Search results for: artificial kidney

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: Dulanjali Ranasinghe, Isuru Supasan, Kaushalya Premachandra, Ranjan Dissanayake, Ajith Rajapaksha, Eustace Fernando

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Proteomics studies of organisms are considered to be significantly information-rich compared to their genomic counterparts because proteomes of organisms represent the expressed state of all proteins of an organism at a given time. In modern top-down and bottom-up proteomics workflows, the primary analysis methods employed are gel–based methods such as two-dimensional (2D) electrophoresis and mass spectrometry based methods. Machine learning (ML) and artificial intelligence (AI) have been used increasingly in modern biological data analyses. In particular, the fields of genomics, DNA sequencing, and bioinformatics have seen an incremental trend in the usage of ML and AI techniques in recent years. The use of aforesaid techniques in the field of proteomics studies is only beginning to be materialised now. Although there is a wealth of information available in the scientific literature pertaining to proteomics workflows, no comprehensive review addresses various aspects of the combined use of proteomics and machine learning. The objective of this review is to provide a comprehensive outlook on the application of machine learning into the known proteomics workflows in order to extract more meaningful information that could be useful in a plethora of applications such as medicine, agriculture, and biotechnology.

Keywords: proteomics, machine learning, gel-based proteomics, mass spectrometry

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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

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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

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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: A. Seham A. Elgamal, Reham H. Saleh

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Chronic kidney disease became a global health problem worldwide. Therefore, in order to maintain a patient’s life and improve the survival rate, hemodialysis is essential to replace the function of their kidneys. However, those patients may complain about multiple physical and psychological stressors due to the nature of the disease and the need for frequent hemodialysis sessions. So, those patients use various strategies to cope with the stressors related to their disease and the treatment procedures. Cross-sectional, descriptive study was carried out to achieve the aim of the study. A convenient sample including all adult patients was recruited for this study. Hemodialysis Stressors Scale (HSS) and Jalowiec Coping Scale (JCS) were used to investigate the stressors and coping strategies of 89 hemodialysis patients, at a governmental hospital (King Khalid Hospital-Jeddah). Results of the study revealed that 50.7% experienced physiological stressors and 38% experienced psychosocial stressors. Also, optimistic, fatalistic, and supportive coping strategies were the most common coping strategies used by the patients with mean scores (2.88 + 0.75, 2.87 + 0.75, and 1.82 + 0.71), respectively. In conclusion, being familiar with the types of stressors and the effective coping strategies of hemodialysis patients and their families are important in order to enhance their adaptation with chronic kidney diseases.

Keywords: copying strategies, hemodialysis, physiological stressors, psychological stressors

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Authors: G. Anjan Babu, G. Sumana, M. Rajasekhar

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Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multi-layered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinanalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Furthermore, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.

Keywords: dialysis, hereditary, transplantation, polycystic, pathogenesis

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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: 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: Thomas E. Portegys

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An animal behavior problem is presented in the form of a nest-building task that involves two cooperating virtual birds, a male and female. The female builds a nest into which she lays an egg. The male's job is to forage in a forest for food for both himself and the female. In addition, the male must fetch stones from a nearby desert for the female to use as nesting material. The task is completed when the nest is built, and an egg is laid in it. A goal-seeking neural network and a recurrent neural network were trained and tested with little success. The goal-seeking network was then enhanced with “place cells”, allowing the birds to spatially navigate the world, building the nest while keeping themselves fed. Place cells are neurons in the hippocampus that map space.

Keywords: artificial animal intelligence, artificial life, goal-seeking neural network, nest-building, place cells, spatial navigation

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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: Jamie Lynn Co, Mary Shiela Ariola-Ramos

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Myroides are aerobic, yellow-pigmented, non-motile, non-fermenting gram-negative rods. They are commonly found in the environment such as water and soil. Although found in the environment, Myroides are rare pathogens of humans. Myroides spp. primarily infect immunocompromised patients, often with diabetes mellitus, liver cirrhosis, chronic kidney disease, chronic obstructive pulmonary disease or prolonged corticosteroid therapy. We present a case of a 70-year-old immunocompromised patient with diabetes mellitus, chronic renal failure, diagnosed with sepsis caused by Myroides spp. The primary portal and source of infection were the pustules and boils found on the lower extremities of the patient. Susceptibility testing showed that our isolate was only susceptible to ciprofloxacin and meropenem; and following the treatment, the patient recovered. Myroides continues to be a rare pathogen of humans that is prevalent in our environment. It primarily affects immunocompromised patients such as those with uncontrolled diabetes mellitus, chronic kidney disease, etc. Despite their low virulence, physicians should consider this opportunistic pathogen as possible etiologic agent especially in cases wherein there is lack of response to commonly used antibiotics.

Keywords: bacteremia, immunocompromised, gram negative rods, Myroides

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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: Yen-Fen Shen, Meng-Fan Li

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The study aims to investigate the effect of full-body vertical rhythmic training on fatigue, physical activity, and quality of life among middle-aged and elderly hemodialysis patients. The study adopted a quasi-experimental research method and recruited 43 long-term hemodialysis patients from a medical center in northern Taiwan, with 23 and 20 participants in the experimental and control groups, respectively. The experimental group received full-body vertical rhythmic training as an intervention, while the control group received standard hemodialysis care without any intervention. Both groups completed the measurements by using "Fatigue Scale", "Physical Activity Scale" and "Chinese version of the Kidney Disease Quality of Life Questionnaire" before and after the study. The experimental group underwent a 10-minute full-body vertical rhythmic training three times per week, which lasted for eight weeks before receiving regular hemodialysis treatment. The data were analyzed by SPSS 25 software, including descriptive statistics such as frequency distribution, percentages, means, and standard deviations, as well as inferential statistics, including chi-square, independent samples t-test, and paired samples t-test. The study results are summarized as follows: 1. There were no significant differences in demographic variables, fatigue, physical activity, and quality of life between the experimental and control groups in the pre-test. 2. After the intervention of the “full-body vertical rhythmic training,” the experimental group showed significantly better results in the category of "feeling tired and fatigued in the lower back", "physical functioning role limitation", "bodily pain", "social functioning", "mental health", and "impact of kidney disease on life quality." 3. The paired samples t-test results revealed that the control group experienced significant differences between the pre-test and post-test in the categories of feeling tired and fatigued in the lower back, bodily pain, social functioning mental health, and impact of kidney disease on life quality, with scores indicating a decline in life quality. Conversely, the experimental group only showed a significant worsening in bodily pain" and the impact of kidney disease on life quality, with lower change values compared to the control group. Additionally, there was an improvement in the condition of "feeling tired and fatigued in the lower back" for the experimental group. Conclusion: The intervention of the “full-body vertical rhythmic training” had a certain positive effect on the quality of life of the experimental group. While it may not entirely enhance patients' quality of life, it can mitigate the negative impact of kidney disease on certain aspects of the body. The study provides clinical practice, nursing education, and research recommendations based on the results and discusses the limitations of the research.

Keywords: hemodialysis, full-body vertical rhythmic training, fatigue, physical activity, quality of life

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Authors: Manea A. I. Alqrad, Alaa Sirwi, Sabrin R. M. Ibrahim, Hossam M. Abdallah, Gamal A. Mohamed

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Phytochemical study of the methanolic extract of the air dried powdered of the parts of Viscum schemperi Engl. (Family: Viscaceae) using different chromatographic techniques led to the isolation of five compounds: -amyrenone (1), betulinic acid (2), (3β)-olean-12-ene-3,23-diol (3), -oleanolic acid (4), and α-oleanolic acid (5). Their structures were established based on physical, chemical, and spectral data. Anti-inflammatory and anti-apoptotic activities of oleanolic acid in a mouse model of acute hepatorenal damage were assessed. This study showed the efficacy of oleanolic acid to counteract thioacetamide-induced hepatic and kidney injury in mice through the reduction of hepatocyte oxidative damage, suppression of inflammation, and apoptosis. More importantly, oleanolic acid suppressed thioacetamide-induced hepatic and kidney injury by inhibiting NF-κB/TNF-α-mediated inflammation/apoptosis and enhancing SIRT1/Nrf2/Heme-oxygenase signalling pathway. These promising pharmacological activities suggest the potential use of oleanolic acid against hepatorenal damage.

Keywords: oleanolic acid, viscum schimperi, thioacetamide, SIRT1/Nrf2/NF-κB, hepatorenal damage

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Authors: Sahar M. El-Gowilly, Mai M. Helmy, Hanan M. El-Gowelli

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Methotrexate (MTX) is widely used in treatment of cancers and autoimmune diseases. However, nephrotoxicity is one of the most important side effects of MTX. The peroxisome proliferator-activated receptor gamma agonist, pioglitazone (PIO), is known to exert anti-inflammatory and reno-protective effects in various kidney injuries. The purpose of this study was to investigate the potential involvement of endothelial damage in MTX-induced renal injury and to elaborate the possible protective effect of PIO against MTX-induced nephropathy. Compared with saline-treated rats, treatment with MTX (7 mg/kg for 3 day) caused significant elevations in serum levels of urea and creatinine, increased renal nitrate/nitrite level and impaired renovascular responsiveness of isolated perfused kidney to endothelium-dependent vasodilations induced by acetylcholine (0.01-2.43 nmol) and isoprenaline (1µmol). These effects were abolished by concurrent treatment with PIO (2.5 mg/kg, for 5 days starting two days before MTX). Alternatively, MTX treatment did not affect endothelium-independent renovascular relaxation induced by sodium nitroprusside (1-30 μmole). The possibility that alterations in renal antioxidants, circulating cytokine and apoptotic factor (Fas) levels contributed to MTX-PIO interaction was assessed. PIO treatment abrogated renal oxidative stress (decreased reduced glutathione and catalase activity and increased malondialdehyde), elevated serum cytokine (interleukin-6, interleukin-10, tumor necrosis factor-alpha and transforming growth factor-beta1) and Fas induced by MTX. Histologically, MTX caused defused tubular cells swelling and vacuolization associated with endothelial damage in renal arterioles. These effects disappeared upon co-treated with PIO. Collectively, PIO abolished MTX-induced endothelium dysfunction and nephrotoxicity via ameliorating oxidative stress and rectifying cytokines and Fas abnormalities caused by MTX.

Keywords: methotrexate, pioglitazone, endothelium, kidney

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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: Sahar M. El-Gowilly, Mai M. Helmy, Hanan M. El-Gowelli

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Methotrexate (MTX) is widely used in treatment of cancers and autoimmune diseases. However, nephrotoxicity is one of its most important side effects. The peroxisome proliferator-activated receptor gamma agonist, pioglitazone, is known to exert antiinflammatory and reno-protective effects in various kidney injuries. The purpose of this study was to investigate the potential involvement of endothelial damage in MTX-induced renal injury and to elaborate the possible protective effect of pioglitazone against MTX-induced endothelial impairment. Compared with saline-treated rats, treatment with MTX (7 mg/kg for 3 day) caused significant elevations in serum levels of urea and creatinine, increased renal nitrate/nitrite level and impaired renovascular responsiveness of isolated perfused kidney to endothelium-dependent vasodilations induced by acetylcholine (0.01-2.43 nmol) and isoprenaline (1µmol). These effects were abolished by concurrent treatment with pioglitazone (2.5 mg/kg, for 5 days starting two days before MTX). Alternatively, MTX treatment did not affect endothelium-independent renovascular relaxation induced by sodium nitroprusside (0.001-10 μmole). The possibility that alterations in renal antioxidants, circulating cytokine and apoptotic factor (Fas) levels contributed to MTX-pioglitazone interaction was assessed. Pioglitazone treatment abrogated renal oxidative stress (decreased reduced glutathione and catalase activity and increased malondialdehyde), elevated serum cytokine (interleukin-6, interleukin-10, tumor necrosis factor-alpha and transforming growth factor-beta1) and Fas induced by MTX. Histologically, MTX caused defused tubular cells swelling and vacuolization associated with endothelial damage in renal arterioles. These effects disappeared upon co-treated with pioglitazone. Collectively, pioglitazone abolished MTX-induced endothelium dysfunction and nephrotoxicity via ameliorating oxidative stress and rectifying cytokines and Fas abnormalities caused by MTX.

Keywords: methotrexate, pioglitazone, endothelium, kidney

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

Abstract:

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: Sulaiman Rabiu, Muazu Gusau Abubakar, Jafar Usman Zakari

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The consumption of meat is of great importance as it provides a good source of proteins and significant amount of essential trace element to the body. However, contamination of meat and meat products with heavy metals is becoming a serious threat to food safety and public health. Therefore, the present study is aimed to evaluate the concentration of some heavy metals in muscles and entrails of free-ranged cattle, sheep and goats. A total of sixty (60) fresh samples of muscles, liver, kidney, small intestines and stomach of free ranged cattle, sheep and goats were collected from abattoirs of different goldmine communities of Anka, Bukkuyum, Maru andTalata-Mafara Local Government Areas of Zamfara State, Nigeria. The samples were digested using 10 mL of a mixed 70% high grade concentration of HNO₃ and 65% HCl (4:1 v/v); the mixture was heated until dense fumes disappeared forming a clear transparent solution and diluted to 50 mL with deionized water. Actual concentrations of Cd, Cr, Cu, Co, As, Ni, Mn, Pb and Zn were determined using Microwave Plasma Atomic Emission Spectrophotometer (MP-AES). From the results obtained, goat liver had the highest mean concentration of lead, arsenic, cobalt and manganese (12.43± 0.31, 14.25±0.32, 3.47± 0.86 and 12.68± 0.92 mg/kg respectively) while goat kidney had the highest concentration of copper and zinc (10.08±0.61 and 24.16±1.30 mg/kg respectively). The highest concentrations of cadmium and nickel were recorded in sheep kidney (7.75± 0.65 and 2.08±0.10 mg/kg respectively). Cattle muscles had the highest chromium concentration than all the organs analysed. The target hazard quotients (THQs) for all the metals were below 1.0, but TR which is a risk indices for carcinogenicity indicates an alarming result that requires stringent control to protect public health.Therefore, intensive public health awareness on the risk associated with contamination of heavy metals in meat should be advocated.

Keywords: contamination, goldmine, heavy metals, meat

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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

Abstract:

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

Procedia PDF Downloads 455

Authors: Ammar Younas

Abstract:

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

Procedia PDF Downloads 115

Authors: Mustafa Tosun, Kevser Dincer

Abstract:

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

Procedia PDF Downloads 245

Authors: Mirza Mujtaba Baig

Abstract:

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

Procedia PDF Downloads 113

Authors: Moon Ho Do, Sin-Hee Park, Jae Hyuk Lee, Kyo Hee Cho, Jae Kyung Chae, Sun Yeou Kim

Abstract:

Long-term hyperglycemic conditions associated with diabetes lead to the formation of advanced glycation end-products (AGEs). Highly reactive glucose metabolites, methylglyoxal (MGO) and glyoxal (GO), induced carbonyl stress and it may induce cellular damage, cross-linking of proteins, and glycation, playing an important role in the impairment of kidney function. Small molecules that have the ability to inhibit AGE formation, and even break preformed AGEs have a beneficial impact on metabolic syndrome, diabetes, and cancer. We quantified contents of polyphenols in nuts and investigated the protective effect of nuts and polyphenols on MGO-induced cytotoxicity in porcine kidney epithelial cells (LLC-PK1). Moreover, we evaluated the inhibitory effect of AGEs formation in the presence of MGO or GO and possess the ability to break preformed AGEs. In this study, we confirmed twenty polyphenols in diverse nuts using LC-MS/MS system. Nuts and polyphenols play a protective role in LLC-PK1 cells by reducing MGO-induced cytotoxicity. They could also prevent the formation of MGO or GO-mediated AGEs and Break AGEs crosslink. It can be surmised that increased consumption of nuts would be an effective means of preventing diabetic diseases.

Keywords: advanced glycation end products, LLC-PK1, methylglyoxal, nut, polyphenol

Procedia PDF Downloads 262