Search results for: artificial immunity technique

Authors: Nathaniel C. Villanueva, Ian K. H. Chun, Alyssa S. Fujiwara, Emily R. Leibovitch, Brennan E. Yamamoto, Loren G. Yamamoto

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Introduction: In previous studies of high school sports, over 15% of concussions were attributed to contact with the playing surface. While artificial turf fields are increasing in popularity due to lower maintenance costs, artificial turf has been associated with more ankle and knee injuries, with inconclusive data on concussions. In this study, natural grass and artificial football fields were compared in terms of deceleration on fall impact. Methods: Accelerometers were placed on the forehead, apex of the head, and right ear of a Century Body Opponent Bag (BOB) manikin. A Riddell HITS football helmet was secured onto the head of the manikin over the accelerometers. This manikin was dropped onto natural grass (n = 10) and artificial turf (n = 9) high school football fields. The manikin was dropped from a stationary position at a height of 60 cm onto its front, back, and left side. Each of these drops was conducted 10 times at the 40-yard line, 20-yard line, and endzone. The net deceleration on impact was calculated as a net vector from each of the three accelerometers’ x, y, and z vectors from the three different locations on the manikin’s head (9 vector measurements per drop). Results: Mean values for the multiple drops were calculated for each accelerometer and drop type for each field. All accelerometers in forward and backward falls and one accelerometer in side falls showed significantly greater impact force on synthetic turf compared to the natural grass surfaces. Conclusion: Impact force was higher on synthetic fields for all drop types for at least one of the accelerometer locations. These findings suggest that concussion risk might be higher for athletes playing on artificial turf fields.

Keywords: concussion, football, biomechanics, sports

Procedia PDF Downloads 152

Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

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A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

Keywords: behavioural biometric, face biometric, neural network, physical biometric, signature biometric

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Authors: Sukhbir Singh

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This paper describes the modeling of novel solar air collector (NSAC) system by using artificial neural network (ANN) model. The objective of the study is to demonstrate the application of the ANN model to predict the performance of the NSAC with acetamide as a phase change material (PCM) storage. Input data set consist of time, solar intensity and ambient temperature wherever as outlet air temperature of NSAC was considered as output. Experiments were conducted between 9.00 and 24.00 h in June and July 2014 underneath the prevailing atmospheric condition of Kurukshetra (city of the India). After that, experimental results were utilized to train the back propagation neural network (BPNN) to predict the outlet air temperature of NSAC. The results of proposed algorithm show that the BPNN is effective tool for the prediction of responses. The BPNN predicted results are 99% in agreement with the experimental results.

Keywords: Evacuated tube solar air collector, Artificial neural network, Phase change material, solar air collector

Procedia PDF Downloads 116

Authors: David Abrahams

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In October 2016 South Africa deposited its ‘instrument of withdrawal’ from the Rome Statute of the International Criminal Court, with the Secretary-General of the United Nations. The Rome Statute is the founding document of the treaty-based International Criminal Court (ICC). The ICC has jurisdiction to hear cases where crimes against humanity, war crimes and genocide have been committed, on the basis of individual criminal responsibility. It is therefore not surprising that one of the ICCs mandates is to ensure that the sufferings, due to gross human rights violations towards the civilian population is, in principle, brought to an end by punishing those individuals responsible, thus providing justice to the victims. The ICC is unable to effectively fulfill its mandate and thus depends, in part on the willingness of states to assist the Court in its functions. This requires states to ratify the Statute and to domesticate its provisions, depending on whether it is a monist or dualist state. South Africa ratified the Statute in November 2000, and domesticated the Statute in 2002 by virtue of the Implementation of the Rome Statute of the International Criminal Court Act 27 of 2002. South Africa thus remains under an obligation to cooperate with the ICC until the final date of withdrawal, which is October 2017. An AU Summit was hosted by South Africa during June 2015. Omar Al-Bashir, whom the prosecutor of the ICC has indicted on two separate occasions, was invited to the summit. South Africa made an agreement with the AU that it will honour its obligations in terms of its Diplomatic and Immunities Privileges Act of 2001, by granting immunity to all heads of state, including that of Sudan. This decision by South Africa has raised a plethora of questions regarding the status and hierarchy of international laws versus regional laws versus domestic laws. In particular, this paper explores whether a state’s international law treaty obligations may be suspended in favour of, firstly, regional peace (thus safeguarding the security of the civilian population against further atrocities and other gross violations of human rights), and secondly, head of state immunity. This paper also reflects on the effectiveness of the trias politca in South Africa in relation the manner in which South African courts have confirmed South Africa’s failure in fulfilling its obligations in terms of the Rome Statute. A secondary question which will also be explored, is whether the Rome Statute is currently an effective tool in dealing with gross violations of human rights, particularly in a regional African context, given the desire by a number of African states currently party to the Statute, to engage in a mass exodus from the Statute. Finally, the paper concludes with a proposal that there can be no justice for victims of gross human rights violations unless states are serious in playing an instrumental role in bringing an end to impunity in Africa, and that withdrawing from the ICC without an alternative, effective system in place, will simply perpetuate impunity.

Keywords: African Union, diplomatic immunity, impunity, international criminal court, South Africa

Procedia PDF Downloads 522

Authors: Min Shi

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State of the art research with Artificial Neural Networks for the downscaling of General Circulation Models (GCMs) mainly uses back-propagation algorithm as a training approach. This paper introduces another training approach of ANNs, Evolutionary Algorithm. The combined algorithm names neuroevolutionary (NE) algorithm. We investigate and evaluate the use of the NE algorithms in statistical downscaling by generating temperature estimates at interior points given information from a lattice of surrounding locations. The results of our experiments indicate that NE algorithms can be efficient alternative downscaling methods for daily temperatures.

Keywords: temperature, downscaling, artificial neural networks, evolutionary algorithms

Procedia PDF Downloads 345

Authors: Sandesh Achar

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Artificial intelligence is being increasingly incorporated into many applications across various sectors such as health, education, security, and agriculture. Recently, there has been rapid development in cloud computing technology, resulting in AI’s implementation into cloud computing to enhance and optimize the technology service rendered. The deployment of AI in cloud-based applications has brought about autonomous computing, whereby systems achieve stated results without human intervention. Despite the amount of research into autonomous computing, work incorporating AI/ML into cloud computing to enhance its performance and resource allocation remain a fundamental challenge. This paper highlights different manifestations, roles, trends, and challenges related to AI-based cloud computing models. This work reviews and highlights excellent investigations and progress in the domain. Future directions are suggested for leveraging AI/ML in next-generation computing for emerging computing paradigms such as cloud environments. Adopting AI-based algorithms and techniques to increase operational efficiency, cost savings, automation, reducing energy consumption and solving complex cloud computing issues are the major findings outlined in this paper.

Keywords: artificial intelligence, cloud computing, deep learning, machine learning, internet of things

Procedia PDF Downloads 103

Authors: Xiaoyu Sun

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In recent years, the rapid development of Artificial Intelligence (AI) has extensively promoted medicine, pharmaceutical, and other related fields. The medical research and development of artificial intelligence by scientific and commercial organizations are on the fast track. The ethics review is one of the critical procedures of registration to get the products approved and launched. However, the SOPs for ethics review is not enough to guide the healthy and rapid development of artificial intelligence in healthcare in China. Ethical Review Measures for Biomedical Research Involving Human Beings was enacted by the National Health Commission of the People's Republic of China (NHC) on December 1st, 2016. However, from a legislative design perspective, it was neither updated timely nor in line with the trends of AI international development. Therefore, it was great that NHC published a consultation paper on the updated version on March 16th, 2021. Based on the most updated laws and regulations in the States and EU, and in-depth-interviewed 11 subject matter experts in China, including lawmakers, regulators, and key members of ethics review committees, heads of Regulatory Affairs in SaMD industry, and data scientists, several suggestions were proposed on top of the updated version. Although the new version indicated that the Ethics Review Committees need to be created by National, Provincial and individual institute levels, the review authorities of different levels were not clarified. The suggestion is that the precise scope of review authorities for each level should be identified based on Risk Analysis and Management Model, such as the complicated leading technology, gene editing, should be reviewed by National Ethics Review Committees, it will be the job of individual institute Ethics Review Committees to review and approve the clinical study with less risk such as an innovative cream to treat acne. Furthermore, to standardize the research and development of artificial intelligence in healthcare in the age of AI, more clear guidance should be given to data security in the layers of data, algorithm, and application in the process of ethics review. In addition, transparency and responsibility, as two of six principles in the Rome Call for AI Ethics, could be further strengthened in the updated version. It is the shared goal among all countries to manage well and develop AI to benefit human beings. Learned from the other countries who have more learning and experience, China could be one of the most advanced countries in artificial intelligence in healthcare.

Keywords: biomedical research involving human beings, data security, ethics committees, ethical review, medical artificial intelligence

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Authors: Ebere Ellison Obisike, Justina N. Adalikwu-Obisike

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Artificial intelligence (AI) is progressively transforming health and social care. With the rapid invention of various electronic devices, machine learning, and computing systems, the use of AI istraversing many health and social care practices. In this systematic review of journal and grey literature, this study explores how the applications of AI might promote the prevention of micro and macrovascular complications in type 1 diabetic patients. This review focuses on the use of a digitized blood glucose meter and the application of insulin pumps for the effective management of type 1 diabetes in low and middle-income countries. It is projected that the applications of AI may assist individuals with type 1 diabetes to monitor and control their blood glucose level and prevent the early onset of micro and macrovascular complications.

Keywords: artificial intelligence, blood glucose meter, insulin pump, low and middle-income countries, micro and macrovascular complications, type 1 diabetes

Procedia PDF Downloads 183

Authors: Sina Saadati

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Multiprocessors Systems-On-Chips (MPSOCs) are used widely on modern computers to execute sophisticated software and applications. These systems include different processors for distinct aims. Most of the proposed task schedulers attempt to improve energy consumption. In some schedulers, the processor's temperature is considered to increase the system's reliability and performance. In this research, we have proposed a new method for thermal-aware task scheduling which is based on an artificial neural network (ANN). This method enables us to consider a variety of factors in the scheduling process. Some factors like ambient temperature, season (which is important for some embedded systems), speed of the processor, computing type of tasks and have a complex relationship with the final temperature of the system. This Issue can be solved using a machine learning algorithm. Another point is that our solution makes the system intelligent So that It can be adaptive. We have also shown that the computational complexity of the proposed method is cheap. As a consequence, It is also suitable for battery-powered systems.

Keywords: task scheduling, MOSOC, artificial neural network, machine learning, architecture of computers, artificial intelligence

Procedia PDF Downloads 98

Authors: A. M. Khourshid, A. M. El-Kassas, I. Sabry

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The objective of this work was to investigate the mechanical properties in order to demonstrate the feasibility of friction stir welding for joining Al 6061 aluminium alloys. Welding was performed on pipe with different thickness (2, 3 and 4 mm), five rotational speeds (485, 710, 910, 1120 and 1400 rpm) and a traverse speed of 4mm/min. This work focuses on two methods which are artificial neural networks using software and Response Surface Methodology (RSM) to predict the tensile strength, the percentage of elongation and hardness of friction stir welded 6061 aluminium alloy. An Artificial Neural Network (ANN) model was developed for the analysis of the friction stir welding parameters of 6061 pipe. Tensile strength, the percentage of elongation and hardness of weld joints were predicted by taking the parameters tool rotation speed, material thickness and axial force as a function. A comparison was made between measured and predicted data. Response Surface Methodology (RSM) was also developed and the values obtained for the response tensile strength, the percentage of elongation and hardness are compared with measured values. The effect of FSW process parameters on mechanical properties of 6061 aluminium alloy has been analysed in detail.

Keywords: friction stir welding, aluminium alloy, response surface methodology, artificial neural network

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Authors: Sevde Altuntas, Fatih Buyukserin

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Alzheimer’s disease is responsible for irreversible neural damage of brain parts. One of the disease markers is Amyloid-β 1-42 protein that accumulates in the brain in the form plaques. The basic problem for detection of the protein is the low amount of protein that cannot be detected properly in body liquids such as blood, saliva or urine. To solve this problem, tests like ELISA or PCR are proposed which are expensive, require specialized personnel and can contain complex protocols. Therefore, Surface-enhanced Raman Spectroscopy (SERS) a good candidate for detection of Amyloid-β 1-42 protein. Because the spectroscopic technique can potentially allow even single molecule detection from liquid and solid surfaces. Besides SERS signal can be improved by using nanopattern surface and also is specific to molecules. In this context, our study proposes to fabricate diagnostic test models that utilize Au-coated nanopatterned polycarbonate (PC) surfaces modified with Thioflavin - T to detect low concentrations of Amyloid-β 1-42 protein in water and artificial saliva medium by the enhancement of protein SERS signal. The nanopatterned PC surface that was used to enhance SERS signal was fabricated by using Anodic Alumina Membranes (AAM) as a template. It is possible to produce AAMs with different column structures and varying thicknesses depending on voltage and anodization time. After fabrication process, the pore diameter of AAMs can be arranged with dilute acid solution treatment. In this study, two different columns structures were prepared. After a surface modification to decrease their surface energy, AAMs were treated with PC solution. Following the solvent evaporation, nanopatterned PC films with tunable pillared structures were peeled off from the membrane surface. The PC film was then modified with Au and Thioflavin-T for the detection of Amyloid-β 1-42 protein. The protein detection studies were conducted first in water via this biosensor platform. Same measurements were conducted in artificial saliva to detect the presence of Amyloid Amyloid-β 1-42 protein. SEM, SERS and contact angle measurements were carried out for the characterization of different surfaces and further demonstration of the protein attachment. SERS enhancement factor calculations were also completed via experimental results. As a result, our research group fabricated diagnostic test models that utilize Au-coated nanopatterned polycarbonate (PC) surfaces modified with Thioflavin-T to detect low concentrations of Alzheimer’s Amiloid – β protein in water and artificial saliva medium. This work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) Grant No: 214Z167.

Keywords: alzheimer, anodic aluminum oxide, nanotopography, surface enhanced Raman spectroscopy

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Authors: Jyotsna Gupta, S. Ghosh, S. Aravindan

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The requirement of artificial prostheses (such as hip and knee joints) has increased with time. Many researchers are working to develop new implants with improved properties such as excellent biocompatibility with no tissue reactions, corrosion resistance in body fluid, high yield strength and low elastic modulus. Further, the morphological properties of the artificial implants should also match with that of the human bone so that cell adhesion, proliferation and transportation of the minerals and nutrition through body fluid can be obtained. Present study attempts to make porous Ti6Al15Mo alloys through powder metallurgy route using space holder technique. The alloy consists of 6wt% of Al which was taken as α phase stabilizer and 15wt% Mo was taken as β phase stabilizer with theoretical density 4.708. Ammonium hydrogen carbonate is used as a space holder in order to generate the porosity. The porosity of these fabricated porous alloys was controlled by adding the 0, 50, 70 vol.% of the space holder content. Three phases were found in the microstructure: α, α_2 and β phase of titanium. Kirkendall pores are observed to be decreased with increase of holding time during sintering and parallelly compressive strength and elastic modulus value increased slightly. Compressive strength and elastic modulus of porous Ti-6Al-15Mo alloy (1.17 g/cm3 density) is found to be suitable for cancellous bone. Released ions from Ti-6Al-15Mo alloy are far below from the permissible limits in human body.

Keywords: bone implant, powder metallurgy, sintering time, Ti-6Al-15Mo

Procedia PDF Downloads 142

Authors: T. K. Tee, M. N. A. Azman, S. Mohamed, M. Muhammad, M. M. Mohamad, J. Md Yunos, M. H. Yee, W. Othman

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Buzan mind mapping is an efficient system of note-taking that makes revision a fun thing to do for students. Tony Buzan has been teaching children all over the world for the past thirty years and has proved that mind maps are the magic formula in the classroom for everyone. The purpose of this paper is to discuss the importance of Buzan mind mapping as a note-taking technique for the secondary school students. This paper also examines the mind mapping technique, advantages and disadvantages of hand-drawn mind maps. Samples of students’ mind maps were presented and discussed.

Keywords: Buzan mind mapping, note-taking technique, hand-drawn, mind maps

Procedia PDF Downloads 533

Authors: Siraa Ben Ftima, Mourad Talbi, Tahar Ezzedine

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In this paper, we present a technique of secure watermarking of grayscale and color images. This technique consists in applying the Singular Value Decomposition (SVD) in LWT (Lifting Wavelet Transform) domain in order to insert the watermark image (grayscale) in the host image (grayscale or color image). It also uses signature in the embedding and extraction steps. The technique is applied on a number of grayscale and color images. The performance of this technique is proved by the PSNR (Pick Signal to Noise Ratio), the MSE (Mean Square Error) and the SSIM (structural similarity) computations.

Keywords: lifting wavelet transform (LWT), sub-space vectorial decomposition, secure, image watermarking, watermark

Procedia PDF Downloads 272

Authors: Mohammad Arif Khan

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This paper analyzes and presents the development of Artificial Neural Network based controller of space vector modulation (ANN-SVPWM) for a seven-phase voltage source inverter. At first, the conventional method of producing sinusoidal output voltage by utilizing six active and one zero space vectors are used to synthesize the input reference, is elaborated and then new PWM scheme called Artificial Neural Network Based PWM is presented. The ANN based controller has the advantage of the very fast implementation and analyzing the algorithms and avoids the direct computation of trigonometric and non-linear functions. The ANN controller uses the individual training strategy with the fixed weight and supervised models. A computer simulation program has been developed using Matlab/Simulink together with the neural network toolbox for training the ANN-controller. A comparison of the proposed scheme with the conventional scheme is presented based on various performance indices. Extensive Simulation results are provided to validate the findings.

Keywords: space vector PWM, total harmonic distortion, seven-phase, voltage source inverter, multi-phase, artificial neural network

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Authors: S. Mousavian, D. Ashouri, F. Mousavian, V. Nikkhah Rashidabad, N. Ghazinia

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PH, temperature, and time of extraction of each stage, agitation speed, and delay time between stages effect on efficiency of zinc extraction from concentrate. In this research, efficiency of zinc extraction was predicted as a function of mentioned variable by artificial neural networks (ANN). ANN with different layer was employed and the result show that the networks with 8 neurons in hidden layer has good agreement with experimental data.

Keywords: zinc extraction, efficiency, neural networks, operating condition

Procedia PDF Downloads 541

Authors: Zhaofeng Li, Jun Kang Chow, Yu-Hsing Wang

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This paper attempts to bridge the soil properties and the mechanical response of soil in the discrete element method (DEM) simulation. The artificial neural network (ANN) was therefore adopted, aiming to reproduce the stress-strain-volumetric response when soil properties are given. 31 biaxial shearing tests with varying soil parameters (e.g., initial void ratio and interparticle friction coefficient) were generated using the DEM simulations. Based on these 45 sets of training data, a three-layer neural network was established which can output the entire stress-strain-volumetric curve during the shearing process from the input soil parameters. Beyond the training data, 2 additional sets of data were generated to examine the validity of the network, and the stress-strain-volumetric curves for both cases were well reproduced using this network. Overall, the ANN was found promising in predicting the soil behavior and reducing repetitive simulation work.

Keywords: artificial neural network, discrete element method, soil properties, stress-strain-volumetric response

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Authors: Prateeksha Mahamallik, Anjali Pal

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In the present study, Co(II)-adsolubilized surfactant modified alumina (SMA) was prepared, and methylene blue (MB) degradation was carried out on Co-SMA surface by visible light photo-Fenton process. The entire reaction proceeded on solid surface as MB was embedded on Co-SMA surface. The reaction followed zero order kinetics. Response surface methodology (RSM) and artificial neural network (ANN) were used for modeling the decolorization of MB by photo-Fenton process as a function of dose of Co-SMA (10, 20 and 30 g/L), initial concentration of MB (10, 20 and 30 mg/L), concentration of H2O2 (174.4, 348.8 and 523.2 mM) and reaction time (30, 45 and 60 min). The prediction capabilities of both the methodologies (RSM and ANN) were compared on the basis of correlation coefficient (R2), root mean square error (RMSE), standard error of prediction (SEP), relative percent deviation (RPD). Due to lower value of RMSE (1.27), SEP (2.06) and RPD (1.17) and higher value of R2 (0.9966), ANN was proved to be more accurate than RSM in order to predict decolorization efficiency.

Keywords: adsolubilization, artificial neural network, methylene blue, photo-fenton process, response surface methodology

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Authors: Rômulo Eustáquio Martins de Souza, Paulo Alexandre Rodrigues de Vasconcelos Figueiredo

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In recent years, there has been a surge in interest in applying artificial intelligence techniques, particularly machine learning algorithms. Machine learning is a data-analysis technique that automates the creation of analytical models, making it especially useful for designing complex situations. As a result, this technology aids in reducing human intervention while producing accurate results. This methodology is also extensively used in aerospace engineering since this is a field that encompasses several high-complexity operations, such as rocket propulsion. Rocket propulsion is a high-risk operation in which engine failure could result in the loss of life. As a result, it is critical to use computational methods capable of precisely representing the spacecraft's analytical model to guarantee its security and operation. Thus, this paper describes the use of machine learning algorithms for rocket propulsion to aid the realization that this technique is an efficient way to deal with challenging and restrictive aerospace engineering activities. The paper focuses on three machine-learning-aided rocket propulsion applications: set-point control of an expander-bleed rocket engine, supersonic retro-propulsion of a small-scale rocket, and leak detection and isolation on rocket engine data. This paper describes the data-driven methods used for each implementation in depth and presents the obtained results.

Keywords: data analysis, modeling, machine learning, aerospace, rocket propulsion

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Authors: Tzyy-Rong Jinn, Sheng-Kuo Hsieh, Yi-Ching Chung, Feng-Chia Hsieh

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In this study, we attempted to develop a recombinant oleosin-based fusion expression strategy in Escherichia coli (E. coli) and coupled with the artificial oil bodies (AOB)-cyanogen bromide technology platform to produce bioactive mastoparan B (MP-B). As reported, the oleosin in AOB system plays a carrier (fusion with target protein), since oleosin possess two amphipathic regions (at the N-terminus and C-terminus), which result in the N-terminus and C-terminus of oleosin could be arranged on the surface of AOB. Thus, the target protein fused to the N-terminus or C-terminus of oleosin which also is exposed on the surface of AOB, and this process will greatly facilitate the subsequent separation and purification of target protein from AOB. In addition, oleosin, a unique structural protein of seed oil bodies, has the added advantage of helping the fused MP-B expressed in inclusion bodies, which can protect from proteolytic degradation. In this work, MP-B was fused to the C-terminus of oleosin and then was expressed in E. coli as an insoluble recombinant protein. As a consequence, we successfully developed a reliable recombinant oleosin-based fusion expression strategy in Escherichia coli and coupled with the artificial oil bodies (AOB)-cyanogen bromide technology platform to produce the small peptide, MP-B. Take together, this platform provides an insight into the production of active MP-B, which will facilitate studies and applications of this peptide in the future.

Keywords: artificial oil bodies, Escherichia coli, Oleosin-fusion protein, Mastoparan-B

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Authors: Omar M. Elmabrouk., Roaa Y. Taha., Najat M. Ebrahim, Sabbreen A. Mohammed

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Power transformers are the most crucial part of power electrical system, distribution and transmission grid. This part is maintained using predictive or condition-based maintenance approach. The diagnosis of power transformer condition is performed based on Dissolved Gas Analysis (DGA). There are five main methods utilized for analyzing these gases. These methods are International Electrotechnical Commission (IEC) gas ratio, Key Gas, Roger gas ratio, Doernenburg, and Duval Triangle. Moreover, due to the importance of the transformers, there is a need for an accurate technique to diagnose and hence predict the transformer condition. The main objective of this technique is to avoid the transformer faults and hence to maintain the power electrical system, distribution and transmission grid. In this paper, the DGA was utilized based on the data collected from the transformer records available in the General Electricity Company of Libya (GECOL) which is located in Benghazi-Libya. The Fuzzy Logic (FL) technique was implemented as a diagnostic approach based on IEC gas ratio method. The FL technique gave better results and approved to be used as an accurate prediction technique for power transformer faults. Also, this technique is approved to be a quite interesting for the readers and the concern researchers in the area of FL mathematics and power transformer.

Keywords: dissolved gas-in-oil analysis, fuzzy logic, power transformer, prediction

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Authors: Hwayeon Song

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The long-term purpose of research and development (R&D) programs is to strengthen national competitiveness by developing new knowledge and technologies. Thus, it is important to determine a proper budget for government programs to maintain the vigor of R&D when the total funding is tight due to the national deficit. In this regard, a ripple effect analysis for the budgetary changes in R&D programs is necessary as well as an investigation of the current status. This study proposes a new approach using Artificial Neural Networks (ANN) for both tasks. It particularly focuses on R&D programs related to Construction and Transportation (C&T) technology in Korea. First, key factors in C&T technology are explored to draw impact indicators in three areas: economy, society, and science and technology (S&T). Simultaneously, ANN is employed to evaluate the relationship between data variables. From this process, four major components in R&D including research personnel, expenses, management, and equipment are assessed. Then the ripple effect analysis is performed to see the changes in the hypothetical future by modifying current data. Any research findings can offer an alternative strategy about R&D programs as well as a new analysis tool.

Keywords: Artificial Neural Networks, construction and transportation technology, Government Research and Development, Ripple Effect

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Authors: Frederic Jumelle, Kelvin So, Didan Deng

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In this paper, we are introducing a model of artificial general intelligence (AGI), the functional neural network (FNN), for modeling human decision-making processes. The FNN is composed of multiple artificial mirror neurons (AMN) racing in the network. Each AMN has a similar structure programmed independently by the users and composed of an intention wheel, a motor core, and a sensory core racing at a specific velocity. The mathematics of the node’s formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education, and medicine, including the opportunity to design an intelligent learning agent with application in AGI. We believe that FNN has a promising potential to transform the way we can compute decision-making and lead to a new generation of AI chips for seamless human-machine interactions (HMI).

Keywords: neural computing, human machine interation, artificial general intelligence, decision processing

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Authors: Craig A. Kaplan

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If AGI proves to be a “winner-take-all” scenario where the first company or country to develop AGI dominates, then the first AGI must also be the safest. The safest, and fastest, path to Artificial General Intelligence (AGI) may be to harness the collective intelligence of multiple AI and human agents in an AGI network. This approach has roots in seminal ideas from four of the scientists who founded the field of Artificial Intelligence: Allen Newell, Marvin Minsky, Claude Shannon, and Herbert Simon. Extrapolating key insights from these founders of AI, and combining them with the work of modern researchers, results in a fast and safe path to AGI. The seminal ideas discussed are: 1) Society of Mind (Minsky), 2) Information Theory (Shannon), 3) Problem Solving Theory (Newell & Simon), and 4) Bounded Rationality (Simon). Society of Mind describes a collective intelligence approach that can be used with AI and human agents to create an AGI network. Information theory helps address the critical issue of how an AGI system will increase its intelligence over time. Problem Solving Theory provides a universal framework that AI and human agents can use to communicate efficiently, effectively, and safely. Bounded Rationality helps us better understand not only the capabilities of SuperIntelligent AGI but also how humans can remain relevant in a world where the intelligence of AGI vastly exceeds that of its human creators. Each key idea can be combined with recent work in the fields of Artificial Intelligence, Machine Learning, and Large Language Models to accelerate the development of a working, safe, AGI system.

Keywords: AI Agents, Collective Intelligence, Minsky, Newell, Shannon, Simon, AGI, AGI Safety

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Authors: Nelvin Joseph, K. Krishna Milan Rao, Praveen Dwarakanath

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The explosive growth of Smart phones around the world has led to the shift of the primary engagement tool for entertainment from traditional consoles and music players to an all integrated device. Augmented Reality is the next big shift in bringing in a new dimension to the play. The paper explores the construct and working of the community engine in Delta T – an Augmented Reality game that allows users to evolve rules in the game basis collective bargaining mirroring democracy even in a gaming world.

Keywords: augmented reality, artificial neural networks, mobile application, human computer interaction, community engine

Procedia PDF Downloads 328

Authors: Najah Kalifah Almazmomi

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In the time of artificial intelligence (AI), there is a need to know the determinants of success in business management, which are taking on a new dimension. This research purports to scrutinize the Critical Success Factors (CSFs) that drive and ignite the fire of success to help uncover the subtle and profound dynamics that might be operative in organizations. By means of a systematic literature review and a number of empirical methods, the paper is aimed at determining and assessing the key aspects of CSFs, putting emphasis on their role and meaning in the context of AI technology adoption. Some central features such as leadership ways, innovation models, strategic thinking methodologies, organizational culture transformations, and human resource management approaches are compared and contrasted with the AI-driven revolution. Additionally, this research will explore the interactive effects of these factors and their joint impact on the success, survival, and flexibility of a business in the current environment, which is changing due to AI development. Through the use of different qualitative and quantitative methodologies, the research concludes that the findings are significant in understanding the relative roles of individual CSFs and in studying the interactions between them in such an AI-enabled business environment.

Keywords: critical success factors, business and management, artificial intelligence, leadership strategies

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Authors: J. S. Dhillon, K. K. Dhaliwal

Abstract:

In almost all the digital filtering applications the digital infinite impulse response (IIR) filters are preferred over finite impulse response (FIR) filters because they provide much better performance, less computational cost and have smaller memory requirements for similar magnitude specifications. However, the digital IIR filters are generally multimodal with respect to the filter coefficients and therefore, reliable methods that can provide global optimal solutions are required. The artificial bee colony (ABC) algorithm is one such recently introduced meta-heuristic optimization algorithm. But in some cases it shows insufficiency while searching the solution space resulting in a weak exchange of information and hence is not able to return better solutions. To overcome this deficiency, the opposition based learning strategy is incorporated in ABC and hence a modified version called oppositional artificial bee colony (OABC) algorithm is proposed in this paper. Duplication of members is avoided during the run which also augments the exploration ability. The developed algorithm is then applied for the design of optimal and stable digital IIR filter structure where design of low-pass (LP) and high-pass (HP) filters is carried out. Fuzzy theory is applied to achieve maximize satisfaction of minimum magnitude error and stability constraints. To check the effectiveness of OABC, the results are compared with some well established filter design techniques and it is observed that in most cases OABC returns better or atleast comparable results.

Keywords: digital infinite impulse response filter, artificial bee colony optimization, opposition based learning, digital filter design, multi-parameter optimization

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Authors: Raquel M. De sousa, Sofiane Labidi, Allan Kardec D. Barros, Alex O. Barradas Filho, Aldalea L. B. Marques

Abstract:

Several parameters are established in order to measure biodiesel quality. One of them is the iodine value, which is an important parameter that measures the total unsaturation within a mixture of fatty acids. Limitation of unsaturated fatty acids is necessary since warming of a higher quantity of these ones ends in either formation of deposits inside the motor or damage of lubricant. Determination of iodine value by official procedure tends to be very laborious, with high costs and toxicity of the reagents, this study uses an artificial neural network (ANN) in order to predict the iodine value property as an alternative to these problems. The methodology of development of networks used 13 esters of fatty acids in the input with convergence algorithms of backpropagation type were optimized in order to get an architecture of prediction of iodine value. This study allowed us to demonstrate the neural networks’ ability to learn the correlation between biodiesel quality properties, in this case iodine value, and the molecular structures that make it up. The model developed in the study reached a correlation coefficient (R) of 0.99 for both network validation and network simulation, with Levenberg-Maquardt algorithm.

Keywords: artificial neural networks, biodiesel, iodine value, prediction

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Authors: D. K. Rao, M. G. M. Khan, K. G. Reddy

Abstract:

The focus of this paper is to develop a technique of solving a combined problem of determining Optimum Strata Boundaries (OSB) and Optimum Sample Size (OSS) of each stratum, when the population understudy is skewed and the study variable has a Pareto frequency distribution. The problem of determining the OSB is formulated as a Mathematical Programming Problem (MPP) which is then solved by dynamic programming technique. A numerical example is presented to illustrate the computational details of the proposed method. The proposed technique is useful to obtain OSB and OSS for a Pareto type skewed population, which minimizes the variance of the estimate of population mean.

Keywords: stratified sampling, optimum strata boundaries, optimum sample size, pareto distribution, mathematical programming problem, dynamic programming technique

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

Abstract:

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

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

Procedia PDF Downloads 57