Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1165
Security in Fog Computing through Encryption
Статья научная
Cloud computing is considered as one of the most exciting technology because of its flexibility and scalability. The main problem that occurs in cloud is security. To overcome the problems or issues of security, a new technique called fog-computing is evolved. As there are security issues in fog even after getting the encrypted data from cloud, we implemented the process of encryption using AES algorithm to check how it works for the fog. So far, to our analysis AES algorithm is the most secured process of encryption for security. Three datasets of different types are considered and applied the analysed encryption technique over those datasets. On validation, entire data over datasets is being accurately encrypted and decrypted back as well. We took android mobile as an edge device and deployed the encryption over datasets into it. Further, performance of encryption is evaluated over selected datasets for accuracy if the entire data is correctly encrypted and decrypted along with the time, User load, Response time, Memory Utilization over file size. Further best and worst cases among the datasets are analysed thereby evaluating the suitability of AES in fog.
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Segmentation of Isolated and Touching Characters in Offline Handwritten Gurmukhi Script Recognition
Статья научная
Segmentation of a word into characters is one of the important challenges in optical character recognition. This is even more challenging when we segment characters in an offline handwritten document. Touching characters make this problem more complex. In this paper, we have applied water reservoir based technique for identification and segmentation of touching characters in handwritten Gurmukhi words. Touching characters are segmented based on reservoir base area points. We could achieve 93.51% accuracy for character segmentation with this method. If the characters are neither broken nor overlapping, then this technique shall produce even better results.
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Selecting Appropriate Requirements Management Tool for Developing Secure Enterprises Software
Статья научная
This paper discusses about the significance of selecting right requirements management tools. It’s no secret that poorly understood user requirements and uncontrolled scope creep to many software project failures. Many of the application development professionals buy wrong tools for the wrong reasons. To avoid purchasing the more complex and expensive tool, the organization needs to be realistic about the particular problem for which they opt. Software development organizations are improving the methods, they use to gather, analyze, trace, document, prioritize and manage their requirements. This paper considers four leading Requirements Management tools; Analyst Pro, CORE, Cradle and Caliber RM, the focus is to select the appropriate tool according to their capabilities and customers need.
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Selecting appropriate metrics for evaluation of recommender systems
Статья научная
The abundance of information on the web makes it difficult for users to find items that meet their information need effectively. To deal with this issue, a large number of recommender systems based on different recommender approaches were developed which have been used successfully in a wide variety of domains such as e-commerce, e-learning, e-resources, and e-government among others. Moreover, in order for a recommender system to generate good quality of recommendations, it is essential for a researcher to find the most suitable evaluation metric which best matches a given recommender algorithm and a recommender's task. However, with the availability of several recommender tasks, recommender algorithms, and evaluation metrics, it is often difficult for a researcher to find their best combination. This paper aims to discuss various evaluation metrics in order to help researchers to select the most appropriate metric which matches a given task and an algorithm so as to provide good quality of recommendations.
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Selection of Optimum Rule Set of Two Dimensional Cellular Automata for Some Morphological Operations
Статья научная
The cellular automaton paradigm is very appealing and its inherent simplicity belies its potential complexity. Two dimensional cellular automata are significantly applying to image processing operations. This paper describes the application of cellular automata (CA) to various morphological operations such as thinning and thickening of binary images. The description about the selection of the optimum rule set of two dimensions cellular automata for thinning and thickening of binary images is illustrated by this paper. The selection of the optimum rule set from large search space has been performed on the basis of sequential floating forward search method. The misclassification error between the images obtained by the standard function and the one obtained by cellular automata rule is used as the fitness function. The proposed method is also compared with some standard methods and found suitable for the purpose of morphological operations.
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Self-organized Detection of Relationships in a Network
Статья научная
Multistate operations within a network result in high-dimensional, multivariate temporal data, and are useful for systems, which monitor access to network entities like resources, objects, etc. Efficient self organization of such multistate network operations stored in databases with respect to relationships amongst users or between a user and a data object is an important and a challenging problem. In this work, a layer is proposed where discovered relationship patterns amongst users are classified as clusters. This information along with attributes of involved users is used to monitor and extract existing and growing relationships. The correlation is used to help generate alerts in advance due to internal user-object interactions or collaboration of internal as well as external entities. Using an experimental setup, the evolving relationships are monitored, and clustered in the database.
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Semantic Indexing of Web Documents Based on Domain Ontology
Статья научная
The first phase of reverse engineering of web-oriented applications is the extraction of concepts hidden in HTML pages including tables, lists and forms, or marked in XML documents. In this paper, we present an approach to index semantically these two sources of information (HTML page and XML document) using on the one hand, domain ontology to validate the extracted concepts and on the other hand the similarity measurement between ontology concepts with the aim of enrichment the index. This approach will be conceived in three steps (modeling, attaching and Enrichment) and thereafter, it will be realized and implemented by examples. The obtained results lead to better re-engineering of web applications and subsequently a distinguished improvement in the web structuring.
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Semantic Multi-granular Lock model for Object Oriented Distributed Systems
Статья научная
In object oriented distributed systems (OODS), the objects are viewed as resources. Concurrency control techniques are usually applied on the database tier. This has the limitations of lack of support of legacy files and requirement of separate concurrency control mechanisms for each database model. Hence concurrency control on the objects at server tier is explored. To implement concurrency control on the objects participating in a system, the impact of method types, properties and class relationships namely inheritance, association and aggregation are to be analyzed. In this paper, the types and properties of classes and attributes are analysed. The semantics of the class relationships are analysed to ascertain their lock modes, granule sizes for defining concurrency control in OODS. It is also intended to propose compatibility matrix among all these object relationships.
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Sentiment Analysis of RSS Feeds on Sports News – A Case Study
Статья научная
With the advent of online social media, such as articles, websites, blogs, messages, posts, news channels, and by and large web content has drastically changed the way individuals take a glimpse at different things around them. Today, it's an everyday practice for some individuals to read the news on the web. Sentiment analysis (also called opinion mining) alludes to the utilization of natural language processing, content investigation, and computational linguistics to distinguish and separate subjective data in source materials. Sentiment analysis is broadly applied to online reviews, news feeds and social networking for a wide variety of applications, ranging from marketing to client services. Sentiment analysis emphasizes on the classification of textual data into positive, negative and neutral categories. This research is an endeavor to the case study that calculates news polarity or emotions on different sports feeds which may influence changes in sports news development patterns. The interest of this approach is to generate various text analytics that computes feelings from all pertinent ongoing sports news accessible out in the public domain. The significance and application value of sentiment analysis of RSS feeds in this study is to distinguish between positive feeds and negative feeds on sports that could affect readers or users minds in order to improve RSS feeds messaging broadcast among folks. The methodology utilizes the sentiment analysis techniques using two different online open-source sentiment analysis tools in Rich Site Summary (RSS) news feeds that have an influence on sports-related broadcast esteems.
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Service Based Cooperation Patterns to Support Flexible Inter-Organizational Workflows
Статья научная
Service Oriented Architecture (SOA) is a paradigm that provides important advantages like interoperability, reusability and flexibility, particularly beneficial for B2B applications. In the current paper, we consider specific architectures of inter-organizational workflows (IOWF) fairly widespread in the B2B area and implementing different cooperation schemas. Our aim is to propose new generic IOWF-architectures by using the SOA paradigm in order to obtain IOWF models flexible enough to ease their adaptation, evolution and reuse. For that, we introduce the concept of Service-Based Cooperation Pattern (SBCP) that supports the definition of IOWF models based on services. A SBCP is defined by three main dimensions: the distribution of services, the control of execution and the structure of interaction between services. Also, we define a concept of composite cooperation pattern based on the combination of elementary patterns. We illustrate our approach by a general description of our cooperation framework called “S-IOFLOW” that supports the implementation of IOWF models obeying to the described SBCP. Three main points characterize our approach: (i) the use of a pattern-based approach; (ii) the definition of composite patterns by reusing elementary ones and (iii) the support of several cooperation schemas with different types of control.
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Статья научная
This paper articulates how Service Oriented Architecture (SOA) and cloud computing together can facilitate technology setup in Telemetry (TM) processing with a case study from the Egyptian space program (ESP) and a comparative study with space situational awareness (SSA) program in European space agency (ESA), Moreover, this paper illustrates how cloud computing services and deployment models enable software and hardware decoupling and making flexible TM data analysis possible. The large amount of available computational resources facilitates a shift in approaches to software development, deployment and operations.
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Several cancer classifiers combined with PLS-DR for base on gene expression profile
Статья научная
It is known that Logistic Regression coupled with Partial Least Squares dimension reduction (PLSDR-LD) is capable of extracting a great deal of useful information for classification from gene expression profile and getting a rather high classification accuracy rate. In this study, we replace the logistic function of Logistic Regression with several functions which are similar to logistic function in appearance, and apply these functions to the analysis of microarray data sets from two cancer gene expression studies. We compare these newly introduced models with PLSDR-LD proposed in the literature. The most effective models with good prediction precision are lastly provided through analyzing the results of two experiments.
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Shape Classification Based on Normalized Distance and Angle Histograms Using PNN
Статья научная
This study presents an attempt to develop a reliable computerized algorithm, which could classify images into predetermined classes. For this purpose, the histogram of the normalized distance between each two points of the image (algorithm I) and the histogram of normalized distances between three points and the normalized angle of the image edge points (algorithm II) are analyzed. The probabilistic neural network (PNN) is implemented to do shape classification. Our proposed approach is tested on ten classes of MPEG-7 image database. It has been shown that feature extraction based on the distance histogram (algorithm I and algorithm II) is efficient due to its potential to preserve interclass and intra-class variation. In addition, these algorithms ensur invariance to geometric transformations (e.g. translation, rotation and scaling). The best classification accuracy is achieved by eight classes with the total accuracy of 90% and 92.5% for algorithm I and algorithm II, respectively. The reported experiment reveal that the proposed classification algorithm could be useful in the study of MPEG-7 shapes.
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Shortcomings of ultrasonic obstacle detection for vehicle driver assistance and profiling
Статья научная
Obstacle detection is a challenging problem that has attracted much attention recently, especially in the context of research in self-driving car technologies. A number of obstacle detection technologies exist. Ultrasound is among the commonly used technologies due to its low cost compared to other technologies. This paper presents some findings on the research that has been carried out by the authors with regard to vehicle driver assistance and profiling. It discusses an experiment for detection of obstacles in a vehicle driver’s operational environment using ultrasound technology. Experiment results clearly depict the capabilities and limitations of ultrasound technology in detection of obstacles under motion and obstacles with varied surfaces. Ultrasound’s wavelength, beam width, directionality among others are put into consideration. Pros and cons of other technologies that could replace ultrasound, for instance RADAR and LIDAR technologies are also discussed. The study recommends sensor fusion where several types of sensor technologies are combined to complement one another. The study was a technical test of configurable technology that could guide future studies on obstacle detection intending to use infrared, sound, radio or laser technologies particularly when both the sensor and obstacle are in motion and when obstacles have differing unpredictable surface properties.
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Similar Words Identification Using Naive and TF-IDF Method
Статья научная
Requirement satisfaction is one of the most important factors to success of software. All the requirements that are specified by the customer should be satisfied in every phase of the development of the software. Satisfaction assessment is the determination of whether each component of the requirement has been addressed in the design document. The objective of this paper is to implement two methods to identify the satisfied requirements in the design document. To identify the satisfied requirements, similar words in both of the documents are determined. The methods such as Naive satisfaction assessment and TF-IDF satisfaction assessment are performed to determine the similar words that are present in the requirements document and design documents. The two methods are evaluated on the basis of the precision and recall value. To perform the stemming, the Porter’s stemming algorithm is used. The satisfaction assessment methods would determine the similarity in the requirement and design documents. The final result would give a accurate picture of the requirement satisfaction so that the defects can be determined at the early stage of software development. Since the defects determines at the early stage, the cost would be low to correct the defects.
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Simple detection and classification of road lanes based on image processing
Статья научная
Road accidents, besides being one of the main causes of mortality, have an economic impact on vehicle owners. Several conditions as driver imprudence, road conditions and obstacles are the main factor that will cause accidents. The most important automotive industries are incorporating technology to reduce risk in vehicles. In this way, lane detection systems have important attention, because from this data is possible to determine risk situations such as presence of obstacles, incorrect lane changes or lane departures. This paper proposes a technique for lane detection, based on image processing, which allows identifying the position of lateral lanes and their type. The method is composed of four stages: edge enhancement, potential lanes detection, post-processing and color lane estimation. The method was proved using image dataset and video captures over 12.000 frames. The accuracy of the system was of 91.9%.
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Simulation Based Comparison of Geo-Location Methods in Wireless Networks
Статья научная
There are many Geo-Location techniques proposed in cellular networks. They are mainly classified based on the parameters used to extract location information. In this study it is tried to have a new look to these positioning methods and to classify them differently regardless of parameters type. We classified these techniques base on mathematical algorithms which is used to derive location information of users in the network. Such algorithms are divided into three main subclasses in here, estimation theory based (MUSIC, ESPIRIT), Meta-heuristic (Genetic, PSO...) and filtering approaches (Kalman, Particle, Grid, MH ). The proofs and details of how to apply techniques are presented and the simulation results are given.
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Simulation and Tuning of PID Controllers using Evolutionary Algorithms
Статья научная
The Proportional Integral Derivative (PID) controller is the most widely used control strategy in the Industry. The popularity of PID controllers can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. Biologically inspired evolutionary strategies have gained importance over other strategies because of their consistent performance over wide range of process models and their flexibility. The level control systems on Deaerator, Feed Water Heaters, and Condenser Hot well are critical to the proper operation of the units in Nuclear Power plants. For Precise control of level, available tuning technologies based on conventional optimization methods are found to be inadequate as these conventional methods are having limitations. To overcome the limitations, alternate tuning techniques based on Genetic Algorithm are emerging. This paper analyses the manual tuning techniques and compares the same with Genetic Algorithm tuning methods for tuning PID controllers for level control system and testing of the quality of process control in the simulation environment of PFBR Operator Training Simulator(OTS).
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Simulation of the operation of induction machines at frequencies other than 50 Hz
Статья научная
In the paper is proposed an induction machine model that corresponds to operating conditions at the change of frequency. The applicability of simulation method for the analysis of its operation at the frequencies other than 50 Hz was examined. The operation of the induction machine which control parameter is the frequency of controlled current was particularly analyzed. The evaluation of the proposed model was performed using the adapted subprogram in the psbdrive MATLAB Simulink package. For the verification of the results, the method of verified simulation was used, as a part of the method which was derived from the artificial intelligence algorithm. It has been confirmed that by controlling the frequencies and current of the induction machine, the ability to work in overloads can be maintained and this type of control has certain advantages in the frequency regulation of the drive with variable loads.
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Single Channel Speech Separation Using an Efficient Model-based Method
Статья научная
The subject of extracting multiple speech signals from a single mixed recording, which is referred to single channel speech separation, has received considerable attention in recent years and many model-based techniques have been proposed. A major problem of most of these systems is their inability to deal with the situation in which the signals are combined at different levels of energies because they assume that the data used in the test and training phase have equal levels of energies, where, this assumption hardly occurs in reality. Our proposed method based on MIXMAX approximation and sub-section vector quantization (VQ) is an attempt to overcome this limitation. The proposed technique is compared with a technique in which a gain adapted minimum mean square error estimator is derived to estimate the separated signals. Through experiments we show that our proposed method outperforms this method in terms of SNR results and also reduces computational complexity.
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