Search results for: animal artificial insemination

Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

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Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

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Authors: Rachid Dehini, Brahim Berbaoui

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The main objectives of shunt active power filter (SAPF) is to preserve the power system from unwanted harmonic currents produced by nonlinear loads, as well as to compensate the reactive power. The aim of this paper is to present a (PAPF) supplied by the Photovoltaic cells ,in such a way that the (PAPF) feeds the linear and nonlinear loads by harmonics currents and the excess of the energy is injected into the power system. In order to improve the performances of conventional (PAPF) This paper also proposes artificial neural networks (ANN) for harmonics identification and DC link voltage control. The simulation study results of the new (SAPF) identification technique are found quite satisfactory by assuring good filtering characteristics and high system stability.

Keywords: SAPF, harmonics current, photovoltaic cells, MPPT, artificial neural networks (ANN)

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Authors: Guangyuan Zhao, Nan Huang, Xuesong Han, Xu Huang

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In order to solve the problem of sample dilution in the traditional particle filter algorithm and achieve accurate state estimation in a nonlinear system, a particle filter method based on an improved artificial bee colony (ABC) algorithm was proposed. The algorithm simulated the process of bee foraging and optimization and made the high likelihood region of the backward probability of particles moving to improve the rationality of particle distribution. The opposition-based learning (OBL) strategy is introduced to optimize the initial population of the artificial bee colony algorithm. The convergence factor is introduced into the neighborhood search strategy to limit the search range and improve the convergence speed. Finally, the crossover and mutation operations of the genetic algorithm are introduced into the search mechanism of the following bee, which makes the algorithm jump out of the local extreme value quickly and continue to search the global extreme value to improve its optimization ability. The simulation results show that the improved method can improve the estimation accuracy of particle filters, ensure the diversity of particles, and improve the rationality of particle distribution.

Keywords: particle filter, impoverishment, state estimation, artificial bee colony algorithm

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Authors: M. H. Hussain, I. Musirin, A. F. Abidin, S. R. A. Rahim

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This paper presents the implementation of Artificial Bees Colony (ABC) algorithm in solving Directional OverCurrent Relays (DOCRs) coordination problem for near-end faults occurring in fixed network topology. The coordination optimization of DOCRs is formulated as linear programming (LP) problem. The objective function is introduced to minimize the operating time of the associated relay which depends on the time multiplier setting. The proposed technique is to taken as a technique for comparison purpose in order to highlight its superiority. The proposed algorithms have been tested successfully on 8 bus test system. The simulation results demonstrated that the ABC algorithm which has been proved to have good search ability is capable in dealing with constraint optimization problems.

Keywords: artificial bees colony, directional overcurrent relay coordination problem, relay settings, time multiplier setting

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Authors: Tugce Talay, Kadir Erkan

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In this study, the necessary steps for the design of axial flow permanent magnet motors are shown. The design and analysis of the engine were carried out based on ANSYS Maxwell program. The design parameters of the ANSYS Maxwell program and the artificial neural network system were established in MATLAB and the most efficient design parameters were found with the trained neural network. The results of the Maxwell program and the results of the artificial neural networks are compared and optimal working design parameters are found. The most efficient design parameters were submitted to the ANSYS Maxwell 3D design and the cogging torque was examined and design studies were carried out to reduce the cogging torque.

Keywords: AFPM, ANSYS Maxwell, cogging torque, design optimisation, efficiency, NNTOOL

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Authors: Muhittin Fatih Demirhan, Sibel Alapala Demirhan

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Agriculture, is also regarded as the primary activity area in all economies. When international comparisons are made Turkey has comparative advantages in agricultural potential. However, it is diffi cult to say that Turkey's agricultural productivity and use of technology is well developed in terms of sufficieny. Turkey, in terms of agricultural production, is one of the rare self-sufficient countries, but for supplying excessive demand of its domesticproduction to foreign markets to obtain the necessary income it is rather insufficient. On the basis of wrong policies implemented during the crisis and found that bottlenecks in agriculture and animal husbandry or agriculture policies of the IMF and World Bank are imposed on countries like Turkey. The IMF and the World Bank, the reduction of support in the agricultural and livestock Turkey, is known to put pressure for the abolition. Under these circumstances, our farmers, livestock producers and breeders of, not a chance to compete in the same market with EU producers. Animal products that capture the productivity levels of developed countries, seems to be our chance to compete with the quality and hygiene criteria. Thus, the discussion of the issue must be raised as for the sector's contribution to the economy in terms of further increasing production of the existing potential in mobilization.

Keywords: agricultural development, animal production, competition, economic crisis, food supply

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Authors: Jamerson Felipe Pereira Lima, Jeane Cecília Bezerra de Melo

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The difficulty and cost related to obtaining of protein tertiary structure information through experimental methods, such as X-ray crystallography or NMR spectroscopy, helped raising the development of computational methods to do so. An approach used in these last is prediction of tridimensional structure based in the residue chain, however, this has been proved an NP-hard problem, due to the complexity of this process, explained by the Levinthal paradox. An alternative solution is the prediction of intermediary structures, such as the secondary structure of the protein. Artificial Intelligence methods, such as Bayesian statistics, artificial neural networks (ANN), support vector machines (SVM), among others, were used to predict protein secondary structure. Due to its good results, artificial neural networks have been used as a standard method to predict protein secondary structure. Recent published methods that use this technique, in general, achieved a Q3 accuracy between 75% and 83%, whereas the theoretical accuracy limit for protein prediction is 88%. Alternatively, to achieve better results, support vector machines prediction methods have been developed. The statistical evaluation of methods that use different AI techniques, such as ANNs and SVMs, for example, is not a trivial problem, since different training sets, validation techniques, as well as other variables can influence the behavior of a prediction method. In this study, we propose a prediction method based on artificial neural networks, which is then compared with a selected SVM method. The chosen SVM protein secondary structure prediction method is the one proposed by Huang in his work Extracting Physico chemical Features to Predict Protein Secondary Structure (2013). The developed ANN method has the same training and testing process that was used by Huang to validate his method, which comprises the use of the CB513 protein data set and three-fold cross-validation, so that the comparative analysis of the results can be made comparing directly the statistical results of each method.

Keywords: artificial neural networks, protein secondary structure, protein structure prediction, support vector machines

Procedia PDF Downloads 583

Authors: Badr M. Alshammari, T. Guesmi

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This study presents a modified version of the artificial bee colony (ABC) algorithm by including a local search technique for solving the non-convex economic power dispatch problem. The local search step is incorporated at the end of each iteration. Total system losses, valve-point loading effects and prohibited operating zones have been incorporated in the problem formulation. Thus, the problem becomes highly nonlinear and with discontinuous objective function. The proposed technique is validated using an IEEE benchmark system with ten thermal units. Simulation results demonstrate that the proposed optimization algorithm has better convergence characteristics in comparison with the original ABC algorithm.

Keywords: economic power dispatch, artificial bee colony, valve-point loading effects, prohibited operating zones

Procedia PDF Downloads 225

Authors: Sakir Tasdemir, Mustafa Altin, Gamze Fahriye Pehlivan, Sadiye Didem Boztepe Erkis, Ismail Saritas, Selma Tasdemir

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Artificial intelligence applications are commonly used in industry in many fields in parallel with the developments in the computer technology. In this study, a fire room was prepared for the resistance of wooden construction elements and with the mechanism here, the experiments of polished materials were carried out. By utilizing from the experimental data, an artificial neural network (ANN) was modeled in order to evaluate the final cross sections of the wooden samples remaining from the fire. In modelling, experimental data obtained from the fire room were used. In the system developed, the first weight of samples (ws-gr), preliminary cross-section (pcs-mm2), fire time (ft-minute), fire temperature (t-oC) as input parameters and final cross-section (fcs-mm2) as output parameter were taken. When the results obtained from ANN and experimental data are compared after making statistical analyses, the data of two groups are determined to be coherent and seen to have no meaning difference between them. As a result, it is seen that ANN can be safely used in determining cross sections of wooden materials after fire and it prevents many disadvantages.

Keywords: artificial neural network, final cross-section, fire retardant polishes, fire safety, wood resistance.

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

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Authors: Vladimir Sharapaev

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This paper seeks to address the question of the authorship of copyrighted works created solely by the artificial intelligence or with the use thereof, and proposes possible interpretational or legislative solutions to the problems arising from the plurality of the persons potentially involved in the ultimate creation of the work and division of tasks among such persons. Being based on the commonly accepted assumption that a copyrighted work can only be created by a natural person, the paper does not deal with the issues regarding the creativity of the artificial intelligence per se (or the lack thereof), and instead focuses on the distribution of the intellectual property rights potentially belonging to the creators of the artificial intelligence and/or the creators of the content used for the formation of the copyrighted work. Moreover, the technical development and rapid improvement of the AI-based programmes, which tend to be reaching even greater independence on a human being, give rise to the question whether the initial creators of the artificial intelligence can be entitled to the intellectual property rights to the works created by such AI at all. As the juridical practice of some European courts and legal doctrine tends to incline to the latter opinion, indicating that the works created by the AI may not at all enjoy copyright protection, the questions of authorships appear to be causing great concerns among the investors in the development of the relevant technology. Although the technology companies dispose with further instruments of protection of their investments, the risk of the works in question not being copyrighted caused by the inconsistency of the case law and a certain research gap constitutes a highly important issue. In order to assess the possible interpretations, the author adopted a doctrinal and analytical approach to the research, systematically analysing the European and Czech copyright laws and case law in some EU jurisdictions. This study aims to contribute to greater legal certainty regarding the issues of the authorship of the AI-created works and define possible clues for further research.

Keywords: artificial intelligence, copyright, authorship, copyrighted work, intellectual property

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Authors: Angelika Magiera, Weronika Penar, Czesław Klocek

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Intellectual disability (ID) affects approximately 1.55% of children and adults in the society of developed countries. Depending on the ID degree, the patient is burdened with additional disease entities. Intellectual disability does not only limits a person’s opportunities to participate in social life but also affects whole families. People with ID belong to the group of risk of mental illnesses, they are less emotionally stable, while families are predisposed to depression. The study was held in a day care center for people with intellectual disabilities (of various degrees of disability) on 26 people. Nurses and carers also took part. The age range of study groups ranged from 22 to 67 years. Therapeutic classes were held for four independent mixed groups (sex and intellectual disability degree) from 6 to 7 people each, lasting no more than 30 minutes. They were created by the facility's staff to make sure that a group is stable. The animal assisted activity took place with a 2.5-year-old Ragdoll cat. The animal has passed the exam (certificate entitling her to take part in felinotherapy) and had 1.5 years of work experience. Due to the different degrees of ID, an individual emotional state survey was conducted among the caregivers of those who were involved in the activity, to assess the impact of animal assisted activity with a cat on patients. A positive effect on the emotional state of people with different types of intellectual disability was observed. Caregivers and nurses of those participating in the study express willingness to continue these types of classes and consider them necessary for this group of people.

Keywords: intellectual disabilities, animal-assisted activity, cat, feline, emotions

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Authors: Michael R. Phangtriastu, Herriyandi Herriyandi, Diaz D. Santika

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This paper presents a comparison of the implementation of metaheuristic algorithms to train the antecedent parameters and consequence parameters in the adaptive network-based fuzzy inference system (ANFIS). The algorithms compared are genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The objective of this paper is to benchmark well-known metaheuristic algorithms. The algorithms are applied to several data set with different nature. The combinations of the algorithms' parameters are tested. In all algorithms, a different number of populations are tested. In PSO, combinations of velocity are tested. In ABC, a different number of limit abandonment are tested. Experiments find out that ABC is more reliable than other algorithms, ABC manages to get better mean square error (MSE) than other algorithms in all data set.

Keywords: ANFIS, artificial bee colony, genetic algorithm, metaheuristic algorithm, particle swarm optimization

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Authors: Netanel Stern

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Artificial intelligence (AI) algorithms have emerged as powerful tools in the field of psychiatry, offering new possibilities for enhancing diagnosis and treatment outcomes. This article explores the utilization of AI algorithms in psychiatry, highlighting their potential to revolutionize patient care. Various AI algorithms, including machine learning, natural language processing (NLP), reinforcement learning, clustering, and Bayesian networks, are discussed in detail. Moreover, ethical considerations and future directions for research and implementation are addressed.

Keywords: AI, software engineering, psychiatry, neuroimaging

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Authors: Anh Duc Dang, Joachim Horn

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This paper presents a new approach to control robots, which can quickly find their swarm while tracking a moving target through the obstacles of the environment. In this approach, an artificial potential field is generated between each free-robot and the virtual attractive point of the swarm. This artificial potential field will lead free-robots to their swarm. The swarm-finding of these free-robots dose not influence the general motion of their swarm and nor other robots. When one singular robot approaches the swarm then its swarm-search will finish, and it will further participate with its swarm to reach the position of the target. The connections between member-robots with their neighbours are controlled by the artificial attractive/repulsive force field between them to avoid collisions and keep the constant distances between them in ordered formation. The effectiveness of the proposed approach has been verified in simulations.

Keywords: formation control, potential field method, obstacle avoidance, swarm intelligence, multi-agent systems

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

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

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

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Authors: Gergely G. Karacsony

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Fundamental rights are the result of thousand years’ progress of legislation, adjudication and legal practice. They serve as the framework of peaceful cohabitation of people, protecting the individual from any abuse by the government or violation by other people. Artificial intelligence, however, is the development of the very recent past, being one of the most important prospects to the future. Artificial intelligence is now capable of communicating and performing actions the same way as humans; such acts are sometimes impossible to tell from actions performed by flesh-and-blood people. In a world, where human-robot interactions are more and more common, a new framework of peaceful cohabitation is to be found. Artificial intelligence, being able to take part in almost any kind of interaction where personal presence is not necessary without being recognized as a non-human actor, is now able to break the law, violate people’s rights, and disturb social peace in many other ways. Therefore, a code of peaceful coexistence is to be found or created. We should consider the issue, whether human rights can serve as the code of ethical and rightful conduct in the new era of artificial intelligence and human coexistence. In this paper, we will examine the applicability of fundamental rights to human-robot interactions as well as to the actions of artificial intelligence performed without human interaction whatsoever. Robot ethics has been a topic of discussion and debate of philosophy, ethics, computing, legal sciences and science fiction writing long before the first functional artificial intelligence has been introduced. Legal science and legislation have approached artificial intelligence from different angles, regulating different areas (e.g. data protection, telecommunications, copyright issues), but they are only chipping away at the mountain of legal issues concerning robotics. For a widely acceptable and permanent solution, a more general set of rules would be preferred to the detailed regulation of specific issues. We argue that human rights as recognized worldwide are able to be adapted to serve as a guideline and a common basis of coexistence of robots and humans. This solution has many virtues: people don’t need to adjust to a completely unknown set of standards, the system has proved itself to withstand the trials of time, legislation is easier, and the actions of non-human entities are more easily adjudicated within their own framework. In this paper we will examine the system of fundamental rights (as defined in the most widely accepted source, the 1966 UN Convention on Human Rights), and try to adapt each individual right to the actions of artificial intelligence actors; in each case we will examine the possible effects on the legal system and the society of such an approach, finally we also examine its effect on the IT industry.

Keywords: human rights, robot ethics, artificial intelligence and law, human-robot interaction

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Authors: Saira Naeem, Rana Muhammad Ayyub

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The main objective of this study is to empirically study the antecedents of perceptions of non-Muslim consumers towards Halal foods. The questionnaire survey was conducted through surveymonkey.com from non-Muslims (n=222) of USA. The validated scales of knowledge about Halal foods, animal welfare concerns, acculturation and perception about Halal foods were adopted after necessary adaptation as measures. The structural equation modelling (SEM) approach was used to study the structural model. It was found that Knowledge about Halal foods and ongoing acculturation among non-Muslims has a positive effect on perception about Halal food whereas; animal welfare concerns have negative effect on it. Furthermore, the acculturation has moderating effects but it was found non-significant. It is recommended that Halal food marketers should increase their efforts to educate customers by updating their knowledge about it. Furthermore, it is recommended that the non-Muslim consumers must be apprised of the fact that their animal welfare concerns are adequately addressed while Halal food production and supply chain. Online data collection is the only limitation of this study. This study will guide the Halal marketers of western countries about how to market the Halal food products and services to serve the non-Muslim customers.

Keywords: non-Muslims, consumer perceptions, animal welfare concerns, acculturation, knowledge about Halal

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Authors: Dimitrios Triantakonstantis, Demetris Stathakis

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Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: artificial neural networks, CORINE, urban atlas, urban growth prediction

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Authors: Unsain Dianne, Roussel Audrey, Goude Gwenaëlle, Magniez Pierre, Storå Jan

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This paper aims to investigate the changes in husbandry practices and meat consumption during the transition from the Medieval Climate Anomaly to the Little Ice Age in the South of France. More precisely, we will investigate breeding strategies, animal size and health status, carcass exploitation strategies, and the impact of socioeconomic status on human-environment interactions. For that purpose, we will analyze faunal remains from ten sites equally distributed between the two periods. Those include consumers from different socio-economic backgrounds (peasants, city dwellers, soldiers, lords, and the Popes). The research will employ different methods used in zooarchaeology: comparative anatomy, biometry, pathologies analyses, traceology, and utility indices, as well as experimental archaeology, to reconstruct and understand the changes in animal breeding and consumption practices. Their analysis will allow the determination of modifications in the animal production chain, with the composition of the flocks (species, size), their management (age, sex, health status), culinary practices (strategies for the exploitation of carcasses, cooking, tastes) or the importance of trade (butchers, sales of processed animal products). The focus will also be on the social extraction of consumers. The aim will be to determine whether climate change has had a greater impact on the most modest groups (such as peasants), whether the consequences have been global and have also affected the highest levels of society, or whether the social and economic factors have been sufficient to balance out the climatic hazards, leading to no significant changes. This study will contribute to our understanding of the impact of climate change on breeding and consumption strategies in medieval society from a historical and social point of view. It combines various research methods to provide a comprehensive analysis of the changes in human-animal interactions during different climatic periods.

Keywords: archaeology, animal economy, cooking, husbandry practices, climate change, France

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Nadia Chagtmi, Nejla Rejab, Noureddine Boulejfen

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This paper presents a calibration approach based on artificial neural networks (ANN) to determine the envelop signal (I+jQ) of a six-port based receiver (SPR). The memory effects called also dynamic behavior and the nonlinearity brought by diode based power detector have been taken into consideration by the ANN. Experimental set-up has been performed to validate the efficiency of this method. The efficiency of this approach has been confirmed by the obtained results in terms of waveforms. Moreover, the obtained error vector magnitude (EVM) and the mean absolute error (MAE) have been calculated in order to confirm and to test the ANN’s performance to achieve I/Q recovery using the output voltage detected by the power based detector. The baseband signal has been recovered using ANN with EVMs no higher than 1 % and an MAE no higher than 17, 26 for the SPR excited different type of signals such QAM (quadrature amplitude modulation) and LTE (Long Term Evolution).

Keywords: six-port based receiver; calibration, nonlinearity, memory effect, artificial neural network

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Authors: Laidi Maamar, Hanini Salah

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The present work investigates the potential of artificial neural network (ANN) model to predict the horizontal global solar radiation (HGSR). The ANN is developed and optimized using three years meteorological database from 2011 to 2013 available at the meteorological station of Blida (Blida 1 university, Algeria, Latitude 36.5°, Longitude 2.81° and 163 m above mean sea level). Optimal configuration of the ANN model has been determined by minimizing the Root Means Square Error (RMSE) and maximizing the correlation coefficient (R2) between observed and predicted data with the ANN model. To select the best ANN architecture, we have conducted several tests by using different combinations of parameters. A two-layer ANN model with six hidden neurons has been found as an optimal topology with (RMSE=4.036 W/m²) and (R²=0.999). A graphical user interface (GUI), was designed based on the best network structure and training algorithm, to enhance the users’ friendliness application of the model.

Keywords: artificial neural network, global solar radiation, solar energy, prediction, Algeria

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Authors: Sabir Messalti, Bilal Boudjellal, Azouz Said

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In this paper, power system Voltage improvement using wind turbine is presented. Two controllers are used: a PI controller and Artificial Neural Networks (ANN) controllers are studied to control of the power flow exchanged between the wind turbine and the power system in order to improve the bus voltage. The wind turbine is based on a doubly-fed induction generator (DFIG) controlled by field-oriented control. Indirect control is used to control of the reactive power flow exchanged between the DFIG and the power system. The proposed controllers are tested on power system for large voltage disturbances.

Keywords: artificial neural networks controller, DFIG, field-oriented control, PI controller, power system voltage improvement

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Authors: Ammar Younas

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This paper is an attempt to explore the legal progression in procedural laws related to “intellectual property protection for the autonomous output of artificial intelligence” in Post-Colonial, Post-Communist and Socialist Countries. An in-depth study of legal progression in Pakistan (Common Law), Uzbekistan (Post-Soviet Civil Law) and China (Socialist Law) has been conducted. A holistic attempt has been made to explore that how the ideological context of the legal systems can impact, not only on substantive components but on the procedural components of the formal laws related to IP Protection of autonomous output of Artificial Intelligence. Moreover, we have tried to shed a light on the prospective IP laws and AI Policy in the countries, which are planning to incorporate the concept of “Digital Personality” in their legal systems. This paper will also address the question: “How far IP of autonomous output of AI can be protected with the introduction of “Non-Human Legal Personality” in legislation?” By using the examples of China, Pakistan and Uzbekistan, a case has been built to highlight the legal progression in General Provisions of Civil Law, Artificial Intelligence Policy of the country and Intellectual Property laws. We have used a range of multi-disciplinary concepts and examined them on the bases of three criteria: accuracy of legal/philosophical presumption, applying to the real time situations and testing on rational falsification tests. It has been observed that the procedural laws are designed in a way that they can be seen correlating with the ideological contexts of these countries.

Keywords: intellectual property, artificial intelligence, digital personality, legal progression

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Authors: Mustafa Tosun, Kevser Dincer

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In this study, analysis on whether the lightweight concrete walled structures used in four climatic regions of Turkey are also capable of insulating sound was conducted. As a new approach, first the wall’s thermal insulation sufficiency’s were calculated and then, artificial neural network (ANN) modeling was used on their cross sections to check if they are sound transmitters too. The ANN was trained and tested by using MATLAB toolbox on a personal computer. ANN input parameters that used were thickness of lightweight concrete wall, frequency and density of lightweight concrete wall, while the transmitted sound was the output parameter. When the results of the TS analysis and those of ANN modeling are evaluated together, it is found from this study, that sound transmit loss increases at higher frequencies, higher wall densities and with larger wall cross sections.

Keywords: artificial neuron network, lightweight concrete, sound insulation, sound transmit loss

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Authors: Victoria Goodall, Paul Fatti, Norman Owen-Smith

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Hidden Markov Models have become popular for the analysis of animal tracking data. These models are being used to model the movements of a variety of species in many areas around the world. A common assumption of the model is that the observations need to have regular time steps. In many ecological studies, this will not be the case. The objective of the research is to modify the movement model to allow for irregularly spaced locations and investigate the effect on the inferences which can be made about the latent states. A modification of the likelihood function to allow for these irregular spaced locations is investigated, without using interpolation or averaging the movement rate. The suitability of the modification is investigated using GPS tracking data for lion (Panthera leo) in South Africa, with many observations obtained during the night, and few observations during the day. Many nocturnal predator tracking studies are set up in this way, to obtain many locations at night when the animal is most active and is difficult to observe. Few observations are obtained during the day, when the animal is expected to rest and is potentially easier to observe. Modifying the likelihood function allows the popular Hidden Markov Model framework to be used to model these irregular spaced locations, making use of all the observed data.

Keywords: hidden Markov Models, irregular observations, animal movement modelling, nocturnal predator

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Authors: Mirza Mujtaba Baig

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Artificial Intelligence (AI) is evolving rapidly, and one of the areas in which this field has influenced is automation. The automobile, healthcare, education, and robotic industries deploy AI technologies constantly, and the automation of tasks is beneficial to allow time for knowledge-based tasks and also introduce convenience to everyday human endeavors. The paper reviews the challenges faced with the current implementations of autonomous self-driving cars by exploring the machine learning, robotics, and artificial intelligence techniques employed for the development of this innovation. The controversy surrounding the development and deployment of autonomous machines, e.g., vehicles, begs the need for the exploration of the configuration of the programming modules. This paper seeks to add to the body of knowledge of research assisting researchers in decreasing the inconsistencies in current programming modules. Blockchain is a technology of which applications are mostly found within the domains of financial, pharmaceutical, manufacturing, and artificial intelligence. The registering of events in a secured manner as well as applying external algorithms required for the data analytics are especially helpful for integrating, adapting, maintaining, and extending to new domains, especially predictive analytics applications.

Keywords: artificial intelligence, automation, big data, self-driving cars, machine learning, neural networking algorithm, blockchain, business intelligence

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Authors: Frankie Burgos, Emely Munar, Conrado Basa

Abstract:

This paper aimed at developing an artificial neural network (ANN) model specifically for the Philippine Stock Exchange index closing value. The inputs to the ANN are US Dollar and Philippine Peso(USD-PHP) exchange rate, GDP growth of the country, quarterly inflation rate, 10-year bond yield, credit rating of the country, previous open, high, low, close values and volume of trade of the Philippine Stock Exchange Index (PSEi), gold price of the previous day, National Association of Securities Dealers Automated Quotations (NASDAQ), Standard and Poor’s 500 (S & P 500) and the iShares MSCI Philippines ETF (EPHE) previous closing value. The target is composed of the closing value of the PSEi during the 627 trading days from November 3, 2011, to May 30, 2014. MATLAB’s Neural Network toolbox was employed to create, train and simulate the network using multi-layer feed forward neural network with back-propagation algorithm. The results satisfactorily show that the neural network developed has the ability to model the PSEi, which is affected by both internal and external economic factors. It was found out that the inputs used are the main factors that influence the movement of the PSEi closing value.

Keywords: artificial neural networks, artificial intelligence, philippine stocks exchange index, stocks trading

Procedia PDF Downloads 265

Authors: Ebtisam A. Al-Mslemani, Khalid A. Enan, Asmaa Abdelgadier, Nada Assaad, Zaynab Elhussein, Khalid Eltom

Abstract:

Background: Toxoplasma gondii (T. gondii) is a zoonotic parasite that can be transmitted from animals to humans, with felids acting as its definitive host. Thus, understanding the epidemiology of this parasite in animal populations is vital to controlling its transmission to humans as well as to other animal groups. Objectives: This systematic review and meta-analysis aim to summarise and analyse reports of T. gondii infection in animal species residing in the Arabian Peninsula. Methods: It was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), with relevant studies being retrieved from MEDLINE/PubMed, Scopus, Cochrane Library, Google Scholar and ScienceDirect. All articles published in Arabic or English languages between January 2000 and December 2020 were screened for eligibility. The random effects model was used to calculate the pooled prevalence of T. gondii infection in different animal populations which were found to harbour this infection. The critical appraisal tool for prevalence studies designed by the Joanna Briggs Institute (JBI) was used to assess the risk of bias in all included studies. Results: A total of 15 studies were retrieved, reporting prevalence estimates from 4 countries in this region and in 13 animal species. A quantitative meta-analysis estimated a pooled prevalence of 43% in felids [95% confidence interval (CI) = 23-64%, I2 index = 100%], 48% in sheep (95% CI = 27-70%, I2 = 99%) and 21% in camels (95% CI = 7-35%, I2 = 99%). Evidence of possible publication bias was found in both felids and sheep. Conclusions: This meta-analysis estimates a high prevalence of T. gondii infection in animal species that are of high economic and cultural importance to countries of this region. Hence, these findings provide valuable insight to public health authorities as well as economic and animal resources advisors in countries of the Arabian Peninsula.

Keywords: Arabian Peninsula, toxoplasma gondii, animals; meta-analysis, toxoplasmosis

Procedia PDF Downloads 47