Search results for: Artificial stock markets

Authors: J. Fernandez de Canete, S. Gonzalez-Perez, P. del Saz-Orozco

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LABVIEW is a graphical programming language that has its roots in automation control and data acquisition. In this paper we have utilized this platform to provide a powerful toolset for process identification and control of nonlinear systems based on artificial neural networks (ANN). This tool has been applied to the monitoring and control of a lab-scale distillation column DELTALAB DC-SP. The proposed control scheme offers high speed of response for changes in set points and null stationary error for dual composition control and shows robustness in presence of externally imposed disturbance.

Keywords: Distillation, neural networks, LABVIEW, monitoring, identification, control.

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Authors: Nhon Do

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Ontology is a terminology which is used in artificial intelligence with different meanings. Ontology researching has an important role in computer science and practical applications, especially distributed knowledge systems. In this paper we present an ontology which is called Computational Object Knowledge Base Ontology. It has been used in designing some knowledge base systems for solving problems such as the system that supports studying knowledge and solving analytic geometry problems, the program for studying and solving problems in Plane Geometry, the knowledge system in linear algebra.

Keywords: Artificial intelligence, knowledge representation, knowledge base system, ontology.

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Authors: Mohsen Hayati, Zahra Mohebi

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In this paper, the application of neural networks to study the design of short-term temperature forecasting (STTF) Systems for Kermanshah city, west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STTF systems is used. Our study based on MLP was trained and tested using ten years (1996-2006) meteorological data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STTF systems.

Keywords: Artificial neural networks, Forecasting, Weather, Multi-layer perceptron.

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Authors: Ahmad Sharieh, R Bremananth

Abstract:

Artificial Intelligence based gaming is an interesting topic in the state-of-art technology. This paper presents an automation of a tradition Omani game, called Al-Hawalees. Its related issues are resolved and implemented using artificial intelligence approach. An AI approach called mini-max procedure is incorporated to make a diverse budges of the on-line gaming. If number of moves increase, time complexity will be increased in terms of propositionally. In order to tackle the time and space complexities, we have employed a back propagation neural network (BPNN) to train in off-line to make a decision for resources required to fulfill the automation of the game. We have utilized Leverberg- Marquardt training in order to get the rapid response during the gaming. A set of optimal moves is determined by the on-line back propagation training fashioned with alpha-beta pruning. The results and analyses reveal that the proposed scheme will be easily incorporated in the on-line scenario with one player against the system.

Keywords: Artificial neural network, back propagation gaming, Leverberg-Marquardt, minimax procedure.

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Authors: N. Sumathi, R. Srinivasa Rao

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This paper presents the experimental results of 11 kV and 33 kV silicon composite insulators under artificial salt and urea polluted conditions. The tests were carried out under different seasons like summer, winter, and monsoon. The artificial pollution is prepared by properly dissolving the salt and urea in the water. The prepared salt and urea pollutions are sprayed on the insulators and dried up for sufficiently large time. The process is continued until a uniform layer is formed on the surface of insulator. For each insulator rating, four samples were tested. The maximum leakage current and breakdown voltage were measured. From experimental data, performance of test specimen is evaluated by comparing breakdown voltage and leakage current during different seasons when exposed to salt and urea polluted conditions. From these results the performance of the insulators can be predicted when they are installed in industrial, agricultural, and coastal areas. The experimental tests were carried out in the High Voltage laboratory using two stage cascade transformer having the rating of 1000 kVA, 500 kV.

Keywords: Silicon composite insulators, Urea pollution, Leakage current, Breakdown voltage, salt pollution, artificial pollution.

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

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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 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 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 back propagation of back propagation 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 caseiodine 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: Taiwo. M. Akinmuyisitan, John Cosmas

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This paper practically demonstrates the theories and concepts of an Advanced Convolutional Neural Network in the design and development of a scalable artificial intelligence model for the detection of criminal masterminds. The technique uses machine vision algorithms to compute the facial characteristics of suspects and classify actors as criminal or non-criminal faces. The paper proceeds further to compare the results of the error accuracy of two popular custom convolutional pre-trained networks, VGG16 and Resnet50. The result shows that VGG16 is probably more efficient than ResNet50 for the dataset we used.

Keywords: Artificial intelligence, convolutional neural networks, Resnet50, VGG16.

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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: Sandesh Achar

Abstract:

Artificial intelligence (AI) 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 remains a fundamental challenge. This paper highlights different manifestations, roles, trends, and challenges related to AI-based cloud computing models. This work reviews and highlights 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, AI, cloud computing, deep learning, machine learning, ML, internet of things, IoT.

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Authors: Mohammed Benbrahim, Khalid Benjelloun, Aomar Ibenbrahim, Adil Daoudi

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The automatic classification of non stationary signals is an important practical goal in several domains. An essential classification task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, we present a modular system composed by three blocs: 1) Representation, 2) Dimensionality reduction and 3) Classification. The originality of our work consists in the use of a new wavelet called "Ben wavelet" in the representation stage. For the dimensionality reduction, we propose a new algorithm based on the random projection and the principal component analysis.

Keywords: Seismic signals, Ben Wavelet, Dimensionality reduction, Artificial neural networks, Classification.

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Authors: Mei Shan Ngan, Chee Wei Tan

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Due to the non-linear characteristics of photovoltaic (PV) array, PV systems typically are equipped with the capability of maximum power point tracking (MPPT) feature. Moreover, in the case of PV array under partially shaded conditions, hotspot problem will occur which could damage the PV cells. Partial shading causes multiple peaks in the P-V characteristic curves. This paper presents a hybrid algorithm of Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) MPPT algorithm for the detection of global peak among the multiple peaks in order to extract the true maximum energy from PV panel. The PV system consists of PV array, dc-dc boost converter controlled by the proposed MPPT algorithm and a resistive load. The system was simulated using MATLAB/Simulink package. The simulation results show that the proposed algorithm performs well to detect the true global peak power. The results of the simulations are analyzed and discussed.

Keywords: Photovoltaic (PV), Partial Shading, Maximum Power Point Tracking (MPPT), Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN)

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Authors: Rachid.Dehini, Abdesselam.Bassou, Brahim.Ferdi

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Active Power Filters (APFs) are today the most widely used systems to eliminate harmonics compensate power factor and correct unbalanced problems in industrial power plants. We propose to improve the performances of conventional APFs by using artificial neural networks (ANNs) for harmonics estimation. This new method combines both the strategies for extracting the three-phase reference currents for active power filters and DC link voltage control method. The ANNs learning capabilities to adaptively choose the power system parameters for both to compute the reference currents and to recharge the capacitor value requested by VDC voltage in order to ensure suitable transit of powers to supply the inverter. To investigate the performance of this identification method, the study has been accomplished using simulation with the MATLAB Simulink Power System Toolbox. The simulation study results of the new (SAPF) identification technique compared to other similar methods are found quite satisfactory by assuring good filtering characteristics and high system stability.

Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total Harmonic Distortion.

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Authors: Allaeddine Athmani, Abdelhacine Gouasmia, Tiago Ferreira, Romeu Vicente

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The seismic risk mitigation from the perspective of the old buildings stock is truly essential in Algerian urban areas, particularly those located in seismic prone regions, such as Annaba city, and which the old buildings present high levels of degradation associated with no seismic strengthening and/or rehabilitation concerns. In this sense, the present paper approaches the issue of the seismic vulnerability assessment of old masonry building stocks through the adaptation of a simplified methodology developed for a European context area similar to that of Annaba city, Algeria. Therefore, this method is used for the first level of seismic vulnerability assessment of the masonry buildings stock of the old city center of Annaba. This methodology is based on a vulnerability index that is suitable for the evaluation of damage and for the creation of large-scale loss scenarios. Over 380 buildings were evaluated in accordance with the referred methodology and the results obtained were then integrated into a Geographical Information System (GIS) tool. Such results can be used by the Annaba city council for supporting management decisions, based on a global view of the site under analysis, which led to more accurate and faster decisions for the risk mitigation strategies and rehabilitation plans.

Keywords: Damage scenarios, masonry buildings, old city center, seismic vulnerability, vulnerability index.

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Authors: Naoto Suzuki

Abstract:

Japanese are affected by the most common causes of eyesight loss such as glaucoma, diabetic retinopathy, pigmentary retinal degeneration, and age-related macular degeneration. We developed an ophthalmological examination apparatus with a fundus camera, precisely fundus-related perimetry (microperimetry), and electroretinogram (ERG) functions to diagnose a variety of diseases that cause eyesight loss. The experimental apparatus was constructed with the same optical system as a fundus camera. The microperimetry optical system was calculated and added to the experimental apparatus using the German company Optenso's optical engineering software (OpTaliX-LT 10.8). We also added an Edmund infrared camera (EO-0413), a lens with a 25 mm focal length, a 45° cold mirror, a 12 V/50 W halogen lamp, and an 8-inch monitor. We made the artificial eye of a plane-convex lens, a black spacer, and a hemispherical cup. The hemispherical cup had a small section of the paper at the bottom. The artificial eye was photographed five times using the experimental apparatus. The software was created to display the examination target on the monitor and save examination data using C++Builder 10.2. The retinal fundus was displayed on the monitor at a length and width of 1 mm and a resolution of 70.4 ± 4.1 and 74.7 ± 6.8 pixels, respectively. The microperimetry and ERG functions were successfully added to the experimental ophthalmological apparatus. A moving machine was developed to measure the artificial eye's movement. The artificial eye's rear part was painted black and white in the central area. It was rotated 10 degrees from one side to the other. The movement was captured five times as motion videos. Three static images were extracted from one of the motion videos captured. The images display the artificial eye facing the center, right, and left directions. The three images were processed using Scilab 6.1.0 and Image Processing and Computer Vision Toolbox 4.1.2, including trimming, binarization, making a window, deleting peripheral area, and morphological operations. To calculate the artificial eye's fundus center, we added a gravity method to the program to calculate the gravity position of connected components. From the three images, the image processing could calculate the center position.

Keywords: Ophthalmological examination apparatus, microperimetry, electroretinogram, eye movement.

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Authors: O. O. Olawusi-Peters, K. I. Adejugbagbe

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The concentration of heavy metal (Cd, Pb, Fe, Zn, Cu) in Clarias gariepinus collected from fish markets; Fanibi (Station I) and Fiwasaye (Station II) in Akure metropolis, Ondo state, Nigeria were investigated to ascertain the safety for the consumers. 60 samples were collected from the two markets in three batches (I, II, III) for a period of six months and analyzed for heavy metals in the gills and muscles of the fish. Also, the Health Risk Index (HRI) was used to determine the health risk of these metals to the consumer. The results showed that the investigated metal concentration was higher in station I than station II, except Pb having higher concentration in station II than station I. In both stations, the highest concentration of Fe was recorded in the gills (12.60 ± 1.51; 6.94 ± 1.38) and muscles (3.72 ± 0.09; 3.86 ± 0.33) of samples in batch I. Also, the HRI revealed that consumption of Clarias gariepinus from these study areas did not pose any health risk (HRI < 1). In addition, concentrations of the heavy metals were all below the permissible limits recommended by FAO/WHO.

Keywords: Health risk index, heavy metals, Clarias gariepinus, Akure metropolis, fish monger.

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Authors: Ahmad M. Sarhan, Omar I. Al Helalat

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In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addition to the input matrix, are fed to an ANN. Simulation results are provided and show that the proposed system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions.

Keywords: ANN, Backpropagation, Gaussian, LMS, MSE, Neuron, standard deviation, Widrow-Hoff rule.

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Authors: Diah Tri Widayati, Aris Junaidi, Kresno Suharto, Amelia Oktaviani, Wahyuningsih

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The estrus female Etawah cross bred goats were synchronized estrus by controlled internal drug release (CIDR) implants for 10 days combined with PGF2α injection, and continued by artificial insemination (AI) within the hours of 24 period. Vaginal epithelium was taken to determine estrus cycle of the goats without estrus synchronization. The estrus responds (the puffy of vulva and vaginal pH) and percentage of pregnancy were investigated. The data were analyzed descriptively and Independent Sample T-Test. The results showed that the puffy of vulva and vaginal pH were significantly different in synchronized estrus goats and control goats (2.18 ± 0.33 cm vs. 1.20 ± 0.16 cm and 8.55 ± 0.63 vs. 8.22 ± 0.22). Percentage of pregnancy was higher in synchronized estrus goats (73.33%) than in control (53.3%). Estrus synchronization by using CIDR implants and PGF2, continued by AI was effective to improve reproduction performance of Etawah cross bred goats.

Keywords: Artificial insemination, Estrus synchronization, Etawah cross bred goat, Reproduction performance.

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Authors: Giovanni Luca Masala

Abstract:

Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Keywords: Computer Aided Detection, mammary tumor, pattern recognition, thalassemia.

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Authors: M. Jazouli, S. Elhoufi, A. Majda, A. Zarghili, R. Aalouane

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Autism spectrum disorder is a complex developmental disability. It is defined by a certain set of behaviors. Persons with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. The objective of this article is to propose a method to automatically detect this unusual behavior. Our study provides a clinical tool which facilitates for doctors the diagnosis of ASD. We focus on automatic identification of five repetitive gestures among autistic children in real time: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. In this paper, we present a gesture recognition system for children with autism, which consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using artificial neural network (ANN). The first one uses the Microsoft Kinect sensor, the second one chooses points of interest from the 3D skeleton to characterize the gestures, and the last one proposes a neural connectionist model to perform the supervised classification of data. The experimental results show that our system can achieve above 93.3% recognition rate.

Keywords: ASD, stereotypical motor movements, repetitive gesture, kinect, artificial neural network.

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Authors: T. Magrakvelidze, N. Bantsadze, N. Lekveishvili, Kh. Lomidze

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The results of an experimental study of the process of convective and boiling heat transfer in the vessel with stirrer for smooth and rough ring-shaped pipes are presented. It is established that creation of two-dimensional artificial roughness on the heated surface causes the essential (~100%) intensification of convective heat transfer. In case of boiling the influence of roughness appears on the initial stage of boiling and in case of fully developed nucleate boiling there was no intensification of heat transfer. The similitude equation for calculating convective heat transfer coefficient, which generalizes well experimental data both for the smooth and the rough surfaces is proposed.

Keywords: boiling, heat transfer, roughness.

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Authors: Vishal Naranje, Shailendra Kumar

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Metal stamping die design is a complex, experiencebased and time-consuming task. Various artificial intelligence (AI) techniques are being used by worldwide researchers for stamping die design to reduce complexity, dependence on human expertise and time taken in design process as well as to improve design efficiency. In this paper a comprehensive review of applications of AI techniques in manufacturability evaluation of sheet metal parts, die design and process planning of metal stamping die is presented. Further the salient features of major research work published in the area of metal stamping are presented in tabular form and scope of future research work is identified.

Keywords: Artificial Intelligence, Die design, ManufacturabilityEvaluation, Metal Stamping Die.

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

Abstract:

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.

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Authors: G. Erkomaishvili

Abstract:

The paper discusses mineral water consumer market and development policy in Georgia, the tools and measures, which will contribute to production of mineral waters and increase its export. The paper studies and analyses current situation in mineral water production sector as well as the factors affecting increase and reduction of its export. It’s noted that in order to gain and maintain competitive advantage, it’s necessary to provide continuous supply of high quality goods with modern design, open new distribution channels to enter new markets, carry out broad promotional activities, organize e-commerce. Economic policy plays an important role in protecting markets from counterfeit goods. The state also plays an important role in attracting foreign direct investments. Stable business environment and export oriented strategy is the basis for the country’s economic growth. Based on the research, the paper suggests the strategy for improving competitiveness of Georgian mineral waters; relevant conclusions and recommendations are provided.

Keywords: Mineral waters, consumer market for mineral waters, export of mineral waters, mineral water development policy in Georgia.

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Authors: Nipun Manirajan, Heeralal Gargama, Sushil Guhe, Manoj Prabhakaran

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In railway industry, train sets are designed based on contractual requirements (mission profile), where reliability targets are measured in terms of mean distance between failures (MDBF). However, during the beginning of revenue services, trains do not achieve the designed mission profile distance (mileage) within the timeframe due to infrastructure constraints, scarcity of commuters or other operational challenges thereby not respecting the original design inputs. Since trains do not run sufficiently and do not achieve the designed mileage within the specified time, car builder has a risk of not achieving the contractual MDBF target. This paper proposes a constant failure rate based model to deal with the situations where mileage accumulation is not a part of the design mission profile. The model provides appropriate MDBF target to be demonstrated based on actual accumulated mileage. A case study of rolling stock running in the field is undertaken to analyze the failure data and MDBF target demonstration during low mileage accumulation. The results of case study prove that with the proposed method, reliability targets are achieved under low mileage accumulation.

Keywords: Mean distance between failures, mileage based reliability, reliability target normalization, rolling stock reliability.

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Authors: Nidhi Sharma, V. K. Banga

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Nowadays, driving support systems, such as car navigation systems, are getting common, and they support drivers in several aspects. It is important for driving support systems to detect status of driver's consciousness. Particularly, detecting driver's drowsiness could prevent drivers from collisions caused by drowsy driving. In this paper, we discuss the various artificial detection methods for detecting driver's drowsiness processing technique. This system is based on facial images analysis for warning the driver of drowsiness or in attention to prevent traffic accidents.

Keywords: Neuro-Fuzzy Model, Halstead Model, Walston-FelixModel, Bailey-Basili Model, Doty Model, GA Based Model, GeneticAlgorithm.

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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, delay-tap sampling, optical signal-to-noise ratio, OSNR.

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Authors: Shah Rizam M. S. B., Farah Yasmin A.R., Ahmad Ihsan M. Y., Shazana K.

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Agriculture products are being more demanding in market today. To increase its productivity, automation to produce these products will be very helpful. The purpose of this work is to measure and determine the ripeness and quality of watermelon. The textures on watermelon skin will be captured using digital camera. These images will be filtered using image processing technique. All these information gathered will be trained using ANN to determine the watermelon ripeness accuracy. Initial results showed that the best model has produced percentage accuracy of 86.51%, when measured at 32 hidden units with a balanced percentage rate of training dataset.

Keywords: Artificial Neural Network (ANN), Digital ImageProcessing, YCbCr Colour Space, Watermelon Ripeness.

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Authors: Hyunchul Ahn, Kyoung-Jae Kim

Abstract:

Corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has been one of the attractive research topics in the literature. In recent years, multiclass classification models such as artificial neural network (ANN) or multiclass support vector machine (MSVM) have become a very appealing machine learning approaches due to their good performance. However, most of them have only focused on classifying samples into nominal categories, thus the unique characteristic of the credit rating - ordinality - has been seldom considered in their approaches. This study proposes new types of ANN and MSVM classifiers, which are named OMANN and OMSVM respectively. OMANN and OMSVM are designed to extend binary ANN or SVM classifiers by applying ordinal pairwise partitioning (OPP) strategy. These models can handle ordinal multiple classes efficiently and effectively. To validate the usefulness of these two models, we applied them to the real-world bond rating case. We compared the results of our models to those of conventional approaches. The experimental results showed that our proposed models improve classification accuracy in comparison to typical multiclass classification techniques with the reduced computation resource.

Keywords: Artificial neural network, Corporate credit rating, Support vector machines, Ordinal pairwise partitioning

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Authors: Mahshid Talebian Kiakalayeh

Abstract:

As artificial intelligence (AI) technologies can be used by both civilians and soldiers; it is vital to consider the consequences emanating from AI military as well as civilian use. Indeed, many of the same technologies can have a dual-use. This paper will explore the military uses of AI and assess their compliance with international legal norms. AI developments not only have changed the capacity of the military to conduct complex operations but have also increased legal concerns. The existence of a potential legal vacuum in legal principles on the military use of AI indicates the necessity of more study on compliance with International Humanitarian Law (IHL), the branch of international law which governs the conduct of hostilities. While capabilities of new means of military AI continue to advance at incredible rates, this body of law is seeking to limit the methods of warfare protecting civilian persons who are not participating in an armed conflict. Implementing AI in the military realm would result in potential issues including ethical and legal challenges. For instance, when intelligence can perform any warfare task without any human involvement, a range of humanitarian debates will be raised as to whether this technology might distinguish between military and civilian targets or not. This is mainly because AI in fully military systems would not seem to carry legal and ethical judgment which can interfere with IHL principles. The paper will take, as a case study, Canada’s compliance with IHL in the area of AI and the related legal issues that are likely to arise as this country continues to develop military uses of AI.

Keywords: Artificial intelligence, military use, International Humanitarian Law, the Canadian perspective.

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Authors: David Jones, Richard McWilliam, Alan Purvis

Abstract:

Morphogenesis is the process that underpins the selforganised development and regeneration of biological systems. The ability to mimick morphogenesis in artificial systems has great potential for many engineering applications, including production of biological tissue, design of robust electronic systems and the co-ordination of parallel computing. Previous attempts to mimick these complex dynamics within artificial systems have relied upon the use of evolutionary algorithms that have limited their size and complexity. This paper will present some insight into the underlying dynamics of morphogenesis, then show how to, without the assistance of evolutionary algorithms, design cellular architectures that converge to complex patterns.

Keywords: Morphogenesis, regeneration, robustness, convergence, cellular automata.

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