stator phases showing the efficiency and the reliability of the new approach to detect and to locate on-line one or more open phases of a five-induction machine.

Keywords: electric vehicle drivetrain, multiphase drives, induction machine, control, open circuit (OC) fault diagnosis, artificial neural network

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Authors: Ferenc Kovács, Gábor Hosszú

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The paper presents an advanced control system for tennis ball throwing machines to improve their accuracy according to the ball impact points. A further advantage of the system is the much easier calibration process involving the intelligent solution of the automatic adjustment of the stroking parameters according to the ball elasticity, the self-calibration, the use of the safety margin at very flat strokes and the possibility to placing the machine to any position of the half court. The system applies mathematical methods to determine the exact ball trajectories and special approximating processes to access all points on the aimed half court.

Keywords: control system, robot programming, robot control, sports equipment, throwing machine

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Authors: Arthur D. Wiedemann, Christopher Fuller, Kyle A. Pascioni

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With the introduction of electric motors, small unmanned aerial vehicle designers have to consider trade-offs between acoustic noise and thrust generated. Currently, there are few low-computational tools available for predicting acoustic noise emitted by a propeller into the far-field. Artificial neural networks offer a highly non-linear and adaptive model for predicting isolated and interactive tonal noise. But neural networks require large data sets, exceeding practical considerations in modeling experimental results. A methodology known as physics guided machine learning has been applied in this study to reduce the required data set to train the network. After building and evaluating several neural networks, the best model is investigated to determine how the network successfully predicts the acoustic waveform. Lastly, a post-network transfer function is developed to remove discontinuity from the predicted waveform. Overall, methodologies from physics guided machine learning show a notable improvement in prediction performance, but additional loss functions are necessary for constructing predictive networks on small datasets.

Keywords: aeroacoustics, machine learning, propeller, rotor, neural network, physics guided machine learning

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Authors: Raghad A. Altowairgi

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With the wide spread of social media platforms, young people tend to use them extensively as the first means of communication due to their ease and modernity. But these platforms often create a fertile ground for bullies to practice their aggressive behavior against their victims. Platform usage cannot be reduced, but intelligent mechanisms can be implemented to reduce the abuse. This is where machine learning comes in. Understanding and classifying text can be helpful in order to minimize the act of cyberbullying. Artificial intelligence techniques have expanded to formulate an applied tool to address the phenomenon of cyberbullying. In this research, machine learning models are built to classify text into two classes; cyberbullying and non-cyberbullying. After preprocessing the data in 4 stages; removing characters that do not provide meaningful information to the models, tokenization, removing stop words, and lowering text. BoW and TF-IDF are used as the main features for the five classifiers, which are; logistic regression, Naïve Bayes, Random Forest, XGboost, and Catboost classifiers. Each of them scores 92%, 90%, 92%, 91%, 86% respectively.

Keywords: cyberbullying, machine learning, Bag-of-Words, term frequency-inverse document frequency, natural language processing, Catboost

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Authors: Szu-Kai Hsu, Po-Ching Lin

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As more and more multi-core processors emerge, traditional sequential programming paradigm no longer suffice. Yet only few modern dynamic programming languages can leverage such advantage. Ruby, for example, despite its wide adoption, only includes threads as a simple parallel primitive. The global virtual machine lock of official Ruby runtime makes it impossible to exploit full parallelism. Though various alternative Ruby implementations do eliminate the global virtual machine lock, they only provide developers dated locking mechanism for data synchronization. However, traditional locking mechanism error-prone by nature. Software Transactional Memory is one of the promising alternatives among others. This paper introduces a new virtual machine: GobiesVM to provide a native software transactional memory based solution for dynamic programming languages to exploit parallelism. We also proposed a simplified variation of Transactional Locking II algorithm. The empirical results of our experiments show that support of STM at virtual machine level enables developers to write straightforward code without compromising parallelism or sacrificing thread safety. Existing source code only requires minimal or even none modi cation, which allows developers to easily switch their legacy codebase to a parallel environment. The performance evaluations of GobiesVM also indicate the difference between sequential and parallel execution is significant.

Keywords: global interpreter lock, ruby, software transactional memory, virtual machine

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Authors: The Danh Phan

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House price forecasting is a main topic in the real estate market research. Effective house price prediction models could not only allow home buyers and real estate agents to make better data-driven decisions but may also be beneficial for the property policymaking process. This study investigates the housing market by using machine learning techniques to analyze real historical house sale transactions in Australia. It seeks useful models which could be deployed as an application for house buyers and sellers. Data analytics show a high discrepancy between the house price in the most expensive suburbs and the most affordable suburbs in the city of Melbourne. In addition, experiments demonstrate that the combination of Stepwise and Support Vector Machine (SVM), based on the Mean Squared Error (MSE) measurement, consistently outperforms other models in terms of prediction accuracy.

Keywords: house price prediction, regression trees, neural network, support vector machine, stepwise

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Authors: Nalini Murugaiyah

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Indigenous students require a positive school environment where meaningful learning ought to be there to minimise myriad challenges. Therefore, Orang Asli student’s school environment should be culturally responsive and equipped with student-centred activities or provide constructively designed curriculum and pedagogy. This study sought to extend the knowledge of culturally responsive school leadership practises which relevant and responsive to Orang Asli students through th lens of a theoretical framework, Culturally Responsive School Leadership. The aim of the proposed study is to examine and understand the real-world application of leadership practices that are relevant and responsive to Orang Asli students in Malaysia. This study will also include the often-voiceless voices’ of Orang Asli students, parents, and community leaders to gain a deeper understanding of the process and experience of engaging in culturally responsive school leadership. The study will explore the differences between school leaders, teachers, parents, and community leaders in relation to culturally responsive school environment, non-Orang Asli school leaders’ and teachers’ support to the needs of Orang Asli children, children’s perspectives of teachers’ practices in the classroom align with their culture; and, the demonstration of teacher’s culturally responsive behaviour in the classroom. A basic qualitative study is the proposed research design for this study, and the data is collected through semi-structured interviews and focus group interviews. This qualitative research is designed to gain in-depth knowledge about how the principal’s leadership is culturally responsive towards the school environment, which will improve the quality of education received by the Orang Asli community in Malaysia, hence reducing the drop-out rates in Orang Asli students.

Keywords: indigenous leadership, equity, inclusion, policy

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Authors: Hamid Mustafa, Heather J. Huson, Adeela Ajmal, Kim Euisoo, Tad S. Sonstegard

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In this study, 67 animals, representing six different cattle breeds of Pakistan, were genotyped with the Bovine high density (777K) SNP Beadchip. These include 13 Sahiwal, 09 Red Sindhi, 13 Tharparkar, 08 Achi, 13 Cholistani and 10 Dhanni cattle breeds. Analysis of 500, 939 SNP markers revealed that the mean minor allele frequency (MAF) was 0.21, 0.22, 0.18, 0.23, 0.22 and 0.22 for Sahiwal, Red Sindhi, Tharparkar, Achi, Cholistani and Dhanni respectively. Significant differences of minor allele frequency (MAF) were observed between the indigenous Pakistani cattle population (P<0.001). Across these Pakistani cattle breeds, a common variant MAF (≥0.10 and ≤0.5) accounted for an overall estimated 75.71 % of the 500,939 SNPs and on the average 19.58 % of the markers were monomorphic. Mean observed (HO) and expected (HE) heterozygosities were 0.656 and 0.638, respectively. This primarily study of Pakistani indigenous cattle breeds indicate that this level of SNPs variation can potentially be used for genomic studies for future breeding plans and for farm animal conservation strategies.

Keywords: Pakistan, cattle, minor allele frequency, SNP, variation

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

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Authors: Ryan Messina, Mehedi Hasan

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This research is to examine the impacts of using data to generate performance requirements for automation in visual inspections using machine vision. These situations are intended for design and how projects can smooth the transfer of tacit knowledge to using an algorithm. We have proposed a framework when specifying machine vision systems. This framework utilizes varying levels of automation as contingency planning to reduce data processing complexity. Using data assists in extracting tacit knowledge from those who can perform the manual tasks to assist design the system; this means that real data from the system is always referenced and minimizes errors between participating parties. We propose using three indicators to know if the project has a high risk of failing to meet requirements related to accuracy and reliability. All systems tested achieved a better integration into operations after applying the framework.

Keywords: automation, contingency planning, continuous engineering, control theory, machine vision, system requirements, system thinking

Procedia PDF Downloads 175

Authors: Rohita Gupta, G. S. Brah, R. Verma, C. S. Mukhopadhayay

Abstract:

Although chicken strains show differences in susceptibility to a number of diseases, the underlying immunological basis is yet to be elucidated. In the present study, heterophils were subjected to LPS stimulation and total RNA extraction, further differential gene expression was studied in broiler, layer and indigenous Aseel strain by Real Time RT-PCR at different time periods before and after induction. The expression of the 14 AvBDs and chTLR 1, 2, 3, 4, 5, 7, 15 and 21 was detectable in heterophils. The expression level of most of the AvBDs significantly increased (P<0.05) 3 hours post in vitro lipopolysaccharide challenge. Higher expression level and stronger activation of most AvBDs, NFkB-1 and IRF-3 in heterophils was observed, with the stimulation of LPS in layer compared to broiler, and in Aseel compared to both layer and broiler. This investigation will allow more refined interpretation of immuno-genetic basis of the variable disease resistance/susceptibility in divergent stock of chicken including indigenous breed. Moreover this study will be helpful in formulation of strategy for isolation of antimicrobial peptides from heterophils.

Keywords: differential expression, heterophils, cytokines, defensin, TLR

Procedia PDF Downloads 588

Authors: Shael Brown, Reza Farivar

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Persistence diagrams capture valuable topological features of datasets that other methods cannot uncover. Still, their adoption in data pipelines has been limited due to the lack of publicly available tools in R (and python) for analyzing groups of them with machine learning and statistical inference. In an easy-to-use and scalable R package called TDApplied, we implement several applied analysis methods tailored to groups of persistence diagrams. The two main contributions of our package are comprehensiveness (most functions do not have implementations elsewhere) and speed (shown through benchmarking against other R packages). We demonstrate applications of the tools on simulated data to illustrate how easily practical analyses of any dataset can be enhanced with topological information.

Keywords: machine learning, persistence diagrams, R, statistical inference

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Authors: Maitham Al-Safwani

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Motor vibration issues were analyzed by several studies. It is generally accepted that vibration issues result from poor equipment installation. We had a water injection pump tested in the factory and exceeded the pump the vibration limit. Once the pump was brought to the site, its half-size shim plates were replaced with full-size shims plates that drastically reduced the vibration. In this study, vibration data was recorded for several similar motors run at the same and different speeds. The vibration values were recorded -for two and a half hours- and the vibration readings were analyzed to determine when the readings became consistent. This was as well supported by recording the audio noises produced by some machines seeking a relationship between changes in machine noises and machine abnormalities, such as vibration.

Keywords: vibration, noise, installation, machine

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Authors: Emiliano Castillo

Abstract:

The aim of this research is to analyze the origins, the development and possible outcomes of the environmental conflict between grassroots organizations, indigenous communities, Kinder Morgan Corporation, and the Canadian government over the Trans Mountain pipeline expansion in Burnaby, British Columbia, Canada. Building on the political ecology and the environmental justice theoretical framework, this research examines the impacts and risks of tar sands extraction, production, and transportation on climate change, public health, the environment, and indigenous people´s rights over their lands. This study is relevant to the environmental justice and political ecology literature because it discusses the unequal distribution of environmental costs and economic benefits of tar sands development; and focuses on the competing interests, needs, values, and claims of the actors involved in the conflict. Furthermore, it will shed light on the context, conditions, and processes that lead to the organization and mobilization of a grassroots movement- comprised of indigenous communities, citizens, scientists, and non-governmental organizations- that draw significant media attention by opposing the Trans Mountain pipeline expansion. Similarly, the research will explain the differences and dynamics within the grassroots movement. This research seeks to address the global context of the conflict by studying the links between the decline of conventional oil production, the rise of unconventional fossil fuels (e.g. tar sands), climate change, and the struggles of low-income, ethnic, and racial minorities over the territorial expansion of extractive industries. Data will be collected from legislative documents, policy and technical reports, scientific journals, newspapers articles, participant observation, and semi-structured interviews with representatives and members of the grassroots organizations, indigenous communities, and Burnaby citizens that oppose the Trans Mountain pipeline. These interviews will focus on their perceptions of the risks of the Trans Mountain pipeline expansion; the roots of the anti-tar sands movement; the differences and dynamics within the movement; and the strategies to defend the livelihoods of local communities and the environment against tar sands development. This research will contribute to the understanding of the underlying causes of the environmental conflict between the Canadian government, Kinder Morgan, and grassroots organizations over tar sands extraction, production, and transportation in Burnaby, British Columbia, Canada. Moreover, this work will elucidate the transformations of society-nature relationships brought by tar sands development. Research findings will provide scientific information about how the resistance movement in British Columbia can challenge the dominant narrative on tar sands, exert greater influence in environmental politics, and efficiently defend Indigenous people´s rights to lands. Furthermore, this research will shed light into how grassroots movements can contribute towards the building of more inclusive and sustainable societies.

Keywords: environmental conflict, environmental justice, extractive industry, indigenous communities, political ecology, tar sands

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Authors: Tae-Uk Yun, Pullip Chung, Kee-Young Kwahk, Hyunchul Ahn

Abstract:

Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection using machine learning techniques over the past years. But, there have been no prior studies proposed an automated fake news detection method for Korean news to our best knowledge. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (topic modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as logistic regression, backpropagation network, support vector machine, and deep neural network can be applied. To validate the effectiveness of the proposed method, we collected about 200 short Korean news from Seoul National University’s FactCheck. which provides with detailed analysis reports from 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

Keywords: fake news detection, Korean news, machine learning, text mining

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