Search results for: car moving techniques

Authors: Mohammad Namazi, Mohammad Sadeghzadeh Maharluie

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Predicting earnings management is vital for the capital market participants, financial analysts and managers. The aim of this research is attempting to respond to this query: Is there a significant difference between the regression model and neural networks’ models in predicting earnings management, and which one leads to a superior prediction of it? In approaching this question, a Linear Regression (LR) model was compared with two neural networks including Multi-Layer Perceptron (MLP), and Generalized Regression Neural Network (GRNN). The population of this study includes 94 listed companies in Tehran Stock Exchange (TSE) market from 2003 to 2011. After the results of all models were acquired, ANOVA was exerted to test the hypotheses. In general, the summary of statistical results showed that the precision of GRNN did not exhibit a significant difference in comparison with MLP. In addition, the mean square error of the MLP and GRNN showed a significant difference with the multi variable LR model. These findings support the notion of nonlinear behavior of the earnings management. Therefore, it is more appropriate for capital market participants to analyze earnings management based upon neural networks techniques, and not to adopt linear regression models.

Keywords: Earnings management, generalized regression neural networks, linear regression, multi-layer perceptron, Tehran stock exchange.

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Authors: Rachid Ait daoud, Abdellah Amine, Belaid Bouikhalene, Rachid Lbibb

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Given the increase in the number of e-commerce sites, the number of competitors has become very important. This means that companies have to take appropriate decisions in order to meet the expectations of their customers and satisfy their needs. In this paper, we present a case study of applying LRFM (length, recency, frequency and monetary) model and clustering techniques in the sector of electronic commerce with a view to evaluating customers’ values of the Moroccan e-commerce websites and then developing effective marketing strategies. To achieve these objectives, we adopt LRFM model by applying a two-stage clustering method. In the first stage, the self-organizing maps method is used to determine the best number of clusters and the initial centroid. In the second stage, kmeans method is applied to segment 730 customers into nine clusters according to their L, R, F and M values. The results show that the cluster 6 is the most important cluster because the average values of L, R, F and M are higher than the overall average value. In addition, this study has considered another variable that describes the mode of payment used by customers to improve and strengthen clusters’ analysis. The clusters’ analysis demonstrates that the payment method is one of the key indicators of a new index which allows to assess the level of customers’ confidence in the company's Website.

Keywords: Customer value, LRFM model, Cluster analysis, Self-Organizing Maps method (SOM), K-means algorithm, loyalty.

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Authors: J. Y. Jang, Y. W. Lee, C. N. Lin, C. H. Wang

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The reheating furnace is used to reheat the steel slabs before the hot-rolling process. The supported system includes the stationary/moving beams, and the skid buttons which block some thermal radiation transmitted to the bottom of the slabs. Therefore, it is important to analyze the steel slab temperature distribution during the heating period. A three-dimensional mathematical transient heat transfer model for the prediction of temperature distribution within the slab has been developed. The effects of different skid button height (H=60mm, 90mm, and 120mm) and different gap distance between two slabs (S=50mm, 75mm, and 100mm) on the slab skid mark formation and temperature profiles are investigated. Comparison with the in-situ experimental data from Steel Company in Taiwan shows that the present heat transfer model works well for the prediction of thermal behavior of the slab in the reheating furnace. It is found that the skid mark severity decreases with an increase in the skid button height. The effect of gap distance is important only for the slab edge planes, while it is insignificant for the slab central planes.

Keywords: 3-D, slab, transient heat conduction, reheating furnace, thermal radiation.

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Authors: M. Derakhshanfar, B. Minaei-Bidgoli

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Spatial trends are one of the valuable patterns in geo databases. They play an important role in data analysis and knowledge discovery from spatial data. A spatial trend is a regular change of one or more non spatial attributes when spatially moving away from a start object. Spatial trend detection is a graph search problem therefore heuristic methods can be good solution. Artificial immune system (AIS) is a special method for searching and optimizing. AIS is a novel evolutionary paradigm inspired by the biological immune system. The models based on immune system principles, such as the clonal selection theory, the immune network model or the negative selection algorithm, have been finding increasing applications in fields of science and engineering. In this paper, we develop a novel immunological algorithm based on clonal selection algorithm (CSA) for spatial trend detection. We are created neighborhood graph and neighborhood path, then select spatial trends that their affinity is high for antibody. In an evolutionary process with artificial immune algorithm, affinity of low trends is increased with mutation until stop condition is satisfied.

Keywords: Spatial Data Mining, Spatial Trend Detection, Heuristic Methods, Artificial Immune System, Clonal Selection Algorithm (CSA)

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Authors: Nevena Stolba, A Min Tjoa

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Evidence-based medicine is a new direction in modern healthcare. Its task is to prevent, diagnose and medicate diseases using medical evidence. Medical data about a large patient population is analyzed to perform healthcare management and medical research. In order to obtain the best evidence for a given disease, external clinical expertise as well as internal clinical experience must be available to the healthcare practitioners at right time and in the right manner. External evidence-based knowledge can not be applied directly to the patient without adjusting it to the patient-s health condition. We propose a data warehouse based approach as a suitable solution for the integration of external evidence-based data sources into the existing clinical information system and data mining techniques for finding appropriate therapy for a given patient and a given disease. Through integration of data warehousing, OLAP and data mining techniques in the healthcare area, an easy to use decision support platform, which supports decision making process of care givers and clinical managers, is built. We present three case studies, which show, that a clinical data warehouse that facilitates evidence-based medicine is a reliable, powerful and user-friendly platform for strategic decision making, which has a great relevance for the practice and acceptance of evidence-based medicine.

Keywords: data mining, data warehousing, decision-support systems, evidence-based medicine.

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Authors: P. S. Hiremath, Siddu P. Algur

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With the explosive growth of information sources available on the World Wide Web, it has become increasingly difficult to identify the relevant pieces of information, since web pages are often cluttered with irrelevant content like advertisements, navigation-panels, copyright notices etc., surrounding the main content of the web page. Hence, tools for the mining of data regions, data records and data items need to be developed in order to provide value-added services. Currently available automatic techniques to mine data regions from web pages are still unsatisfactory because of their poor performance and tag-dependence. In this paper a novel method to extract data items from the web pages automatically is proposed. It comprises of two steps: (1) Identification and Extraction of the data regions based on visual clues information. (2) Identification of data records and extraction of data items from a data region. For step1, a novel and more effective method is proposed based on visual clues, which finds the data regions formed by all types of tags using visual clues. For step2 a more effective method namely, Extraction of Data Items from web Pages (EDIP), is adopted to mine data items. The EDIP technique is a list-based approach in which the list is a linear data structure. The proposed technique is able to mine the non-contiguous data records and can correctly identify data regions, irrespective of the type of tag in which it is bound. Our experimental results show that the proposed technique performs better than the existing techniques.

Keywords: Web data records, web data regions, web mining.

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Authors: A. Doaa M. Atia, Faten H. Fahmy, Ninet M. Ahmed, Hassen T. Dorrah

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Temperature is one of the most principle factors affects aquaculture system. It can cause stress and mortality or superior environment for growth and reproduction. This paper presents the control of pond water temperature using artificial intelligence technique. The water temperature is very important parameter for shrimp growth. The required temperature for optimal growth is 34oC, if temperature increase up to 38oC it cause death of the shrimp, so it is important to control water temperature. Solar thermal water heating system is designed to supply an aquaculture pond with the required hot water in Mersa Matruh in Egypt. Neural networks are massively parallel processors that have the ability to learn patterns through a training experience. Because of this feature, they are often well suited for modeling complex and non-linear processes such as those commonly found in the heating system. Artificial neural network is proposed to control water temperature due to Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques. They have been used to solve complicated practical problems. Moreover this paper introduces a complete mathematical modeling and MATLAB SIMULINK model for the aquaculture system. The simulation results indicate that, the control unit success in keeping water temperature constant at the desired temperature by controlling the hot water flow rate.

Keywords: artificial neural networks, aquaculture, forced circulation hot water system,

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Authors: Amira Elsonbaty, Rawya Rizk, Mohamed Elksas, Mofreh Salem

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In this paper, a Cooperative Multi-robot for Carrying Targets (CMCT) algorithm is proposed. The multi-robot team consists of three robots, one is a supervisor and the others are workers for carrying boxes in a store of 100×100 m2. Each robot has a self recharging mechanism. The CMCT minimizes robot-s worked time for carrying many boxes during day by working in parallel. That is, the supervisor detects the required variables in the same time another robots work with previous variables. It works with straightforward mechanical models by using simple cosine laws. It detects the robot-s shortest path for reaching the target position avoiding obstacles by using a proposed CMCT path planning (CMCT-PP) algorithm. It prevents the collision between robots during moving. The robots interact in an ad hoc wireless network. Simulation results show that the proposed system that consists of CMCT algorithm and its accomplished CMCT-PP algorithm achieves a high improvement in time and distance while performing the required tasks over the already existed algorithms.

Keywords: Ad hoc network, Computer vision based positioning, Dynamic collision avoidance, Multi-robot, Path planning algorithms, Self recharging.

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Authors: George Yungchih Wang

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For decades financial economists have been attempted to determine the optimal investment policy by recognizing the option value embedded in irreversible investment whose project value evolves as a geometric Brownian motion (GBM). This paper aims to examine the effects of the optimal investment trigger and of the misspecification of stochastic processes on investment in real options applications. Specifically, the former explores the consequence of adopting optimal investment rules on the distributions of corporate value under the correct assumption of stochastic process while the latter analyzes the influence on the distributions of corporate value as a result of the misspecification of stochastic processes, i.e., mistaking an alternative process as a GBM. It is found that adopting the correct optimal investment policy may increase corporate value by shifting the value distribution rightward, and the misspecification effect may decrease corporate value by shifting the value distribution leftward. The adoption of the optimal investment trigger has a major impact on investment to such an extent that the downside risk of investment is truncated at the project value of zero, thereby moving the value distributions rightward. The analytical framework is also extended to situations where collection lags are in place, and the result indicates that collection lags reduce the effects of investment trigger and misspecification on investment in an opposite way.

Keywords: GBM, real options, investment trigger, misspecification, collection lags

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Authors: Mawloud Mosbah, Bachir Boucheham

Abstract:

We present here the results for a comparative study of some techniques, available in the literature, related to the relevance feedback mechanism in the case of a short-term learning. Only one method among those considered here is belonging to the data mining field which is the K-nearest neighbors algorithm (KNN) while the rest of the methods is related purely to the information retrieval field and they fall under the purview of the following three major axes: Shifting query, Feature Weighting and the optimization of the parameters of similarity metric. As a contribution, and in addition to the comparative purpose, we propose a new version of the KNN algorithm referred to as an incremental KNN which is distinct from the original version in the sense that besides the influence of the seeds, the rate of the actual target image is influenced also by the images already rated. The results presented here have been obtained after experiments conducted on the Wang database for one iteration and utilizing color moments on the RGB space. This compact descriptor, Color Moments, is adequate for the efficiency purposes needed in the case of interactive systems. The results obtained allow us to claim that the proposed algorithm proves good results; it even outperforms a wide range of techniques available in the literature.

Keywords: CBIR, Category Search, Relevance Feedback (RFB), Query Point Movement, Standard Rocchio’s Formula, Adaptive Shifting Query, Feature Weighting, Optimization of the Parameters of Similarity Metric, Original KNN, Incremental KNN.

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Authors: Wafa' S.Al-Sharafat, Reyadh Naoum

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Society has grown to rely on Internet services, and the number of Internet users increases every day. As more and more users become connected to the network, the window of opportunity for malicious users to do their damage becomes very great and lucrative. The objective of this paper is to incorporate different techniques into classier system to detect and classify intrusion from normal network packet. Among several techniques, Steady State Genetic-based Machine Leaning Algorithm (SSGBML) will be used to detect intrusions. Where Steady State Genetic Algorithm (SSGA), Simple Genetic Algorithm (SGA), Modified Genetic Algorithm and Zeroth Level Classifier system are investigated in this research. SSGA is used as a discovery mechanism instead of SGA. SGA replaces all old rules with new produced rule preventing old good rules from participating in the next rule generation. Zeroth Level Classifier System is used to play the role of detector by matching incoming environment message with classifiers to determine whether the current message is normal or intrusion and receiving feedback from environment. Finally, in order to attain the best results, Modified SSGA will enhance our discovery engine by using Fuzzy Logic to optimize crossover and mutation probability. The experiments and evaluations of the proposed method were performed with the KDD 99 intrusion detection dataset.

Keywords: MSSGBML, Network Intrusion Detection, SGA, SSGA.

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Authors: Rashid S. Mohammad, Shicheng Zhang, Sun Lu, Syed Jamal-Ud-Din, Xinzhe Zhao

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A compositional reservoir simulation model (CMG-GEM) was used for cyclic CO₂ injection process in unconventional tight reservoir. Cyclic CO₂ injection is an enhanced oil recovery process consisting of injection, shut-in, and production. The study of cyclic CO₂ injection and hydrocarbon recovery in ultra-low permeability reservoirs is mainly a function of rock, fluid, and operational parameters. CMG-GEM was used to study several design parameters of cyclic CO₂ injection process to distinguish the parameters with maximum effect on the oil recovery and to comprehend the behavior of cyclic CO₂ injection in tight reservoir. On the other hand, permeability reduction induced by asphaltene precipitation is one of the major issues in the oil industry due to its plugging onto the porous media which reduces the oil productivity. In addition to asphaltene deposition, solubility of CO₂ in the aquifer is one of the safest and permanent trapping techniques when considering CO₂ storage mechanisms in geological formations. However, the effects of the above uncertain parameters on the process of CO₂ enhanced oil recovery have not been understood systematically. Hence, it is absolutely necessary to study the most significant parameters which dominate the process. The main objective of this study is to improve techniques for designing cyclic CO₂ injection process while considering the effects of asphaltene deposition and solubility of CO₂ in the brine in order to prevent asphaltene precipitation, minimize CO₂ emission, optimize cyclic CO₂ injection, and maximize oil production.

Keywords: Tight reservoirs, cyclic O2 injection, asphaltene, solubility, reservoir simulation.

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Authors: M. Amrita, R. R. Srikant, A. V. Sita Rama Raju

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Water miscible cutting fluids are conventionally used to lubricate and cool the machining zone. But issues related to health hazards, maintenance and disposal costs have limited their usage, leading to application of Minimum Quantity Lubrication (MQL). To increase the effectiveness of MQL, nanocutting fluids are proposed. In the present work, water miscible nanographite cutting fluids of varying concentration are applied at cutting zone by two systems A and B. System A utilizes high pressure air and supplies cutting fluid at a flow rate of 1ml/min. System B uses low pressure air and supplies cutting fluid at a flow rate of 5ml/min. Their performance in machining is evaluated by measuring cutting temperatures, tool wear, cutting forces and surface roughness and compared with dry machining and flood machining. Application of nanocutting fluid using both systems showed better performance than dry machining. Cutting temperatures and cutting forces obtained by both techniques are more than flood machining. But tool wear and surface roughness showed improvement compared to flood machining. Economic analysis has been carried out in all the cases to decide the applicability of the techniques.

Keywords: Economic analysis, Machining, Minimum Quantity lubrication, nanofluid.

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Authors: Alper Akın, İbrahim Aydoğdu

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This study presents a hybrid metaheuristic algorithm to obtain optimum designs for steel space buildings. The optimum design problem of three-dimensional steel frames is mathematically formulated according to provisions of LRFD-AISC (Load and Resistance factor design of American Institute of Steel Construction). Design constraints such as the strength requirements of structural members, the displacement limitations, the inter-story drift and the other structural constraints are derived from LRFD-AISC specification. In this study, a hybrid algorithm by using teachinglearning based optimization (TLBO) and harmony search (HS) algorithms is employed to solve the stated optimum design problem. These algorithms are two of the recent additions to metaheuristic techniques of numerical optimization and have been an efficient tool for solving discrete programming problems. Using these two algorithms in collaboration creates a more powerful tool and mitigates each other’s weaknesses. To demonstrate the powerful performance of presented hybrid algorithm, the optimum design of a large scale steel building is presented and the results are compared to the previously obtained results available in the literature.

Keywords: Optimum structural design, hybrid techniques, teaching-learning based optimization, harmony search algorithm, minimum weight, steel space frame.

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Authors: Khaled S. Abdallah, Abdel-Aziz M. Mohamed

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Moving industries consume numerous resources and dispose masses of used packaging materials. Proper sorting, recycling and disposing the packaging materials is necessary to avoid a sever pollution disaster. This research paper presents a conceptual model to propose sustainable truck rental operations instead of the regular one. An optimization model was developed to select the locations of truck rental centers, collection sites, maintenance and repair sites, and identify the rental fees to be charged for all routes that maximize the total closed supply chain profits. Fixed costs of vehicle purchasing, costs of constructing collection centers and repair centers, as well as the fixed costs paid to use disposal and recycling centers are considered. Operating costs include the truck maintenance, repair costs as well as the cost of recycling and disposing the packing materials, and the costs of relocating the truck are presented in the model. A mixed integer model is developed followed by a simulation model to examine the factors affecting the operation of the model.

Keywords: Modeling, truck rental, supply chains management, simulation.

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Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original dataset. In this paper, a new design of distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 dataset is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

Keywords: Distributed intrusion detection system, mobile agent, feature selection, Bees Algorithm, decision tree.

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Authors: Mohammad Reza Ayatollahzadeh Shirazi, Pooya Jaferian, Golnaz Elahi, Hamid Baghi, Babak Sadeghian

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The world is moving rapidly toward the deployment of information and communication systems. Nowadays, computing systems with their fast growth are found everywhere and one of the main challenges for these systems is increasing attacks and security threats against them. Thus, capturing, analyzing and verifying security requirements becomes a very important activity in development process of computing systems, specially in developing systems such as banking, military and e-business systems. For developing every system, a process model which includes a process, methods and tools is chosen. The Rational Unified Process (RUP) is one of the most popular and complete process models which is used by developers in recent years. This process model should be extended to be used in developing secure software systems. In this paper, the Requirement Discipline of RUP is extended to improve RUP for developing secure software systems. These proposed extensions are adding and integrating a number of Activities, Roles, and Artifacts to RUP in order to capture, document and model threats and security requirements of system. These extensions introduce a group of clear and stepwise activities to developers. By following these activities, developers assure that security requirements are captured and modeled. These models are used in design, implementation and test activitie

Keywords:

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Authors: Gabriela V. Angeles Perez, Jose Castillejos Lopez, Araceli L. Reyes Cabello, Emilio Bravo Grajales, Adriana Perez Espinosa, Jose L. Quiroz Fabian

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Road traffic accidents are among the principal causes of traffic congestion, causing human losses, damages to health and the environment, economic losses and material damages. Studies about traditional road traffic accidents in urban zones represents very high inversion of time and money, additionally, the result are not current. However, nowadays in many countries, the crowdsourced GPS based traffic and navigation apps have emerged as an important source of information to low cost to studies of road traffic accidents and urban congestion caused by them. In this article we identified the zones, roads and specific time in the CDMX in which the largest number of road traffic accidents are concentrated during 2016. We built a database compiling information obtained from the social network known as Waze. The methodology employed was Discovery of knowledge in the database (KDD) for the discovery of patterns in the accidents reports. Furthermore, using data mining techniques with the help of Weka. The selected algorithms was the Maximization of Expectations (EM) to obtain the number ideal of clusters for the data and k-means as a grouping method. Finally, the results were visualized with the Geographic Information System QGIS.

Keywords: Data mining, K-means, road traffic accidents, Waze, Weka.

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Authors: Walid MOUDANI

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The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the Coronary Heart Disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts- knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: Multi-Classifier Decisions Tree, Features Reduction, Dynamic Programming, Rough Sets.

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Authors: Yamini Raju, Parminder S. Kang, Adam Moroz, Ross Clement, Ashley Hopwell, Alistair Duffy

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Accurate forecasting of fresh produce demand is one the challenges faced by Small Medium Enterprise (SME) wholesalers. This paper is an attempt to understand the cause for the high level of variability such as weather, holidays etc., in demand of SME wholesalers. Therefore, understanding the significance of unidentified factors may improve the forecasting accuracy. This paper presents the current literature on the factors used to predict demand and the existing forecasting techniques of short shelf life products. It then investigates a variety of internal and external possible factors, some of which is not used by other researchers in the demand prediction process. The results presented in this paper are further analysed using a number of techniques to minimize noise in the data. For the analysis past sales data (January 2009 to May 2014) from a UK based SME wholesaler is used and the results presented are limited to product ‘Milk’ focused on café’s in derby. The correlation analysis is done to check the dependencies of variability factor on the actual demand. Further PCA analysis is done to understand the significance of factors identified using correlation. The PCA results suggest that the cloud cover, weather summary and temperature are the most significant factors that can be used in forecasting the demand. The correlation of the above three factors increased relative to monthly and becomes more stable compared to the weekly and daily demand.

Keywords: Demand Forecasting, Deteriorating Products, Food Wholesalers, Principal Component Analysis and Variability Factors.

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Authors: Yamamah Abdulrazaq, Lahieb A. Abrahim, Samuel E. Davies, Iain Shewring

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Electric water heater (EWH) is a powerful appliance that uses electricity in residential, commercial, and industrial settings, and the ability to control them properly will result in cost savings and the prevention of blackouts on the national grid. This article discusses the usage of timers in EWH control strategies for demand-side management (DSM). To the authors' knowledge, there is no systematic mapping review focusing on the utilization of EWH control strategies in DSM has yet been conducted. Consequently, the purpose of this research is to identify and examine main papers exploring EWH procedures in DSM by quantifying and categorizing information with regard to publication year and source, kind of methods, and source of data for monitoring control techniques. In order to answer the research questions, a total of 31 publications published between 1999 and 2023 were selected depending on specific inclusion and exclusion criteria. The data indicate that direct load control (DLC) has been somewhat more prevalent than indirect load control (ILC). Additionally, the mix method is much lower than the other techniques, and the proportion of real-time data (RTD) to non-real-time data (NRTD) is about equal.

Keywords: Demand side management, direct load control, electric water heater, indirect load control, non-real-time data, real time data.

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Authors: Diogen Babuc

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The question that is a motivation of writing is how many devote themselves to discovering something in the world of science where much is discerned and revealed, but at the same time, much is unknown. The insightful elements of this algorithm are the ciphering and deciphering algorithms of Playfair, Caesar, and Vigen`ere. Only a few of their main properties are taken and modified, with the aim of forming a specific functionality of the algorithm called Latin Djokovic. Specifically, a string is entered as input data. A key k is given, with a random value between the values a and b = a+3. The obtained value is stored in a variable with the aim of being constant during the run of the algorithm. In correlation to the given key, the string is divided into several groups of substrings, and each substring has a length of k characters. The next step involves encoding each substring from the list of existing substrings. Encoding is performed using the basis of Caesar algorithm, i.e. shifting with k characters. However, that k is incremented by 1 when moving to the next substring in that list. When the value of k becomes greater than b + 1, it will return to its initial value. The algorithm is executed, following the same procedure, until the last substring in the list is traversed. Using this polyalphabetic method, ciphering and deciphering of strings are achieved. The algorithm also works for a 100-character string. The x character is not used when the number of characters in a substring is incompatible with the expected length. The algorithm is simple to implement, but it is questionable if it works better than the other methods, from the point of view of execution time and storage space.

Keywords: Ciphering and deciphering, Authentic Algorithm, Polyalphabetic Cipher, Random Key, methods comparison.

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Authors: Banaja Mohanty, Prakash Kumar Hota

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This paper demonstrates dynamic performance evaluation of load frequency control (LFC) with different intelligent techniques. All non-linearities and physical constraints have been considered in simulation studies such as governor dead band (GDB), generation rate constraint (GRC) and boiler dynamics. The conventional integral time absolute error has been considered as objective function. The design problem is formulated as an optimisation problem and particle swarm optimisation (PSO), bacterial foraging optimisation algorithm (BFOA) and differential evolution (DE) are employed to search optimal controller parameters. The superiority of the proposed approach has been shown by comparing the results with published fuzzy logic control (FLC) for the same interconnected power system. The comparison is done using various performance measures like overshoot, undershoot, settling time and standard error criteria of frequency and tie-line power deviation following a step load perturbation (SLP). It is noticed that, the dynamic performance of proposed controller is better than FLC. Further, robustness analysis is carried out by varying the time constants of speed governor, turbine, tie-line power in the range of +40% to -40% to demonstrate the robustness of the proposed DE optimized PID controller.

Keywords: Automatic generation control, governor dead band, generation rate constraint, differential evolution.

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Authors: S. Kulsri, M. Jaroensutasinee, K. Jaroensutasinee

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We developed a method based on quasi-molecular modelling to simulate the fall of water drops on horizontally smooth and rough surfaces. Each quasi-molecule was a group of particles that interacted in a fashion entirely analogous to classical Newtonian molecular interactions. When a falling water droplet was simulated at low impact velocity on both smooth and rough surfaces, the droplets moved periodically (i.e. the droplets moved up and down for a certain period, finally they stopped moving and reached a steady state), spreading and recoiling without splash or break-up. Spreading rates of falling water droplets increased rapidly as time increased until the spreading rate reached its steady state at time t ~ 0.25 s for rough surface and t ~ 0.40 s for smooth surface. The droplet height above both surfaces decreased as time increased, remained constant after the droplet diameter attained a maximum value and reached its steady state at time t ~ 0.4 s. However, rough surface had higher spreading rates of falling water droplets and lower height on the surface than smooth one.

Keywords: Quasi-molecular modelling, particle modelling, molecular aggregate approach.

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Authors: Erika Martinez Ramirez, Rene V. Mayorga

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In our modern world, more physical transactions are being substituted by electronic transactions (i.e. banking, shopping, and payments), many businesses and companies are performing most of their operations through the internet. Instead of having a physical commerce, internet visitors are now adapting to electronic commerce (e-Commerce). The ability of web users to reach products worldwide can be greatly benefited by creating friendly and personalized online business portals. Internet visitors will return to a particular website when they can find the information they need or want easily. Dealing with this human conceptualization brings the incorporation of Artificial/Computational Intelligence techniques in the creation of customized portals. From these techniques, Fuzzy-Set technologies can make many useful contributions to the development of such a human-centered endeavor as e-Commerce. The main objective of this paper is the implementation of a Paradigm for the Intelligent Design and Operation of Human-Computer interfaces. In particular, the paradigm is quite appropriate for the intelligent design and operation of software modules that display information (such Web Pages, graphic user interfaces GUIs, Multimedia modules) on a computer screen. The human conceptualization of the user personal information is analyzed throughout a Cascaded Fuzzy Inference (decision-making) System to generate the User Ascribe Qualities, which identify the user and that can be used to customize portals with proper Web links.

Keywords: Fuzzy Logic, Internet, Electronic Commerce, Intelligent Portals, Electronic Shopping.

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Authors: Harshavardhan Reddy, Balaji Subramanian

Abstract:

A systematic parametric study to find the optimum Bellmouth profile by relating geometric and performance parameters to satisfy a set of specifications is reported. A careful aerodynamic design of Bellmouth intake is critical to properly direct the flow with minimal losses and maximal flow uniformity into the honeycomb located inside the settling chamber of an indraft wind tunnel, thus improving the efficiency of the entire unit. Design charts for elliptically profiled Bellmouth's with two different contraction ratios (9 and 18) and three different test section speeds (25 m/s, 50 m/s, and 75 m/s) were presented. A significant performance improvement - especially in the coefficient of discharge and in the flow angularity and boundary layer thickness at the honeycomb inlet - was observed when an entry corner radius (r/D = 0.08) was added to the Bellmouth profile. The nonuniformity at the honeycomb inlet drops by about three times (~1% to 0.3%) when moving from square to regular octagonal cross-section. An octagonal cross-sectioned Bellmouth intake with L/d = 0.55, D/d = 1.625, and r/D = 0.08 met all the four target performance specifications and is proposed as the best choice for a low-speed wind tunnel.

Keywords: Bellmouth intake, low-speed wind tunnel, coefficient of discharge, nonuniformity, flow angularity, boundary layer thickness, CFD, aerodynamics.

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Authors: Marcin Ciura, Damian Grund, S

Abstract:

This paper describes a system, in which various methods of text summarizing can be adapted to Polish. A structure of the system is presented. A modular construction of the system and access to the system via the Internet are signaled.

Keywords: Automatic summary generation, linguistic analysis, text generation.

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Authors: M. Sankari, R. Bremananth, C.Meena

Abstract:

Localization and Recognition of License registration characters from the moving vehicle is a computationally complex task in the field of machine vision and is of substantial interest because of its diverse applications such as cross border security, law enforcement and various other intelligent transportation applications. Previous research used the plate specific details such as aspect ratio, character style, color or dimensions of the plate in the complex task of plate localization. In this paper, license registration character is localized by Enhanced Weight based density map (EWBDM) method, which is independent of such constraints. In connection with our previous method, this paper proposes a method that relaxes constraints in lighting conditions, different fonts of character occurred in the plate and plates with hand-drawn characters in various aspect quotients. The robustness of this method is well suited for applications where the appearance of plates seems to be varied widely. Experimental results show that this approach is suited for recognizing license plates in different external environments. 

Keywords: Character segmentation, Connectivity checking, Edge detection, Image analysis, license plate localization, license number recognition, Quality frame selection

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Authors: Chryssa Thermolia, Ekaterini S. Bei, Stelios Sotiriadis, Kostas Stravoskoufos, Euripides G.M. Petrakis

Abstract:

Moving into a new era of healthcare, new tools and devices are developed to extend and improve health services, such as remote patient monitoring and risk prevention. In this concept, Internet of Things (IoT) and Cloud Computing present great advantages by providing remote and efficient services, as well as cooperation between patients, clinicians, researchers and other health professionals. This paper focuses on patients suffering from bipolar disorder, a brain disorder that belongs to a group of conditions called affective disorders, which is characterized by great mood swings. We exploit the advantages of Semantic Web and Cloud Technologies to develop a patient monitoring system to support clinicians. Based on intelligently filtering of evidence-knowledge and individual-specific information we aim to provide treatment notifications and recommended function tests at appropriate times or concluding into alerts for serious mood changes and patient’s nonresponse to treatment. We propose an architecture as the back-end part of a cloud platform for IoT, intertwining intelligence devices with patients’ daily routine and clinicians’ support.

Keywords: Bipolar disorder, intelligent systems patient monitoring, semantic web technologies, IoT.

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Authors: Manuela Nayantara Jeyaraj

Abstract:

Green IT is a term that refers to the collective strategic and tactical practices that unswervingly condense the carbon footprint to a diminished proportion in an establishment’s computing procedures. This concept has been tightly knit with IT related organizations; hence it has been precluded to be applied within non-IT organizations in Sri Lanka. With the turn of the century, computing technologies have taken over commonplace activities in every nook and corner in Sri Lanka, which is still on the verge of moving forth in its march towards being a developed country. Hence, it needs to be recursively proven that non-IT industries are well-bound to adhere to ‘Green IT’ practices as well, in order to reduce their carbon footprint and move towards considering the practicality of implementing Green-IT practices within their work-arounds. There are several spheres that need to be taken into account in creating awareness of ‘Green IT’, such as the economic breach, technologies available, legislative bounds, community mind-set and many more. This paper tends to reconnoiter causes that currently restrain non-IT organizations from considering Green IT concepts. By doing so, it is expected to prove the beneficial providence gained by implementing this concept within the organization. The ultimate goal is to propose feasible ‘Green IT’ practices that could be implemented within the context of Sri Lankan non-IT sectors in order to ensure that organization’s sustainable growth towards a long term existence.

Keywords: Computing practices, green IT, non-IT industries, Sri Lanka, sustainability.

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