Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1195
APESS - A Service-Oriented Data Mining Platform: Application for Medical Sciences
Статья научная
The domain medical and public health remains the principal preoccupation of all world population. It makes recourse at several means from various disciplines, including for instance epidemiology, to help clinicians in decision processes. This paper proposes an Assistance Platform for Epidemiological Searches and Surveillance (APESS) for service-oriented data mining in the field of epidemiology. The main aim of the present platform is to build a system that enables extracting predictive rules, flexible and scalable for aid in decision-making by trades' experts. Results showed that the current system provides prediction models of chronic diseases (epidemiological prediction rules), using classification algorithms.
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AQUAZONE: A Spatial Decision Support System for Aquatic Zone Management
Статья научная
During the last years, the Sebkha Lake of Oran (Algeria) has been the subject of many studies for its protection and recovery. Many environmental and wetlands experts are a hope on the integration of this rich and fragile space, ecologically, as a pilot project in "management of water tides". Support the large of Sebkha (Lake) of Oran is a major concern for governments looking to make this a protected natural area and viable place. It was a question of putting in place a management policy to respond to the requirements of economic, agricultural and urban development and the preservation of this natural site through management of its water and the preservation of its quality. The objective of this study is to design and develop a Spatial Decision Support System, namely AQUAZONE, able to assist decision makers in various natural resource management projects. The proposed system integrates remote sensing image processing methods, from display operations, to analysis results, in order to extract useful knowledge to best natural resource management, and in particular define the extension of Sebkha Lake of Oran (Algeria). Two methods were applied to classify LANDSAT 5 TM images of Oran (Algeria): Fuzzy C-Means (FCM) applied on multi spectral images, and the other that comes with the manual which is the Ordered Queue-based Watershed (OQW). The FCM will serve as initialization phase, to automatically discover the different classes (urban, forest, water, etc..) from a LANDSAT 5 TM images, and also minimize ambiguity in grayscale and establish Land cover map of this region.
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ATAM-based Architecture Evaluation Using LOTOS Formal Method
Статья научная
System Architecture evaluation and formal specification are the significant processes and practical endeavors in all domains. Many methods and formal descriptive techniques have been proposed to make a comprehensive analysis and formal representation of a system architecture. This paper consists of two main parts, in first we evaluated system performance, quality attribute in Remote Temperature Sensor clients-Server architecture by implementing an ATAM model, which provides a comprehensive support for evaluation of architecture designs by considering design quality attributes and how they can be represented in the architecture. In the second part, we computed the selected system architecture in ISO standards formal description technique LOTOS with which a system can be specified by the temporal relation between interactions and behavior of the system. Our proposed approach improves on factors such as ambiguity, inconsistency and incompleteness in current system architecture.
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AWG Based Optical Packet Switch Architecture
Статья научная
This paper discusses an optical packet switch (OPS) architecture, which utilizes the components like optical reflectors, tunable wavelength converters (TWCs), arrayed waveguide grating (AWG) and pieces of fiber to realize the switching action. This architecture uses routing pattern of AWG, and its symmetric nature, to simplify switch operation significantly. It is also shown that using multi-wavelengths optical reflector, length of delay lines can be reduced to half of its original value. This reduction in length is useful for comparatively larger size packets as for them. It can grow up some kilometers. The considered architecture is compared with already published architecture. Finally, modifications in the architecture are suggested such that switch can be efficiently placed in the backbone network.
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About One Model Strategic Game of Collective Choice
Статья научная
A model of dyadic non-cooperative game Γ(H) is discussed in the paper for the set of one and the same players’ strategies. The players make their choice sitting round the table and have the opportunity to coordinate only the meanings of utilities in every situation. Therefore the players’ payoffs are given by 2×2 matrixes. A notion “the equalized situation” in mixed strategies which is at the same time the equilibrium is introduced. The theorem has been proved, which establishes the conditions of existance of an equalized situation in the given game. In the case of the existence algorithm is constructed. If equalized situation doesn’t exist in the game, then there exists the equilibrium situation in the pure strategies and it is possible to find it by analysis of situations. Γ(H) game’s with bimatrix game in case of two players is given. The players’ conditions of optimal mixed strategies existence in game is written. Relevant examples are solved and Γ(H) game’s application for finite amount of players’ is discussed.
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Abuse-Free Optimistic Contract Signing Using RSA for Multiuser Systems
Статья научная
Multi-party contract signing (MPCS) is a way for signers to agree on a predetermined contract by exchanging their signature. This matter has become crucial with the growing number of communications. In this paper, we focus mainly on studying the state of the art protocols and more specifically the cryptography involved. We identify the major advances in MPCS, highlight a few gaps with the current protocols and propose an algorithm for contract signing to be abuse-free, optimistic for many signers in industrial standards.
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Accelerated K-means Clustering Algorithm
Статья научная
Optimizing K-means is still an active area of research for purpose of clustering. Recent developments in Cloud Computing have resulted in emergence of Big Data Analytics. There is a fresh need of simple, fast yet accurate algorithm for clustering huge amount of data. This paper proposes optimization of K-means through reduction of the points which are considered for re-clustering in each iteration. The work is generalization of earlier work by Poteras et al who proposed this idea. The suggested scheme has an improved average runtime. The cost per iteration reduces as number of iterations grow which makes the proposal very scalable.
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Accelerated Simulation Scheme for Solving Financial Problems
Статья научная
The Monte Carlo simulation method uses random sampling to study properties of systems with components that behave in a random state. More precisely, the idea is to simulate on the computer the behavior of these systems by randomly generating the variables describing the behavior of their components. In this paper, we propose an efficient and reliable simulation scheme based on Monte Carlo algorithm and combining two variance reduction procedures. We simulate a European option price numerically using the proposed simulation scheme.
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Accident Response Time Enhancement Using Drones: A Case Study in Najm for Insurance Services
Статья научная
One of the main reasons for mortality among people is traffic accidents. The percentage of traffic accidents in the world has increased to become the third in the expected causes of death in 2020. In Saudi Arabia, there are more than 460,000 car accidents every year. The number of car accidents in Saudi Arabia is rising, especially during busy periods such as Ramadan and the Hajj season. The Saudi Arabia’s government is making the required efforts to lower the nations of car accident rate. This paper suggests a business process improvement for car accident reports handled by Najm in accordance with the Saudi Vision 2030. According to drone success in many fields (e.g., entertainment, monitoring, and photography), the paper proposes using drones to respond to accident reports, which will help to expedite the process and minimize turnaround time. In addition, the drone provides quick accident response and recording scenes with accurate results. The Business Process Management (BPM) methodology is followed in this proposal. The model was validated by comparing before and after simulation results which shows a significant impact on performance about 40% regarding turnaround time. Therefore, using drones can enhance the process of accident response with Najm in Saudi Arabia.
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Accuracy evaluation of brain tumor detection using entropy-based image thresholding
Статья научная
In this paper, the accuracy of the entropy-based thresholding approaches in brain tumor detection framework is investigated. Entropies are information gain methods that have been used for image thresholding with various application and different image modalities. The accuracy of the existing entropies for image thresholding has been studied in general domain (e.g.: natural images) and were not compared thoroughly. Thus, a framework for brain tumor segmentation is proposed with the core process of the image thresholding, in order to evaluate the accuracy of the entropies. Five entropies, namely, Renyi, Maximum, Minimum, Tsallis and Kapur are evaluated. Moreover, the aggregation of entropies was implemented and evaluated. The results show that the maximum entropy is the best for brain tumor detection. Moreover, it was shown that aggregation of entropies output does not enhance the result, however, it works as automatic selection of the best result and produces the results with the highest accuracy.
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Accurate Anomaly Detection using Adaptive Monitoring and Fast Switching in SDN
Статья научная
Software defined networking (SDN) is rapidly evolving technology which provides a suitable environment for easily applying efficient monitoring policies on the networks. SDN provides a centralized control of the whole network from which monitoring of network traffic and resources can be done with ease. SDN promises to drastically simplify network monitoring and management and also enable rapid innovation of networks through network programmability. SDN architecture separates the control of the network from the forwarding devices. With the higher innovation provided by the SDN, security threats at open interfaces of SDN also increases significantly as an attacker can target the single centralized point i.e. controller, to attack the network. Hence, efficient adaptive monitoring and measurement is required to detect and prevent malicious activities inside the network. Various such techniques have already been proposed by many researchers. This paper describes a work of applying efficient adaptive monitoring on the network while maintaining the performance of the network considering monitoring overhead over the controller. This work represents effective bandwidth utilization for calculation of threshold range while applying anomaly detection rules for monitoring of the network. Accurate detection of anomalies is implemented and also allows valid users and applications to transfer the data without any restrictions inside the network which otherwise were considered as anomalies in previous technique due to fluctuation of data and narrow threshold window. The concept of fast switching also used to improve the processing speed and performance of the networks.
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Active Selection Constraints for Semi-supervised Clustering Algorithms
Статья научная
Semi.-supervised clustering algorithms aim to enhance the performance of clustering using the pairwise constraints. However, selecting these constraints randomly or improperly can minimize the performance of clustering in certain situations and with different applications. In this paper, we select the most informative constraints to improve semi-supervised clustering algorithms. We present an active selection of constraints, including active must.-link (AML) and active cannot.-link (ACL) constraints. Based on Radial-Bases Function, we compute lower-bound and upper-bound between data points to select the constraints that improve the performance. We test the proposed algorithm with the base-line methods and show that our proposed active pairwise constraints outperform other algorithms.
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Статья научная
An evolving weighted neuro-neo-fuzzy-ANARX model and its learning procedures are introduced in the article. This system is basically used for time series forecasting. It's based on neo-fuzzy elements. This system may be considered as a pool of elements that process data in a parallel manner. The proposed evolving system may provide online processing data streams.
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Adaptive Guidance based on Context Profile for Software Process Modeling
Статья научная
This paper aims to define an adaptive guidance for software process modeling. The proposed guidance approach is based on development’s profile context (actor’s role in the process, actor’s qualification and related activities in progress). We introduce new guidance concepts through adaptive guidance meta-model (AGM) allowing specific assistance interventions (corrective, constructive and automatic guidance). We illustrate our guidance approach using SPEM formalism extended with these new guidance concepts.
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Статья научная
Polarimetric radar images suffer from the presence of speckles that degrade the received signal and introduce untruthful indications about the nature of the objects. In this study, we proposed a new framework to filter polarimetric images in which the edges and the channel correlation are preserved. Through a proposed scheme, the image is segmented into groups of regular and irregular pixels. The segmentation process is based on the homogeneity of the texture variation throughout the image. In the homogeneous area, speckle reduction is performed using the adaptive local mean of the neighboring pixels. For non-homogeneous surfaces, the scheme works independently for each set of resolution cells using the general product model containing both intensity and texture information. Quantitative and qualitative assessments confirmed that the proposed filter achieved highly ranked order; it has the ability to preserve fine details, polarimetric information, and to maintain the scattering mechanism of the different objects.
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Adaptive Modeling of Urban Dynamics during Armada Event using CDRs
Статья научная
This study investigates the mobile phone data during ephemeral event (Armada). The statistical techniques have been used for modeling human mobility collectively and individually. The undertaken substantial parameters are: inter-event times, travel distances (displacements), and radius of gyration. They have been analyzed and simulated using computing platform by integrating various applications for huge database management, visualization, analysis, and simulation. Accordingly, the general population pattern law has been extracted. This study has revealed the individuals mobility in dynamic perspective for 615,712 mobile users, also the simulated observed data are classified according to general, work, and off days.
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Статья научная
The world is going to be a universal digital village and from the flow of this digitalization Bangladesh also riding of the tide. The key points of these digitalization is young generations basically university students of Bangladesh. ICT and Internet is a new trend for this country that’s the main reasons to encompass this by opportunity by young students. Bangladeshi young generations have also addicted in the upper tier of this list. The addiction of ICT & internet is more on the young generations than any other parts of the people generally in the third world countries. The main objective of this paper is to investigate the excessive use of Information and Communication Technology (ICT) and internet by the university students in Bangladesh. The study had collected the data from 24 public and private universities in Bangladesh out of 135. The study was used the simple random sampling (SRS) for analyzing the sample size with IBM SPSS 23.
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Advance Mining of Temporal High Utility Itemset
Статья научная
The stock market domain is a dynamic and unpredictable environment. Traditional techniques, such as fundamental and technical analysis can provide investors with some tools for managing their stocks and predicting their prices. However, these techniques cannot discover all the possible relations between stocks and thus there is a need for a different approach that will provide a deeper kind of analysis. Data mining can be used extensively in the financial markets and help in stock-price forecasting. Therefore, we propose in this paper a portfolio management solution with business intelligence characteristics. We know that the temporal high utility itemsets are the itemsets with support larger than a pre-specified threshold in current time window of data stream. Discovery of temporal high utility itemsets is an important process for mining interesting patterns like association rules from data streams. We proposed the novel algorithm for temporal association mining with utility approach. This make us to find the temporal high utility itemset which can generate less candidate itemsets.
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Advanced Applications of Neural Networks and Artificial Intelligence: A Review
Статья научная
Artificial Neural Network is a branch of Artificial intelligence and has been accepted as a new computing technology in computer science fields. This paper reviews the field of Artificial intelligence and focusing on recent applications which uses Artificial Neural Networks (ANN’s) and Artificial Intelligence (AI). It also considers the integration of neural networks with other computing methods Such as fuzzy logic to enhance the interpretation ability of data. Artificial Neural Networks is considers as major soft-computing technology and have been extensively studied and applied during the last two decades. The most general applications where neural networks are most widely used for problem solving are in pattern recognition, data analysis, control and clustering. Artificial Neural Networks have abundant features including high processing speeds and the ability to learn the solution to a problem from a set of examples. The main aim of this paper is to explore the recent applications of Neural Networks and Artificial Intelligence and provides an overview of the field, where the AI & ANN’s are used and discusses the critical role of AI & NN played in different areas.
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Advanced Deep Learning Models for Accurate Retinal Disease State Detection
Статья научная
Retinal diseases are a significant challenge in the realm of medical diagnosis, with potential complications to vision and overall ocular health. This research endeavors to address the challenge of automating the detection of retinal disease states using advanced deep learning models, including VGG-19, ResNet-50, InceptionV3, and EfficientNetV2. Each model leverages transfer learning, drawing insights from a substantial dataset comprising optical coherence tomography (OCT) images and subsequently classifying images into four distinct retinal conditions: choroidal neovascularization, drusen, diabetic macular edema and a healthy state. The training dataset, sourced from repositories that are available to the public including OCT retinal images, spanning all four disease categories. Our findings reveal that among the models tested, EfficientNetV2 demonstrates superior performance, with a remarkable classification accuracy of 98.92%, precision of 99.6%, and a recall of 99.4%, surpassing the performance of the other models.
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