Search results for: Social network sites

Authors: Hafiz Gulfam Ahmad, Chuangdong Li, Zeeshan Ahmad

Abstract:

In this paper, we have proposed a parallel IDS and honeypot based approach to detect and analyze the unknown and known attack taxonomy for improving the IDS performance and protecting the network from intruders. The main theme of our approach is to record and analyze the intruder activities by using both the low and high interaction honeypots. Our architecture aims to achieve the required goals by combing signature based IDS, honeypots and generate the new signatures. The paper describes the basic component, design and implementation of this approach and also demonstrates the effectiveness of this approach to reduce the probability of network attacks.

Keywords: Network security, Intrusion detection, Honeypot, Snort, Nmap.

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Authors: Mojca Ciglaric, Andrej Krevl, Matjaž Pancur, Tone Vidmar

Abstract:

P2P Networks are highly dynamic structures since their nodes – peer users keep joining and leaving continuously. In the paper, we study the effects of network change rates on query routing efficiency. First we describe some background and an abstract system model. The chosen routing technique makes use of cached metadata from previous answer messages and also employs a mechanism for broken path detection and metadata maintenance. Several metrics are used to show that the protocol behaves quite well even with high rate of node departures, but above a certain threshold it literally breaks down and exhibits considerable efficiency degradation.

Keywords: Network dynamics, overlay network, P2P system, routing efficiency.

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Authors: D. Singh, R. Yousefi, M. Boroushaki

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Intelligent deep-drawing is an instrumental research field in sheet metal forming. A set of 28 different experimental data have been employed in this paper, investigating the roles of die radius, punch radius, friction coefficients and drawing ratios for axisymmetric workpieces deep drawing. This paper focuses an evolutionary neural network, specifically, error back propagation in collaboration with genetic algorithm. The neural network encompasses a number of different functional nodes defined through the established principles. The input parameters, i.e., punch radii, die radii, friction coefficients and drawing ratios are set to the network; thereafter, the material outputs at two critical points are accurately calculated. The output of the network is used to establish the best parameters leading to the most uniform thickness in the product via the genetic algorithm. This research achieved satisfactory results based on demonstration of neural networks.

Keywords: Deep-drawing, Neural network, Genetic algorithm, Sheet metal forming.

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Authors: S. Montenegro, W. Hu

Abstract:

The objective of this paper is to develop a neural network-based residual generator to detect the fault in the actuators for a specific communication satellite in its attitude control system (ACS). First, a dynamic multilayer perceptron network with dynamic neurons is used, those neurons correspond a second order linear Infinite Impulse Response (IIR) filter and a nonlinear activation function with adjustable parameters. Second, the parameters from the network are adjusted to minimize a performance index specified by the output estimated error, with the given input-output data collected from the specific ACS. Then, the proposed dynamic neural network is trained and applied for detecting the faults injected to the wheel, which is the main actuator in the normal mode for the communication satellite. Then the performance and capabilities of the proposed network were tested and compared with a conventional model-based observer residual, showing the differences between these two methods, and indicating the benefit of the proposed algorithm to know the real status of the momentum wheel. Finally, the application of the methods in a satellite ground station is discussed.

Keywords: Satellite, Attitude Control, Momentum Wheel, Neural Network, Fault Detection.

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Authors: Mario Gonzalez, David Dominguez, Francisco B. Rodriguez

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The model of neural networks on the small-world topology, with metric (local and random connectivity) is investigated. The synaptic weights are random, driving the network towards a chaotic state for the neural activity. An ordered macroscopic neuron state is induced by a bias in the network connections. When the connections are mainly local, the network emulates a block-like structure. It is found that the topology and the bias compete to influence the network to evolve into a global or a block activity ordering, according to the initial conditions.

Keywords: Block attractor, random interaction, small world, spin glass.

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Authors: Jung-Hyun Kim, Jihyung Kim, Kwangjae Lim, Dong Seung Kwon

Abstract:

Network coding has recently attracted attention as an efficient technique in multicast/broadcast services. The problem of finding the optimal network coding mechanism maximizing the bandwidth efficiency is hard to solve and hard to approximate. Lots of network coding-based schemes have been suggested in the literature to improve the bandwidth efficiency, especially network coding-based automatic repeat request (NCARQ) schemes. However, existing schemes have several limitations which cause the performance degradation in resource limited systems. To improve the performance in resource limited systems, we propose NCARQ with overlapping selection (OS-NCARQ) scheme. The advantages of OS-NCARQ scheme over the traditional ARQ scheme and existing NCARQ schemes are shown through the analysis and simulations.

Keywords: ARQ, Network coding, Multicast/Broadcast services, Packet-based systems.

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Authors: E. Nemati Lay, M. Peymani, E. Sanjari

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Prediction of viscosity of natural gas is an important parameter in the energy industries such as natural gas storage and transportation. In this study viscosity of different compositions of natural gas is modeled by using an artificial neural network (ANN) based on back-propagation method. A reliable database including more than 3841 experimental data of viscosity for testing and training of ANN is used. The designed neural network can predict the natural gas viscosity using pseudo-reduced pressure and pseudo-reduced temperature with AARD% of 0.221. The accuracy of designed ANN has been compared to other published empirical models. The comparison indicates that the proposed method can provide accurate results.

Keywords: Artificial neural network, Empirical correlation, Natural gas, Viscosity

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Authors: Jouhra Dad, Mohammed Ouali, Yahia Lebbah

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Urban road network traffic has become one of the most studied research topics in the last decades. This is mainly due to the enlargement of the cities and the growing number of motor vehicles traveling in this road network. One of the most sensitive problems is to verify if the network is congestion-free. Another related problem is the automatic reconfiguration of the network without building new roads to alleviate congestions. These problems require an accurate model of the traffic to determine the steady state of the system. An alternative is to simulate the traffic to see if there are congestions and when and where they occur. One key issue is to find an adequate model for road intersections. Once the model established, either a large scale model is built or the intersection is represented by its performance measures and simulation for analysis. In both cases, it is important to seek the queueing model to represent the road intersection. In this paper, we propose to model the road intersection as a BCMP queueing network and we compare this analytical model against a simulation model for validation.

Keywords: Queueing theory, transportation systems, BCMPqueueing network, performance measures, modeling, simulation

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision making has not been far-fetched. Proper classification of these textual information in a given context has also been very difficult. As a result, a systematic review was conducted from previous literature on sentiment classification and AI-based techniques. The study was done in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that could correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy using the knowledge gain from the evaluation of different artificial intelligence techniques reviewed. The study evaluated over 250 articles from digital sources like ACM digital library, Google Scholar, and IEEE Xplore; and whittled down the number of research to 52 articles. Findings revealed that deep learning approaches such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Bidirectional Encoder Representations from Transformer (BERT), and Long Short-Term Memory (LSTM) outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also required to develop a robust sentiment classifier. Results also revealed that data can be obtained from places like Twitter, movie reviews, Kaggle, Stanford Sentiment Treebank (SST), and SemEval Task4 based on the required domain. The hybrid deep learning techniques like CNN+LSTM, CNN+ Gated Recurrent Unit (GRU), CNN+BERT outperformed single deep learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of development simplicity and AI-based library functionalities. Finally, the study recommended the findings obtained for building robust sentiment classifier in the future.

Keywords: Artificial Intelligence, Natural Language Processing, Sentiment Analysis, Social Network, Text.

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Authors: Maria Spanoudaki, Pauldy C. J. Otermans, Dev Aditya

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Relationships form an integral part of our everyday wellbeing. In this study, the focus is on Inner Wellbeing which can be described as an individuals' thoughts and feelings about what they can do and be. Relationships can come in many forms and can be divided into Social Connections (thoughts and feelings about the social network people can establish and rely on), and Close Relationships (thoughts and feeling about the emotional support people can receive from significant others or their close, intimate circle). The purpose of this study is to compare the Social Connections and Close Relationship dimensions of Inner Wellbeing during the COVID-19 pandemic between the UK and India. As part of the study, 392 participants in the UK and 205 participants India completed an online questionnaire using the Inner Wellbeing scale. Factor analyses showed that the construct of Inner Wellbeing can be described as one factor for the UK sample whereas it can be described as two factors (one focusing on positive items and one focusing on negative items) for the Indian sample. Results showed that during COVID-19, Social Connections were significantly different in the UK compared to India, whereas there is no significant difference for Close Relationships. The implications on relationships and wellbeing are discussed in detail.

Keywords: Social networks, relationship maintenance, relationship satisfaction, inner wellbeing.

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Authors: A.Muthuramalingam, S.Himavathi

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Space Vector Modulation (SVM) is an optimum Pulse Width Modulation (PWM) technique for an inverter used in a variable frequency drive applications. It is computationally rigorous and hence limits the inverter switching frequency. Increase in switching frequency can be achieved using Neural Network (NN) based SVM, implemented on application specific chips. This paper proposes a neural network based SVM technique for a Voltage Source Inverter (VSI). The network proposed is independent of switching frequency. Different architectures are investigated keeping the total number of neurons constant. The performance of the inverter is compared for various switching frequencies for different architectures of NN based SVM. From the results obtained, the network with minimum resource and appropriate word length is identified. The bit precision required for this application is identified. The network with 8-bit precision is implemented in the IC XCV 400 and the results are presented. The performance of NN based general purpose SVM with higher bit precision is discussed.

Keywords: NN based SVM, FPGA Implementation, LayerMultiplexing, NN structure and Resource Reduction, PerformanceEvaluation

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Authors: Krisztina Szegedi, Gyula Fülöp, Ádám Bereczk

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The shared goal of social entrepreneurship, corporate social responsibility and social innovation is the advancement of society. The business model of social enterprises is characterized by unique strategies based on the competencies of the entrepreneurs, and is not aimed primarily at the maximization of profits, but rather at carrying out goals for the benefit of society. Corporate social responsibility refers to the active behavior of a company, by which it can create new solutions to meet the needs of society, either on its own or in cooperation with other social stakeholders. The objectives of this article are to define concepts, describe and integrate relevant theoretical models, develop a model and introduce some examples of international practice that can inspire initiatives for social development.

Keywords: Corporate social responsibility, CSR, social innovation, social entrepreneurship.

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Authors: Pallavi Bhogaram, Xiaolong Wu, Min He, Onyedikachi Okenwa

Abstract:

A transportation network is a realization of a spatial network, describing a structure which permits either vehicular movement or flow of some commodity. Examples include road networks, railways, air routes, pipelines, and many more. The transportation network plays a vital role in maintaining the vigor of the nation’s economy. Hence, ensuring the network stays resilient all the time, especially in the face of challenges such as heavy traffic loads and large scale natural disasters, is of utmost importance. In this paper, we used the Neo4j application to develop the graph. Neo4j is the world's leading open-source, NoSQL, a native graph database that implements an ACID-compliant transactional backend to applications. The Southern California network model is developed using the Neo4j application and obtained the most critical and optimal nodes and paths in the network using centrality algorithms. The edge betweenness centrality algorithm calculates the critical or optimal paths using Yen's k-shortest paths algorithm, and the node betweenness centrality algorithm calculates the amount of influence a node has over the network. The preliminary study results confirm that the Neo4j application can be a suitable tool to study the important nodes and the critical paths for the major congested metropolitan area.

Keywords: Transportation network, critical path, connectivity reliability, network model, Neo4J application, optimal path, critical path, edge betweenness centrality index, node betweenness centrality index, Yen’s k-shortest paths.

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Authors: D. P. Sharma, A. Chaturvedi, G.Purohit , R.Shivarudraswamy

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In today scenario, to meet enhanced demand imposed by domestic, commercial and industrial consumers, various operational & control activities of Radial Distribution Network (RDN) requires a focused attention. Irrespective of sub-domains research aspects of RDN like network reconfiguration, reactive power compensation and economic load scheduling etc, network performance parameters are usually estimated by an iterative process and is commonly known as load (power) flow algorithm. In this paper, a simple mechanism is presented to implement the load flow analysis (LFA) algorithm. The reported algorithm utilizes graph theory principles and is tested on a 69- bus RDN.

Keywords: Radial Distribution network, Graph, Load-flow, Array.

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Authors: Thiang, Handry Khoswanto, Rendy Pangaldus

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Inverse kinematics analysis plays an important role in developing a robot manipulator. But it is not too easy to derive the inverse kinematic equation of a robot manipulator especially robot manipulator which has numerous degree of freedom. This paper describes an application of Artificial Neural Network for modeling the inverse kinematics equation of a robot manipulator. In this case, the robot has three degree of freedoms and the robot was implemented for drilling a printed circuit board. The artificial neural network architecture used for modeling is a multilayer perceptron networks with steepest descent backpropagation training algorithm. The designed artificial neural network has 2 inputs, 2 outputs and varies in number of hidden layer. Experiments were done in variation of number of hidden layer and learning rate. Experimental results show that the best architecture of artificial neural network used for modeling inverse kinematics of is multilayer perceptron with 1 hidden layer and 38 neurons per hidden layer. This network resulted a RMSE value of 0.01474.

Keywords: Artificial neural network, back propagation, inverse kinematics, manipulator, robot.

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Authors: Yasser k. Ali, Hesham N. Elmahdy, Sanaa El Olla Hanfy Ahmed

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Mobile agents are a powerful approach to develop distributed systems since they migrate to hosts on which they have the resources to execute individual tasks. In a dynamic environment like a peer-to-peer network, Agents have to be generated frequently and dispatched to the network. Thus they will certainly consume a certain amount of bandwidth of each link in the network if there are too many agents migration through one or several links at the same time, they will introduce too much transferring overhead to the links eventually, these links will be busy and indirectly block the network traffic, therefore, there is a need of developing routing algorithms that consider about traffic load. In this paper we seek to create cooperation between a probabilistic manner according to the quality measure of the network traffic situation and the agent's migration decision making to the next hop based on decision tree learning algorithms.

Keywords: Agent Migration, Decision Tree learning, ID3 algorithm, Naive Bayes Classifier

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Authors: Che Z. Zulkifli, Nursyahida M. Noor, Siti N. Semunab, Shafawati A. Malek

Abstract:

Wireless sensors, also known as wireless sensor nodes, have been making a significant impact on human daily life. The Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN) are two complementary technologies; hence, an integrated implementation of these technologies expands the overall functionality in obtaining long-range and real-time information on the location and properties of objects and people. An approach for integrating ZigBee and RFID networks is proposed in this paper, to create an energy-efficient network improved by the benefits of combining ZigBee and RFID architecture. Furthermore, the compatibility and requirements of the ZigBee device and communication links in the typical RFID system which is presented with the real world experiment on the capabilities of the proposed RFID system.

Keywords: Mesh network, RFID, wireless sensor network, zigbee.

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Authors: John Kabuba, Antoine Mulaba-Bafubiandi, Kim Battle

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In this study a neural network (NN) was proposed to predict the sorption of binary mixture of copper-cobalt ions into clinoptilolite as ion-exchanger. The configuration of the backpropagation neural network giving the smallest mean square error was three-layer NN with tangent sigmoid transfer function at hidden layer with 10 neurons, linear transfer function at output layer and Levenberg-Marquardt backpropagation training algorithm. Experiments have been carried out in the batch reactor to obtain equilibrium data of the individual sorption and the mixture of coppercobalt ions. The obtained modeling results have shown that the used of neural network has better adjusted the equilibrium data of the binary system when compared with the conventional sorption isotherm models.

Keywords: Adsorption isotherm, binary system, neural network; sorption

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Authors: Assyl Bari, Olga Berillo, Saltanat Orazova, Anatoliy Ivashchenko

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Among all microRNAs (miRNAs) in 12 plant species investigated in this study, only miR398 targeted the copper chaperone for superoxide dismutase (CCS). The nucleotide sequences of miRNA binding sites were located in the mRNA protein-coding sequence (CDS) and were highly homologous. These binding sites in CCS mRNA encoded a conservative GDLGTL hexapeptide. The binding sites for miR398 in the CDS of superoxide dismutase 1 mRNA encoded GDLGN pentapeptide. The conservative miR398 binding site located in the CDS of superoxide dismutase 2 mRNA encoded the GDLGNI hexapeptide. The miR398 binding site in the CDS of superoxide dismutase 3 mRNA encoded the GDLGNI or GDLGNV hexapeptide. Gene expression of the entire superoxide dismutase family in the studied plant species was regulated only by miR398. All members of the miR398 family, i.e. miR398a,b,c were connected to one site for each CuZnSOD and chaperone mRNA.

Keywords: MicroRNA, mRNA, plant, superoxide dismutase.

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Authors: Jaswinder Kaur, Satwinder Singh, Karanjeet Singh Kahlon

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Accurate software cost estimates are critical to both developers and customers. They can be used for generating request for proposals, contract negotiations, scheduling, monitoring and control. The exact relationship between the attributes of the effort estimation is difficult to establish. A neural network is good at discovering relationships and pattern in the data. So, in this paper a comparative analysis among existing Halstead Model, Walston-Felix Model, Bailey-Basili Model, Doty Model and Neural Network Based Model is performed. Neural Network has outperformed the other considered models. Hence, we proposed Neural Network system as a soft computing approach to model the effort estimation of the software systems.

Keywords: Effort Estimation, Neural Network, Halstead Model, Walston-Felix Model, Bailey-Basili Model, Doty Model.

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Authors: C. N. Vanitha, M. Usha

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In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: Neural networks, pattern learning, security, wireless sensor networks.

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Authors: Liu Zhiyuan, Sun Zongdi

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In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City.

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Authors: Toshihiko Sasama, Kentaro Kishida, Kazunori Sugahara, Hiroshi Masuyama

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A mobile ad hoc network is a network of mobile nodes without any notion of centralized administration. In such a network, each mobile node behaves not only as a host which runs applications but also as a router to forward packets on behalf of others. Clustering has been applied to routing protocols to achieve efficient communications. A CH network expresses the connected relationship among cluster-heads. This paper discusses the methods for constructing a CH network, and produces the following results: (1) The required running costs of 3 traditional methods for constructing a CH network are not so different from each other in the static circumstance, or in the dynamic circumstance. Their running costs in the static circumstance do not differ from their costs in the dynamic circumstance. Meanwhile, although the routing costs required for the above 3 methods are not so different in the static circumstance, the costs are considerably different from each other in the dynamic circumstance. Their routing costs in the static circumstance are also very different from their costs in the dynamic circumstance, and the former is one tenths of the latter. The routing cost in the dynamic circumstance is mostly the cost for re-routing. (2) On the strength of the above results, we discuss new 2 methods regarding whether they are tolerable or not in the dynamic circumstance, that is, whether the times of re-routing are small or not. These new methods are revised methods that are based on the traditional methods. We recommended the method which produces the smallest routing cost in the dynamic circumstance, therefore producing the smallest total cost.

Keywords: cluster, mobile ad hoc network, re-routing cost, simulation

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Authors: A.Qaderi, A. Heydarinasab, M. Ardjmand

Abstract:

Poly-β-hydroxybutyrate (PHB) is one of the most famous biopolymers that has various applications in production of biodegradable carriers. The most important strategy for enhancing efficiency in production process and reducing the price of PHB, is the accurate expression of kinetic model of products formation and parameters that are effective on it, such as Dry Cell Weight (DCW) and substrate consumption. Considering the high capabilities of artificial neural networks in modeling and simulation of non-linear systems such as biological and chemical industries that mainly are multivariable systems, kinetic modeling of microbial production of PHB that is a complex and non-linear biological process, the three layers perceptron neural network model was used in this study. Artificial neural network educates itself and finds the hidden laws behind the data with mapping based on experimental data, of dry cell weight, substrate concentration as input and PHB concentration as output. For training the network, a series of experimental data for PHB production from Hydrogenophaga Pseudoflava by glucose carbon source was used. After training the network, two other experimental data sets that have not intervened in the network education, including dry cell concentration and substrate concentration were applied as inputs to the network, and PHB concentration was predicted by the network. Comparison of predicted data by network and experimental data, indicated a high precision predicted for both fructose and whey carbon sources. Also in present study for better understanding of the ability of neural network in modeling of biological processes, microbial production kinetic of PHB by Leudeking-Piret experimental equation was modeled. The Observed result indicated an accurate prediction of PHB concentration by artificial neural network higher than Leudeking- Piret model.

Keywords: Kinetic Modeling, Poly-β-Hydroxybutyrate (PHB), Hydrogenophaga Pseudoflava, Artificial Neural Network, Leudeking-Piret

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Authors: Osama Elsarrar, Marjorie Darrah, Richard Devin

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This paper discusses the idea of capturing an expert’s knowledge in the form of human understandable rules and then inserting these rules into a dynamic cell structure (DCS) neural network. The DCS is a form of self-organizing map that can be used for many purposes, including classification and prediction. This particular neural network is considered to be a topology preserving network that starts with no pre-structure, but assumes a structure once trained. The DCS has been used in mission and safety-critical applications, including adaptive flight control and health-monitoring in aerial vehicles. The approach is to insert expert knowledge into the DCS before training. Rules are translated into a pre-structure and then training data are presented. This idea has been demonstrated using the well-known Iris data set and it has been shown that inserting the pre-structure results in better accuracy with the same training.

Keywords: Neural network, rule extraction, rule insertion, self-organizing map.

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Authors: Yuanyuan Zhang, Guang Sun, Li Su, Depeng Jin, Lieguang Zeng

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Since Network-on-Chip (NoC) uses network interfaces (NIs) to improve the design productivity, by now, there have been a few papers addressing the design and implementation of a NI module. However, none of them considered the difference of address encoding methods between NoC and the traditional bus-shared architecture. On the basis of this difference, in the paper, we introduce a transmit mechanism to solve such a problem for global asynchronous locally synchronous (GALS) NoC. Furthermore, we give the concrete implementation of the NI module in this transmit mechanism. Finally, we evaluate its performance and area overhead by a VHDL-based cycle-accurate RTL model and simulation results confirm the validity of this address-oriented transmit mechanism.

Keywords: Network-on-Chip, Network Interface, Open CoreProtocol, Address.

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Authors: Jain-Shing Liu

Abstract:

In this paper, we present a matrix game-theoretic cross-layer optimization formulation to maximize the network lifetime in wireless ad hoc networks with network coding. To this end, we introduce a cross-layer formulation of general NUM (network utility maximization) that accommodates routing, scheduling, and stream control from different layers in the coded networks. Specifically, for the scheduling problem and then the objective function involved, we develop a matrix game with the strategy sets of the players corresponding to hyperlinks and transmission modes, and design the payoffs specific to the lifetime. In particular, with the inherit merit that matrix game can be solved with linear programming, our cross-layer programming formulation can benefit from both game-based and NUM-based approaches at the same time by cooperating the programming model for the matrix game with that for the other layers in a consistent framework. Finally, our numerical example demonstrates its performance results on a well-known wireless butterfly network to verify the cross-layer optimization scheme.

Keywords: Cross-layer design, Lifetime maximization, Matrix game, Network coding

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Authors: Craig M. Allen

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Twenty two African entrepreneurs with Small and Medium Enterprises (SMEs) in a single social network centered around a non-Muslim population in a smaller African country, selected an Islamic financing structure, a form of Murabaha, based solely on market rationale. These entrepreneurs had all won procurement contracts from major purchasers of goods within their country and faced difficulty arranging traditional bank financing to support their supply-chain needs. The Murabaha-based structure satisfied their market-driven demand and provided an attractive alternative to the traditional bank-offered lending products. The Murabaha-styled trade-financing structure was not promoted with any religious implications, but solely as a market solution to the existing problems associated with bank-related financing. This indicates the strong market forces that draw SMEs to financing structures that are traditionally considered within the framework of Islamic finance.

Keywords: Africa, entrepreneurs, Islamic finance, market acceptance, Murabaha, SMEs.

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Authors: S. Ganesh

Abstract:

Power distribution systems typically have tie and sectionalizing switches whose states determine the topological configuration of the network. The aim of network reconfiguration of the distribution network is to minimize the losses for a load arrangement at a particular time. Thus the objective function is to minimize the losses of the network by satisfying the distribution network constraints. The various constraints are radiality, voltage limits and the power balance condition. In this paper the status of the switches is obtained by using Artificial Bee Colony (ABC) algorithm. ABC is based on a particular intelligent behavior of honeybee swarms. ABC is developed based on inspecting the behaviors of real bees to find nectar and sharing the information of food sources to the bees in the hive. The proposed methodology has three stages. In stage one ABC is used to find the tie switches, in stage two the identified tie switches are checked for radiality constraint and if the radilaity constraint is satisfied then the procedure is proceeded to stage three otherwise the process is repeated. In stage three load flow analysis is performed. The process is repeated till the losses are minimized. The ABC is implemented to find the power flow path and the Forward Sweeper algorithm is used to calculate the power flow parameters. The proposed methodology is applied for a 33–bus single feeder distribution network using MATLAB.

Keywords: Artificial Bee Colony (ABC) algorithm, Distribution system, Loss reduction, Network reconfiguration.

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Authors: Tarik Rashid, B. Q. Huang, M-T. Kechadi, B. Gleeson

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this paper presents an auto-regressive network called the Auto-Regressive Multi-Context Recurrent Neural Network (ARMCRN), which forecasts the daily peak load for two large power plant systems. The auto-regressive network is a combination of both recurrent and non-recurrent networks. Weather component variables are the key elements in forecasting because any change in these variables affects the demand of energy load. So the AR-MCRN is used to learn the relationship between past, previous, and future exogenous and endogenous variables. Experimental results show that using the change in weather components and the change that occurred in past load as inputs to the AR-MCRN, rather than the basic weather parameters and past load itself as inputs to the same network, produce higher accuracy of predicted load. Experimental results also show that using exogenous and endogenous variables as inputs is better than using only the exogenous variables as inputs to the network.

Keywords: Daily peak load forecasting, neural networks, recurrent neural networks, auto regressive multi-context neural network.

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