Search results for: artificial feeding

Authors: Amy Gooden

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Various modes of consent have been utilized in health research, but open consent has not been explored in South Africa’s AI research context. Open consent entails the sharing of data without assurances of privacy and may be seen as an attempt to marry open science with informed consent. Because all potential uses of data are unknown, it has been questioned whether consent can be informed. Instead of trying to adapt existing modes of consent, why not adopt a new perspective? This is what open consent proposes and what this research will explore in AI health research in South Africa.

Keywords: artificial intelligence, consent, health, law, research, South Africa

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Authors: Ivanka Valova

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This paper is aimed at using a multi-agent approach to monitor and diagnose a biotechnological system in order to validate certain control actions depending on the process development and the operating conditions. A multi-agent system is defined as a network of interacting software modules that collectively solve complex tasks. Remote monitoring and control of biotechnological processes is a necessity when automated and reliable systems operating with no interruption of certain activities are required. The advantage of our approach is in its flexibility, modularity and the possibility of improving by acquiring functionalities through the integration of artificial intelligence.

Keywords: multi-agent approach, artificial intelligence, biotechnological processes, anaerobic biodegradation

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Developing speed model is a challenging task particularly under mixed traffic condition where the traffic composition plays a significant role in determining vehicular speed. The present research has been conducted to model individual vehicular speed in the context of mixed traffic on an urban arterial. Traffic speed and volume data have been collected from three midblock arterial road sections in New Delhi. Using the field data, a volume based speed prediction model has been developed adopting the methodology of Artificial Neural Network (ANN). The model developed in this work is capable of estimating speed for individual vehicle category. Validation results show a great deal of agreement between the observed speeds and the predicted values by the model developed. Also, it has been observed that the ANN based model performs better compared to other existing models in terms of accuracy. Finally, the sensitivity analysis has been performed utilizing the model in order to examine the effects of traffic volume and its composition on individual speeds.

Keywords: speed model, artificial neural network, arterial, mixed traffic

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Authors: P. Singh, R. M. Banik

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Artificial neural network (ANN) was employed to optimize assay parameters viz., time, temperature, pH of reaction mixture, enzyme volume and substrate concentration of L-glutaminase from Bacillus cereus MTCC 1305. ANN model showed high value of coefficient of determination (0.9999), low value of root mean square error (0.6697) and low value of absolute average deviation. A multilayer perceptron neural network trained with an error back-propagation algorithm was incorporated for developing a predictive model and its topology was obtained as 5-3-1 after applying Levenberg Marquardt (LM) training algorithm. The predicted activity of L-glutaminase was obtained as 633.7349 U/l by considering optimum assay parameters, viz., pH of reaction mixture (7.5), reaction time (20 minutes), incubation temperature (35˚C), substrate concentration (40mM), and enzyme volume (0.5ml). The predicted data was verified by running experiment at simulated optimum assay condition and activity was obtained as 634.00 U/l. The application of ANN model for optimization of assay conditions improved the activity of L-glutaminase by 1.499 fold.

Keywords: Bacillus cereus, L-glutaminase, assay parameters, artificial neural network

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Authors: Mariel L. Alonzo, Patricia Mae T. Chi, Juliana Patrice P. Mayormita, Sanjana A. Sorio

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Compassion fade proposes an inverse relationship between the magnitude of stimuli to elicited compassion. This phenomenon is viewed within a framework that integrates a 3-Act Compassion structure with Latané and Darley’s Unresponsive Bystander Model and Prospect Theory of Decision-making under risk. Students (N=211) from Ateneo de Davao were sampled to examine the effects of mass perception (increasing number of needy persons) and intertemporal choice (soon versus later) on volunteering behavior. Collegiate classes in their natural setting were randomly assigned to five different treatment groups and were presented with audiovisual presentations featuring an increasing number of needy persons. The students were deceived to believe that two hypothetical feeding programs for Marawi refugees, taking place in 1 month and 6 months, were in need of volunteers for its preparatory phase. Results show a statistically significant (p=0.000; p=0.013) non-linear trend consistently for both feeding programs. There was a decrease in volunteered time means as identifiable victims increased from 0-47 and an increase as it progressed towards 267 non-identifiable victims. Highest interest was expressed for the 0 needy people shown and least for 47. The 0 hours volunteered was consistently the mode and median in all treatments. There was no statistically significant temporal discounting effect.

Keywords: compassion, group perception, identifiable victim, intertemporal choice, prosocial behavior, unresponsive bystander

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Authors: Peng Li, Luan Wang, Haifeng Fei, Renhong Xie, Yibin Rui, Shanhong Guo

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The applications of satellite-based automatic identification system (S-AIS) pave the road for wide-range maritime traffic monitoring and management. But the coverage of satellite’s view includes multiple AIS self-organizing networks, which leads to the collision of AIS signals from different cells. The contribution of this work is to propose an improved multi-channel blind source separation algorithm based on Artificial Bee Colony (ABC) and advanced stochastic optimization to perform separation of the mixed AIS signals. The proposed approach adopts modified ABC algorithm to get an optimized initial separating matrix, which can expedite the initialization bias correction, and utilizes the Adaptive Moment Estimation (Adam) to update the separating matrix by adjusting the learning rate for each parameter dynamically. Simulation results show that the algorithm can speed up convergence and lead to better performance in separation accuracy.

Keywords: satellite-based automatic identification system, blind source separation, artificial bee colony, adaptive moment estimation

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Authors: Shuang Luo, Er-Xiang Song

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Many problems in geotechnical engineering, such as foundation deformation, groundwater seepage, seismic wave propagation and geothermal transfer problems, may involve analysis in the ground which can be seen as extending to infinity. To that end, consideration has to be given regarding how to deal with the unbounded domain to be analyzed by using numerical methods, such as finite element method (FEM), finite difference method (FDM) or finite volume method (FVM). A simple artificial boundary approach derived from the analytical solutions for transient radial seepage problems, is introduced. It should be noted, however, that the analytical solutions used to derive the artificial boundary are particular solutions under certain boundary conditions, such as constant hydraulic head at the origin or constant pumping rate of the well. When dealing with unbounded domains with unsteady boundary conditions, a more sophisticated artificial boundary approach to deal with the infinity of the domain is presented. By applying Laplace transforms and introducing some specially defined auxiliary variables, the global artificial boundary conditions (ABCs) are simplified to local ones so that the computational efficiency is enhanced significantly. The introduced two local ABCs are implemented in a finite element computer program so that various seepage problems can be calculated. The two approaches are first verified by the computation of a one-dimensional radial flow problem, and then tentatively applied to more general two-dimensional cylindrical problems and plane problems. Numerical calculations show that the local ABCs can not only give good results for one-dimensional axisymmetric transient flow, but also applicable for more general problems, such as axisymmetric two-dimensional cylindrical problems, and even more general planar two-dimensional flow problems for well doublet and well groups. An important advantage of the latter local boundary is its applicability for seepage under rapidly changing unsteady boundary conditions, and even the computational results on the truncated boundary are usually quite satisfactory. In this aspect, it is superior over the former local boundary. Simulation of relatively long operational time demonstrates to certain extents the numerical stability of the local boundary. The solutions of the two local ABCs are compared with each other and with those obtained by using large element mesh, which proves the satisfactory performance and obvious superiority over the large mesh model.

Keywords: transient seepage, unbounded domain, artificial boundary condition, numerical simulation

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Authors: Tewodros Abate Alemayehu, Abebe Getahun, Akewake Geremew, Dawit Solomon Demeke, John Recha, Dawit Solomon, Gebremedihin Ambaw, Fasil Dawit Moges

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The purpose of this study was to examine the effects of supplementation of zinc oxide (ZnO) nanoparticles and Mannan-oligosaccharide (MOS) on growth performance, feed utilization, fatty acid profiles, hematology, and intestinal morphology of Chamo strain Nile tilapia Oreochromis niloticus (L.) fry reared at optimal temperature (28.62 ± 0.11 ⁰C). Nile tilapia fry (initial weight 1.45 ± 0.01g) were fed basal diet/control diet (Diet-T1), 6 g kg-¹ MOS supplemented diet (Diet-T2), 4 mg ZnO-NPs supplemented diet (Diet-T3), 4 mg ZnO-Bulk supplemented diet (Diet-T4), a combination of 6 g kg-¹ MOS and 4 mg ZnO-Bulk supplemented diet (Diet-T5) and combination of 6 g kg-¹ MOS and 4 mg ZnO-NPs supplemented diet (Diet-T6). Randomly, duplicate aquariums for each diet were assigned and hand-fed to apparent satiation three times daily (08:00, 12:00, and 16:00) for 12 weeks. Fish fed MOS, ZnO-NPs, and a combination of MOS and ZnO-Bulk supplemented diet had higher weight gain, Daily Growth Rate (DGR), and Specific Growth Rate (SGR) than fish fed the basal diet and other feeding groups, although the effect was not significant. According to the GC analysis, Nile tilapia was supplemented with 6 g kg-¹ MOS, 4 mg ZnO-NPs, or a combination of ZnO-NPs, and MOS showed the highest content of EPA, DHA, and higher ratios of PUFA/SFA than other feeding groups. Mean villi length in the proximal and middle portion of the Nile tilapia intestine was affected significantly (p<0.05) by diet. Fish fed Diet-T2 and Diet-T3 had significantly higher villi lengths in the proximal and middle portions of the intestine compared to other feeding groups. The inclusion of additives significantly improved goblet numbers at the proximal, middle, and distal portions of the intestine. Supplementation of additives had also improved some hematological parameters compared with control groups. In conclusion, dietary supplementation of additives MOS and ZnO-NPs could confer benefits on growth performance, fatty acid profiles, hematology, and intestinal morphology of Chamo strain Nile tilapia.

Keywords: chamo strain nile tilapia, fatty acid profile, hematology, intestinal morphology, MOS, ZnO-Bulk, ZnO-NPs

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Authors: Kiran Nair, Jincy George, Suhaib Anagreh

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Artificial intelligence (AI) in a marketing context is a form of a deterministic tool designed to optimize and enhance marketing tasks, research tools, and techniques. It is on the verge of transforming marketing roles and revolutionize the entire industry. This paper aims to explore the current dissemination of the application of artificial intelligence (AI) in the marketing mix, reviewing the scope and application of AI in various aspects of sales and distribution management. The paper also aims at identifying the areas of the strong impact of AI in factors of sales and distribution management such as distribution channel, purchase automation, customer service, merchandising automation, and shopping experiences. This is a qualitative research paper that aims to examine the impact of AI on sales and distribution management of 30 multinational brands in six different industries, namely: airline; automobile; banking and insurance; education; information technology; retail and telecom. Primary data is collected by means of interviews and questionnaires from a sample of 100 marketing managers that have been selected using convenient sampling method. The data is then analyzed using descriptive statistics, correlation analysis and multiple regression analysis. The study reveals that AI applications are extensively used in sales and distribution management, with a strong impact on various factors such as identifying new distribution channels, automation in merchandising, customer service, and purchase automation as well as sales processes. International brands have already integrated AI extensively in their day-to-day operations for better efficiency and improved market share while others are investing heavily in new AI applications for gaining competitive advantage.

Keywords: artificial intelligence, sales and distribution, marketing mix, distribution channel, customer service

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Authors: Dicky Eka Putra, S. P. Nadia Chairunissa, Lidia Paramita, Roza Hartati, Ice Yolanda Puri

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Background: Soy bean and the products such as tofu, tempeh and soy milk are famous in the community. Moreover, another product is tofu flour which is not familiar in Indonesia yet and it is well known as Okara. There are massive differences of energy, protein and carbohydrate between them which is know as good for protein sources as well. Unfortunately, it is seldom used as food variety. Basically, it can be benefit in order to create many products for example cakes, snacks and some desserts. Aim: the study was in order to promote the benefit of tofu flour as school feeding of elementary school and baby porridge and also to compare the nutrient. Method: Soy pulp was filtered and steamed approximately 30 minutes. Then, it was put at a plate under sunrise or barked on the oven for 10 hours at 800C. When it have dried and milling and tofu flour is ready to be used. Result: Tofu flour could be used as substitute of flour and rice flour when people want to cook some foods. In addition, some references said that soy bean is good for a specific remedy for the proper functioning of the heart, liver, kidneys, stomach, and bowels, constipation, as a stimulant for the lungs, for eradication of poison from the system, improving the complexion by cleaning the skin of impurities, and stimulating the growth and appearance of the hair. Discussion: Comparing between soy bean, tofu and tofu flour which has difference amount of nutrients. For example energy 382 kcal, 79 kcal and 393 kcal respectively and also protein 30.2 kcal, 7.8 kcal, and 17.4 kcal. In addition, carbohydrate of soy pulp was high than soy bean and tofu (30.1 kcal). Finally, local should replace flour, rice and gelatin rice flour with tofu flour.

Keywords: tofu flour, protein, soy bean, school feeding

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Authors: Saif M. M. Obeidat

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This research investigates the impact of artificial intelligence (AI) on interior spaces within a college of Architecture and Design. Employing a qualitative approach, the study conducts in-depth interviews and reviews AI-integrated design projects within the academic setting. The key objectives include assessing AI integration in design processes, examining the influence of AI on user experience, exploring its role in architectural innovation, identifying challenges, and assessing educational implications. The study aims to provide a comprehensive understanding of AI's role in shaping interior spaces within academia. It anticipates improved efficiency in design processes, positive user feedback on functionality and experiences, the emergence of innovative design solutions, and the identification of challenges like ethical considerations and technical limitations. Additionally, the research expects insights into how educational programs may need to adapt to incorporate AI knowledge and skills, ensuring students are well-prepared for the evolving landscape of architecture and design practice. By addressing these objectives, the research contributes valuable insights into the evolving relationship between technology and the field of architecture, particularly within educational contexts.

Keywords: interior design, artificial intelligence, academic settings, technology, education

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Authors: Mehvish Bilal, Navneet Singh, Jasir Mushtaq

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Water pollution puts people's health at risk, and it can also impact the ecology. For practitioners of integrated water resources management (IWRM), water quality modelling may be useful for informing decisions about pollution control (such as discharge permitting) or demand management (such as abstraction permitting). To comprehend the current pollutant load, movement of effective load movement of contaminants generates effective relation between pollutants, mathematical simulation, source, and water quality is regarded as one of the best estimating tools. The current study involves the Qual2k model, which includes manual simulation of the various physiochemical characteristics of water. To this end, various sensors could be installed for the automatic simulation of various physiochemical characteristics of water. An artificial intelligence model has been proposed for the automatic simulation of water quality parameters. Models of water quality have become an effective tool for identifying worldwide water contamination, as well as the ultimate fate and behavior of contaminants in the water environment. Water quality model research is primarily conducted in Europe and other industrialized countries in the first world, where theoretical underpinnings and practical research are prioritized.

Keywords: artificial intelligence, QUAL2K, simulation, physiochemical parameters

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Authors: Jone Ibarruri, Mikel Manso, Marta Cebrián

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A high amount of food is lost or discarded in the World every year. In addition, in the last decades, an increasing demand of new alternative and sustainable sources of proteins and other valuable compounds is being observed in the food and feeding sectors and, therefore, the use of food by-products as nutrients for these purposes sounds very interesting from the environmental and economical point of view. However, the direct use of discarded fruit and vegetables that present, in general, a low protein content is not interesting as feeding ingredient except if they are used as a source of fiber for ruminants. Especially in the case of aquaculture, several alternatives to the use of fish meal and other vegetable protein sources have been extensively explored due to the scarcity of fish stocks and the unsustainability of fishing for these purposes. Fish mortality is also of great concern in this sector as this problem highly reduces their economic feasibility. So, the development of new functional and natural ingredients that could reduce the need for vaccination is also of great interest. In this work, several fermentation tests were developed at lab scale using a selected mixture of fruit and vegetable discards from a wholesale market located in the Basque Country to increase their protein content and also to produce some bioactive extracts that could be used as additives in aquaculture. Fruit and vegetable mixtures (60/40 ww) were centrifugated for humidity reduction and crushed to 2-5 mm particle size. Samples were inoculated with a selected Rhizopus oryzae strain and fermented for 7 days in controlled conditions (humidity between 65 and 75% and 28ºC) in Petri plates (120 mm) by triplicate. Obtained results indicated that the final fermented product presented a twofold protein content (from 13 to 28% d.w). Fermented product was further processed to determine their possible functionality as a feed additive. Extraction tests were carried out to obtain an ethanolic extract (60:40 ethanol: water, v.v) and remaining biomass that also could present applications in food or feed sectors. The extract presented a polyphenol content of about 27 mg GAE/gr d.w with antioxidant activity of 8.4 mg TEAC/g d.w. Remining biomass is mainly composed of fiber (51%), protein (24%) and fat (10%). Extracts also presented antibacterial activity according to the results obtained in Agar Diffusion and to the Minimum Inhibitory Concentration (MIC) tests determined against several food and fish pathogen strains. In vitro, digestibility was also assessed to obtain preliminary information about the expected effect of extraction procedure on fermented product digestibility. First results indicated that remaining biomass after extraction doesn´t seem to improve digestibility in comparison to the initial fermented product. These preliminary results show that fermented fruit and vegetables can be a useful source of functional ingredients for aquaculture applications and a substitute of other protein sources in the feeding sector. Further validation will be also carried out through “in vivo” tests with trout and bass.

Keywords: fungal solid state fermentation, protein increase, functional extracts, feed ingredients

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Authors: Feng Wang, Shencheng Ni, Shuying Han, Shanhong You

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With the development of optical communication, optical performance monitoring (OPM) has received more and more attentions. Since optical signal-to-noise ratio (OSNR) is directly related to bit error rate (BER), it is one of the important parameters in optical networks. Recently, artificial neural network (ANN) has been greatly developed. ANN has strong learning and generalization ability. In this paper, a method of OSNR monitoring based on delay-tap sampling (DTS) and ANN has been proposed. DTS technique is used to extract the eigenvalues of the signal. Then, the eigenvalues are input into the ANN to realize the OSNR monitoring. The experiments of 10 Gb/s non-return-to-zero (NRZ) on–off keying (OOK), 20 Gb/s pulse amplitude modulation (PAM4) and 20 Gb/s return-to-zero (RZ) differential phase-shift keying (DPSK) systems are demonstrated for the OSNR monitoring based on the proposed method. The experimental results show that the range of OSNR monitoring is from 15 to 30 dB and the root-mean-square errors (RMSEs) for 10 Gb/s NRZ-OOK, 20 Gb/s PAM4 and 20 Gb/s RZ-DPSK systems are 0.36 dB, 0.45 dB and 0.48 dB respectively. The impact of chromatic dispersion (CD) on the accuracy of OSNR monitoring is also investigated in the three experimental systems mentioned above.

Keywords: artificial neural network (ANN), chromatic dispersion (CD), delay-tap sampling (DTS), optical signal-to-noise ratio (OSNR)

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Authors: Joshua Osondu

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This research explores how Artificial Intelligence (AI) can be utilized as a policy response to address teaching and learning (TL) issues in education in Ghana. The dual (AI and human) instructor model is used as a theoretical framework to examine how AI can be employed to improve teaching and learning processes and to equip learners with the necessary skills in the emerging AI society. A qualitative research design was employed to assess the impact of AI on various TL issues, such as teacher workloads, a lack of qualified educators, low academic performance, unequal access to education and educational resources, a lack of participation in learning, and poor access and participation based on gender, place of origin, and disability. The study concludes that AI can be an effective policy response to TL issues in Ghana, as it has the potential to increase students’ participation in learning, increase access to quality education, reduce teacher workloads, and provide more personalized instruction. The findings of this study are significant for filling in the gaps in AI research in Ghana and other developing countries and for motivating the government and educational institutions to implement AI in TL, as this would ensure quality, access, and participation in education and help Ghana industrialize.

Keywords: artificial intelligence, teacher, learner, students, policy response

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Authors: Alaa A. Shamaun Al-Abboodi, Yunis A. A. Bapeer

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400 one-day-old male broiler chicks (Ross-308) randomly divided to 2 main groups, 1st main group (GA) was feeding basal diet with medium chain fatty acid (MCFA) at rate of 0.15% and divided to four subgroups, 3 subgroups vaccinated with different routes with Newcastle Disease Virus (NDV) and non-vaccinated group. The 2nd main group (GB) feeding basal diet without MCFA and divided the same as 1st main group. The parameters used in this study included: ND antibody titers at 1, 10, 21, 28, 35 and 42 days of age and values of CD4 and CD8 at 1, 20, 30 and 42 days of age. This experiment detected increase in ND antibodies titers in (G1, G2, G3) groups were fed on basal diet MCFA comparing to groups were fed without adding MCFA (G5, G6, G7) and control groups (G4, G8). The results of cellular immune response (CD4 and CD8) T-cells in broiler chicks indicated that there was obviously significant relationship between dietary Fatty Acid (FA) versus the diet without FA on the level of CD4 parameter, for the entire experimental period. The effect of different ages was statistically significant in creating different values of CD4 level, whereas the CD4 level decreases markedly with age. However, analyzing the data of different vaccination methods, oculonasal method of vaccination led to the highest value of CD4 compared with the oral, S/C and control groups. There were statistical differences in CD8 values due to supplementation of FA versus the basal diet and due to the effect of different age periods. As for the age effect, the CD8 value at 20 days of age was significantly higher than at 42 and 30 days.

Keywords: broiler, CD4 and CD8, fatty acids, Newcastle Disease

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Authors: Boris M. Fedorov, Liudmila P. Goncharenko, Sergey A. Sybachin, Natalia A. Mamedova, Ekaterina V. Makarenkova, Saule Rakhimova

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In modern economic conditions, the question of the possibility of correct recognition of a signature on digital documents in order to verify the expression of will or confirm a certain operation is relevant. The additional complexity of processing lies in the dynamic variability of the signature for each individual, as well as in the way information is processed because the signature refers to biometric data. The article discusses the issues of using artificial intelligence models in order to improve the quality of signature confirmation in document recognition. The analysis of several possible options for using the model is carried out. The results of the study are given, in which it is possible to correctly determine the authenticity of the signature on small samples.

Keywords: signature recognition, biometric data, artificial intelligence, neural networks

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Authors: Abbas Ahmadi, Bijan Raie, Mohammad Reza Neyshabouri, Mohammad Ali Ghorbani, Farrokh Asadzadeh

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In recent years, vast areas of Urmia Lake in Dasht-e-Tabriz has dried up leading to saline sediments exposure on the surface lake coastal areas being highly susceptible to wind erosion. This study was conducted to investigate wind erosion and its relevance to soil physicochemical properties and also modeling of wind erodibility (WE) using artificial intelligence techniques. For this purpose, 96 soil samples were collected from 0-5 cm depth in 414000 hectares using stratified random sampling method. To measure the WE, all samples (<8 mm) were exposed to 5 different wind velocities (9.5, 11, 12.5, 14.1 and 15 m s-1 at the height of 20 cm) in wind tunnel and its relationship with soil physicochemical properties was evaluated. According to the results, WE varied within the range of 76.69-9.98 (g m-2 min-1)/(m s-1) with a mean of 10.21 and coefficient of variation of 94.5% showing a relatively high variation in the studied area. WE was significantly (P<0.01) affected by soil physical properties, including mean weight diameter, erodible fraction (secondary particles smaller than 0.85 mm) and percentage of the secondary particle size classes 2-4.75, 1.7-2 and 0.1-0.25 mm. Results showed that the mean weight diameter, erodible fraction and percentage of size class 0.1-0.25 mm demonstrated stronger relationship with WE (coefficients of determination were 0.69, 0.67 and 0.68, respectively). This study also compared efficiency of multiple linear regression (MLR), gene expression programming (GEP), artificial neural network (MLP), artificial neural network based on genetic algorithm (MLP-GA) and artificial neural network based on whale optimization algorithm (MLP-WOA) in predicting of soil wind erodibility in Dasht-e-Tabriz. Among 32 measured soil variable, percentages of fine sand, size classes of 1.7-2.0 and 0.1-0.25 mm (secondary particles) and organic carbon were selected as the model inputs by step-wise regression. Findings showed MLP-WOA as the most powerful artificial intelligence techniques (R2=0.87, NSE=0.87, ME=0.11 and RMSE=2.9) to predict soil wind erodibility in the study area; followed by MLP-GA, MLP, GEP and MLR and the difference between these methods were significant according to the MGN test. Based on the above finding MLP-WOA may be used as a promising method to predict soil wind erodibility in the study area.

Keywords: wind erosion, erodible fraction, gene expression programming, artificial neural network

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Authors: Ge Zheng, Jun Peng

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Unbalanced power output from individual cells of Proton Exchange Membrane Fuel Cell (PEMFC) has direct effects on PEMFC stack performance, in particular under transient operation. In the paper, a multi-layer ANN (Artificial Neural Network) model Radial Basis Functions (RBF) has been developed for predicting cells' output profiles by applying gas supply parameters, cooling conditions, temperature measurement of individual cells, etc. The feed-forward ANN model was validated with experimental data. Influence of relevant parameters of RBF on the network accuracy was investigated. After adequate model training, the modelling results show good correspondence between actual measurements and reconstructed output profiles. Finally, after the model was used to optimize the stack output performance under steady-state and transient operating conditions, it suggested that the developed ANN control model can help PEMFC stack to have obvious improvement on power output under fast acceleration process.

Keywords: proton exchange membrane fuel cell, PEMFC, artificial neural network, ANN, cell output profile, transient

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Authors: Sirilak Areerachakul

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Water quality has initiated serious management efforts in many countries. Artificial Neural Network (ANN) models are developed as forecasting tools in predicting water quality trend based on historical data. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (T-Coliform). The methodology involves applying data mining techniques using multilayer perceptron (MLP) neural network models. The data consisted of 11 sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2007-2011. The results of multilayer perceptron neural network exhibit a high accuracy multilayer perception rate at 94.23% in classifying the water quality of Saen Saep canal in Bangkok. Subsequently, this encouraging result could be combined with GIS data improves the classification accuracy significantly.

Keywords: artificial neural network, geographic information system, water quality, computer science

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Authors: Yogesh Karunakar, Praveen Kandaswamy

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Mother’s milk provides the most superior source of nutrition to a child. There is no other substitute to the mother’s milk. Postpartum secretions like breast milk can be analyzed on the go for testing the presence of any harmful pathogen before a mother can feed the child or donate the milk for the milk bank. Since breast feeding is one of the main causes for transmission of diseases to the newborn, it is mandatory to test the secretions. In this paper, we describe the detection of pathogens like E-coli, Human Immunodeficiency Virus (HIV), Hepatitis B (HBV), Hepatitis C (HCV), Cytomegalovirus (CMV), Zika and Ebola virus through an innovative method, in which we are developing a unique chip for testing the mother’s milk sample. The chip will contain an antibody specific to the target pathogen that will show a color change if there are enough pathogens present in the fluid that will be considered dangerous. A smart-phone camera will then be acquiring the image of the strip and using various image processing techniques we will detect the color development due to antigen antibody interaction within 5 minutes, thereby not adding to any delay, before the newborn is fed or prior to the collection of the milk for the milk bank. If the target pathogen comes positive through this method, then the health care provider can provide adequate treatment to bring down the number of pathogens. This will reduce the postpartum related mortality and morbidity which arises due to feeding infectious breast milk to own child.

Keywords: postpartum, fluids, camera, HIV, HCV, CMV, Zika, Ebola, smart-phones, breast milk, pathogens, image processing techniques

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Authors: Angelis P. Barlampas

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Target: The (SARS-CoV-2) is still a threat. AI software could be useful, categorizing the disease into different severities and indicate the extent of the lesions. Materials and methods: AI is a new revolutionary technique, which uses powered computerized systems, to do what a human being does more rapidly, more easily, as accurate and diagnostically safe as the original medical report and, in certain circumstances, even better, saving time and helping the health system to overcome problems, such as work overload and human fatigue. Results: It will be given an effort to describe to the inexperienced reader (see figures), as simple as possible, how an artificial intelligence system diagnoses computed tomography pictures. First, the computerized machine learns the physiologic motives of lung parenchyma by being feeded with normal structured images of the lung tissue. Having being used to recognizing normal structures, it can then easily indentify the pathologic ones, as their images do not fit to known normal picture motives. It is the same way as when someone spends his free time in reading magazines with quizzes, such as <> and <>. General conclusion: The AI mimics the physiological processes of the human mind, but it does that more efficiently and rapidly and provides results in a few seconds, whereas an experienced radiologist needs many days to do that, or even worse, he is unable to accomplish such a huge task.

Keywords: covid-19, artificial intelligence, automated imaging, CT, chest imaging

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Authors: Agada N. Ihuoma, Nagata Yasunori

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

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

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Authors: Jui-Ting Hsu, Heng-Li Huang, Ming-Tzu Tsai, Kuo-Chih Su, Lih-Jyh Fuh

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The objectives of this study were to measure the initial stability of the dental implant (ISQ and PTV) in the artificial foam bone block with three different quality levels. In addition, the 3D bone to implant contact percentage (BIC%) was measured based on the micro-computed tomography images. Furthermore, the relation between the initial stability of dental implant (ISQ and PTV) and BIC% were calculated. The experimental results indicated that enhanced the material property of the artificial foam bone increased the initial stability of the dental implant. The Pearson’s correlation coefficient between the BIC% and the two approaches (ISQ and PTV) were 0.652 and 0.745.

Keywords: dental implant, implant stability quotient, peak insertion torque, bone-implant contact, micro-computed tomography

Procedia PDF Downloads 556

Authors: Kasun Gayashan Samarawickrama

Abstract:

Bone diseases, defects, and fractions are commonly seen in modern life. Since bone is regenerating dynamic living tissue, it will undergo healing process naturally, it cannot recover from major bone injuries, diseases and defects. In order to overcome them, bone grafting technique was introduced. Gold standard was the best method for bone grafting for the past decades. Due to limitations of gold standard, alternative methods have been implemented. Apart from them artificial bone substitutes and bone tissue engineering have become the emerging methods with technology for bone grafting. Many bone diseases and defects will be healed permanently with these promising techniques in future.

Keywords: bone grafting, gold standard, bone substitutes, bone tissue engineering

Procedia PDF Downloads 278

Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

Abstract:

The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

Procedia PDF Downloads 51

Authors: Zouhour Neji Ben Salem

Abstract:

Microscopic evacuation focuses on the evacuee behavior and way of search of safety place in an egress situation. In recent years, several models handled microscopic evacuation problem. Among them, we have proposed Artificial Neural Network (ANN) as an alternative to mathematical models that can deal with such problem. In this paper, we present two ANN models: SOM map and Hopfield Network used to predict the evacuee behavior in a disaster situation. These models are tested in a real case, the second floor of Tunisian children hospital evacuation in case of fire. The two models are studied and compared in order to evaluate their performance.

Keywords: artificial neural networks, self-organization map, hopfield network, microscopic evacuation, fire building evacuation

Procedia PDF Downloads 376

Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

Abstract:

Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

Procedia PDF Downloads 112

Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

Abstract:

Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

Procedia PDF Downloads 104

Authors: Sayor Ajfar Aaron, Sajjat Hossain Abir, Ashif Newaz, Mushfiqur Rahman

Abstract:

Investigating the convergence of blockchain technology and artificial intelligence, this paper examines their synergistic effects on data integrity and decision-making within smart contracts. By implementing AI-driven analytics on blockchain-based platforms, the research identifies improvements in automated contract enforcement and decision accuracy. The paper presents a framework that leverages AI to enhance transparency and trust while blockchain ensures immutable record-keeping, culminating in significantly optimized operational efficiencies in various industries.

Keywords: artificial intelligence, blockchain, data integrity, smart contracts

Procedia PDF Downloads 17