Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1165
Robust Sliding Mode Fuzzy Control of a Car Suspension System
Статья научная
Different characteristics can be considered in a suspension system design like: ride comfort, body travel, road handling and suspension travel. No suspension system can optimize all these parameters together but a better tradeoff among these parameters can be achieved in active suspension system. Objective of this paper is to establish a robust control technique of the active suspension system for a quarter-car model. The paper describes also the model and controller used in the study and discusses the vehicle response results obtained from a range of road input simulations. A comparison of robust suspension sliding fuzzy control and passive control is shown using MATLAB simulations.
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Robust monocular visual odometry trajectory estimation in urban environments
Статья научная
Visual SLAM (Simultaneous Localization and Mapping) is widely used in autonomous robots and vehicles for autonomous navigation. Trajectory estimation is one part of Visual SLAM. Trajectory estimation is needed to estimate camera position in order to align the real image locations. In this paper, we present a new framework for trajectory estimation aided by Monocular Visual Odometry. Our proposed method combines the feature points extracting and matching based on ORB (Oriented FAST and Rotated BRIEF) and PnP (Perspective-n-Point). Thus, it was used a Matlab® dynamic model and an OpenCV/C++ computer graphics platform to perform a very robust monocular Visual Odometry mechanism for trajectory estimation in outdoor environments. Our proposed method displays that meaningful depth estimation can be extracted and frame-to-frame image rotations can be successfully estimated and can be translated in large view even texture-less. The best key-points has been extracted from ORB key point detectors depend on their key-point response value. These extracted key points are used to decrease trajectory estimation errors. Finally, the robustness and high performance of our proposed method were verified on image sequences from public KITTI dataset.
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Robust, Secure and High Capacity Watermarking Technique based on Image Partitioning-Merging Scheme
Статья научная
This paper presents secure and high capacity watermarking technique using novel approach of Image Partitioning-Merging Scheme (IPMS). The IPMS is used as reduction method to reduce the size of watermark logically and increases security levels of proposed watermarking technique. The technique effectively uses special properties of Discrete Wavelet Transform (DWT), Fast Walse Hadamrd Transform (FWHT), Singular Value Decomposition (SVD) and proved strongly robust to 14 noise addition and filtering attacks. Fibonacci Lucas Transform (FLT) is used as effective scrambling technique to scramble the watermark to provide additional security in embedding process. Many researchers failed to achieve imperceptibility and robustness under high watermark embedding scenario with strong security provision as these quality parameters conflict each other. The novel technique presented here archives imperceptibility, high capacity watermark embedding, security and robustness against 14 noise addition and filtering attacks. The technique is non-blind and tested with grey scale cover images of size 512x512 and watermark images of size 512x512. The experimental results demonstrate that Lena image gives 75.8446dBs imperceptibility which is measured in terms of Peak signal to noise ratio. The robustness is measured in terms of normalized correlation (NC) equals to 1 showing exact recovery of watermark. The method is found strongly robust against noise addition and filtering attacks with compared to existing watermarking methods under consideration.
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Robustness Analysis of Layered Public Transport Networks Due to Edge Overload Breakdown
Статья научная
The robustness of urban bus-transport networks has important influence on the network performance. This paper proposes the model of layered public bus-transport network which is composed of the logical layer and the physical layer and expounds the relationship between these two layers. We map the bus-transport network into two spaces: space P and space L and take space P as logical layer while space L as physical layer. We define the load of edges in the physical layer according to the traffic flow in the logical layer and assume that a removed edge only leads to a redistribution of the load through it to its neighboring edges. We analysis the robustness of layered public bus-transport networks in the face of cascading failure under the case of removing the edge with the highest load and redistributing of the load. Through the simulation of the public bus-transport networks of three major cities in China, we find that in the layered public bus-transport network the traffic flow in the logical layer affects the distribution of load of edges in the physical layer. The removal of the edge with the highest load may lead to the cascading failures of the physical layer, and the avalanche size decreases with the increase of the tolerance parameter.
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SANB-SEB Clustering: A Hybrid Ontology Based Image and Webpage Retrieval for Knowledge Extraction
Статья научная
Data mining is a hype-word and its major goal is to extract the information from the dataset and convert it into readable format. Web mining is one of the applications of data mining which helps to extract the web page. Personalized image was retrieved in existing systems by using tag-annotation-demand ranking for image retrieval (TAD) where image uploading, query searching, and page refreshing steps were taken place. In the proposed work, both the image and web page are retrieved by several techniques. Two major steps are followed in this work, where the primary step is server database upload. Herein, database for both image and content are stored using block acquiring page segmentation (BAPS). The subsequent step is to extract the image and content from the respective server database. The subsequent database is further applied into semantic annotation based clustering (SANB) (for image) and semantic based clustering (SEB) (for content). The experimental results show that the proposed approach accurately retrieves both the images and relevant pages.
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SASMEDU: security assessment method of software in engineering education
Статья научная
Security and usability of web and mobile applications where users share their personal information have become to be a factor about which users should be careful. Rapid increase of developers, programming at early ages, desire for earning money by working freelance have caused widespread use of web and mobile applications and an increase of codes which contain vulnerabilities. Safe and good software development is also based on software lessons given to the students in high school or college years. This paper presents a developed testing and evaluation software in order to find out the leakages in the web applications which was developed by using asp.net, php and java languages. It is aimed that the developed analysis tool was designed to be used by engineering students as a training tool, in security courses by trainees and by programmers for testing. Within the scope of the study, security tests of web projects were carried out with static code analysis method in input control, metric analysis and style control phases. For testing the developed software tool, student web projects were used which were downloaded from "www.freestudentprojects.com" website. 10 test projects were tested in the stages of input control, metric analysis and style control. According to the results of the analysis, the errors were concentrated on Structural Query Language Injection and Cross Site Scripting attacks, which were developed by the students due to the lack of security audit in the projects.
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SBIoT: Scalable Broker Design for Real Time Streaming Big Data in the Internet of Things Environment
Статья научная
Thanks to the recent development in the technology number of IoT devices increased dramatically. Therefore, industries have been started to use IoT devices for their business processes. Many systems can be done automatically thanks to them. For this purpose, there is a server to process sensors data. Transferring these data to the server without any loss has crucial importance for the accuracy of IoT applications. Therefore, in this thesis a scalable broker for real time streaming data is proposed. Open source technologies, which are NoSql and in-memory databases, queueing, full-text index search, virtualization and container management orchestration algorithms, are used to increase efficiency of the broker. Firstly, it is planned to be used for the biggest airport in Turkey to determine the staff location. Considering the experiment analysis, proposed system is good enough to transfer data produced by devices in that airport. In addition to this, the system can adapt to device increase, which means if number of devices increasing in time, number of nodes can be increased to capture more data.
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Статья научная
The proposed framework shows the customized approach that improves the software development process by using the Agile techniques. The proposed research work blends the CMMI practices according to Agile methods of development (specially Scrum, because Scrum is extremely used by Pakistani firms) CMMI is a model for the process improvement within organization, but it is too expensive to implement all practices of CMMI for each level. So for small size organizations, at level 2, 3 there are some practices which are helpful in managing and defining their software development process. By implementing the proposed framework, organizations can improve their maturity and capability level. There are several problems while working on Agile, many management areas can be neglected. In SCXTREME there are eight process area selected from CMMI and fit them according to Agile, so agile can be suitable for Pakistani organization by implementing some process area at level 2 and level 3.
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SDRED: smart data retrieval engine for databases
Статья научная
Today computers are continuously betrothed in almost all domains and organizations. Thus, databases act as the heart for storing and retrieving information that contain huge digital data. However, in order to interact with such databases, it is necessary to have knowledge about the Structured Query Language (SQL), which is difficult for non-expert users to understand and manipulate. So, there is an emergent need to develop a smart and a user friendly computational technique to interact with databases. The current work proposed a smart technique that can handle such context. The proposed “Smart Data Retrieval Engine for Databases (SDRED)” provided an environment that allows a non-expert user to write and to execute the database queries easily. Furthermore, it retrieved the data stored in databases without a prior knowledge of the SQL. SDRED, which enables the non-expert user to write database queries in natural language (such as English) and to convert them to their SQL query equivalents. The current work presented a detailed design and evaluation for the proposed system by executing different database queries in English. The results established that SDRED successfully converted the non-expert user’s natural language queries into their equivalent SQL queries, thereby providing an easy and user-friendly environment to interact with databases.
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SQL Versus NoSQL Movement with Big Data Analytics
Статья научная
Two main revolutions in data management have occurred recently, namely Big Data analytics and NoSQL databases. Even though they have evolved with different purposes, their independent developments complement each other and their convergence would benefit businesses tremendously in making real-time decisions using volumes of complex data sets that could be both structured and unstructured. While on one hand many software solutions have emerged in supporting Big Data analytics, on the other, many NoSQL database packages have arrived in the market. However, they lack an independent benchmarking and comparative evaluation. The aim of this paper is to provide an understanding of their contexts and an in-depth study to compare the features of four main NoSQL data models that have evolved. The performance comparison of traditional SQL with NoSQL databases for Big Data analytics shows that NoSQL database poses to be a better option for business situations that require simplicity, adaptability, high performance analytics and distributed scalability of large data. This paper concludes that the NoSQL movement should be leveraged for Big Data analytics and would coexist with relational (SQL) databases.
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SQUIREL: Semantic Querying Interlinked OWL-S traveling Process Models
Статья научная
With the advent of new forms of information and communication technologies, the consumer needs to combine and customize different travel components as a complete travel package, namely: Dynamic Packaging Technology. Nevertheless, disparate tourist offers and services make it difficult for consumer to use them effectively. Therefore, our paper presents an intelligent querying framework of OWL-S travel services, called SQUIREL composition engine. It uses Semantic Web Services (SWSs) technologies combined with the useful of Linked e-tourism Data concept to fulfill the preferences and constraints of the e-tourist any time. This purpose supports SWSs pre-selection through the valuation of the rewritten SPARQL consumer query at runtime that manages dynamic service dependencies extracted from Linked e-tourism Data and returns the SWSs endpoint. Then, SQUIREL catches this endpoint and makes the necessary optimizations to refine it to its relevant atomic processes needed to be composed using matrix computation. However, the experimental results indicate that this method owns both lower computation cost and higher success ratio of fine-grained discovery-based atomic processes composition.
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SSKHOA: Hybrid Metaheuristic Algorithm for Resource Aware Task Scheduling in Cloud-fog Computing
Статья научная
Cloud fog computing is a new paradigm that combines cloud computing and fog computing to boost resource efficiency and distributed system performance. Task scheduling is crucial in cloud fog computing because it decides the way computer resources are divided up across tasks. Our study suggests that the Shark Search Krill Herd Optimization (SSKHOA) method be incorporated into cloud fog computing's task scheduling. To enhance both the global and local search capabilities of the optimization process, the SSKHOA algorithm combines the shark search algorithm and the krill herd algorithm. It quickly explores the solution space and finds near-optimal work schedules by modelling the swarm intelligence of krill herds and the predator-prey behavior of sharks. In order to test the efficacy of the SSKHOA algorithm, we created a synthetic cloud fog environment and performed some tests. Traditional task scheduling techniques like LTRA, DRL, and DAPSO were used to evaluate the findings. The experimental results demonstrate that the SSKHOA outperformed the baseline algorithms in terms of task success rate increased 34%, reduced the execution time by 36%, and reduced makespan time by 54% respectively.
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Sample of Groups: A New Strategy to Find a Representative Point for Each Undisclosed Cluster
Статья научная
Some problems involving the selection of samples from undisclosed groups are relevant in various areas such as health, statistics, economics, and computer science. For instance, when selecting a sample from a population, well-known strategies include simple random and stratified random selection. Another related problem is selecting the initial points corresponding to samples for the K-means clustering algorithm. In this regard, many studies propose different strategies for choosing these samples. However, there is no consensus on the best or most effective ap-proaches, even when considering specific datasets or domains. In this work, we present a new strategy called the Sam-ple of Groups (SOG) Algorithm, which combines concepts from grid, density, and maximum distance clustering algo-rithms to identify representative points or samples located near the center of the cluster mass. To achieve this, we create boxes with the right size to partition the data and select the representatives of the most relevant boxes. Thus, the main goal of this work is to find quality samples or seeds of data that represent different clusters. To compare our approach with other algorithms, we not only utilize indirect measures related to K-means but also employ two direct measures that facili-tate a fairer comparison among these strategies. The results indicate that our proposal outperforms the most common-ly used algorithms.
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ScrumFall: a hybrid software process model
Статья научная
Every software project is unique in its own way. As a consequence, a single software process model cannot be suitable for all types of projects. In the real world, practitioners face different difficulties with the existing process models during development. Still, they cope up with the challenges by tailoring the software development lifecycle according to their needs. Most of these custom-tailored practices are kept inside the walls of the organizations. However, sharing these proven and tested practices as well as acquired knowledge and experience would be highly beneficial for other practitioners as well as researchers. So in this paper, we have presented a software process model which contains the characteristics of both Scrum and Waterfall model and named it “ScrumFall”. This model has been practicing in an Anonymous Software Development Company, Bangladesh to solve the shortcomings of Scrum and Waterfall models. Moreover, we have analyzed the performance and suitability for applying this process model. The result shows that this process model is highly effective for the certain projects.
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Scrutable Mobile Client-side Personalization
Статья научная
Personalization has become an essential feature of mobile services of different domains. On the other hand, users have conflicting needs of personalized experience and privacy. This leads to the question of how to maximize the user’s experience of personalized mobile services while keeping privacy. One possible solution is to provide user’s control of their personal data by keeping their user model on their personal mobile devices. In this way, a user can scrutinize the data while sharing with service providers depending on her/his requirements. The client-side personalization approach can shift the control of privacy to the users and can involve them in personalization process. Transparency and user control can increase the user’s trust. In this paper, we have proposed a solution with the objective of scrutable client-side personalization while keeping the user in control of both privacy and personalization process.
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Secluding Efficient Geographic Multicast Protocol against Multicast Attacks
Статья научная
A Mobile Ad-hoc Network (MANETs) is composed of Mobile Nodes without any infrastructure. The network nodes in MANETs, not only act as ordinary network nodes but also as the routers for other peer devices. The dynamic topology, lack of a fixed infrastructure and the wireless nature make MANETs susceptible to the security attacks. To add to that, due to the inherent, severe constraints in power, storage and computational resources in the MANET nodes, incorporating sound defense mechanisms against such attacks is also non-trivial. Therefore, interest in research of Mobile Ad-hoc NETworks has been growing since last few years. Security is a big issue in MANETs as they are infrastructure-less and autonomous. The main objective of this paper is to address some basic security concerns in EGMP protocol which is a multicast protocol found to be more vulnerable towards attacks like blackhole, wormhole and flooding attacks. The proposed technique uses the concepts of certificate to prevent these attacks and to find the malicious node. These attacks are simulated using NS2.28 version and the proposed proactive technique is implemented. The following metrics like packet delivery ratio, control overhead, total overhead and End to End delay are used to prove that the proposed solution is secure and robust.
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Secure Hajj Permission Based on Identifiable Pilgrim’s Information
Статья научная
Event management of large international events is attracting interest from researchers, not least due to the potential use of technology to provide support throughout the different stages of the event. Some events, such as major sports or religious events, can involve millions of people from different countries, and require active management to control access (e.g. many popular events can be oversubscribed) and to reduce risks for the participants, local communities and environment. This paper explores the context of a large event - the Hajj pilgrims in Saudi Arabia - which involves up to three million pilgrims, many of whom are international. The paper presents a novel identification system - the Identification Wristband Hajj Permission (IWHP) - which uses encryption technologies and biometric attributes to identify pilgrims, whilst remaining sensitive to the context of the Hajj. The suggested solution has many attributes of relevance that could support its use in other large-crowd events.
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Secured and Optimized AODV for Wireless Sensor Networks
Статья научная
Similar to conventional wireless networks, WSNs are based on multi hop routing to ensure connectivity and data forwarding which makes the routing service a challenging task due to the nature of sensors usually limited in memory, battery and computing capacities as well as the nature of the environment which is hostile and unpredictable making the routing protocols developed for conventional wireless network useless for WSNs without modifications and adaptations for the new context of WSNs. Thus in this paper we present an optimized version of AODV protocol for WSNs which takes into consideration the traffic pattern of WSNs and sensors’ constraints. In the proposed protocol we affect the task of route discovery to the base station which periodically informs sensors about its location instead of letting this task to sensors which consumes the network resources due to the broadcasting nature of the route discovery. We have also proposed a key distribution scheme destined to establish a symmetric encrypting key between each sensor and the base station, the proposed key management scheme uses the underlying routing requests to execute handshakes and key update which have greatly saved the network resources and ensured a good threshold of security.
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Secured biometric identification: hybrid fusion of fingerprint and finger veins
Статья научная
The goal of this work is the improvement of the performance of a multimodal biometric identification system based on fingerprints and finger vein recognition. This system has to authenticate the person identity using features extracted from his fingerprints and finger veins by multimodal fusion. It is already proved that multimodal fusion improves the performance of biometric recognition, basically the fusion at feature level and score level. However, both of them showed some limits and in order to enhance the overall performance, a new fusion method has been proposed in this work; it consists on using both features and scores fusion at the same time. The main contribution of investigation this technique of fusion is the reduction of the template size after fusion without influencing the overall performance of recognition. Experiments were performed on a real multimodal database SDUMLA-HMT and obtained results showed that as expected multimodal fusion strategies achieved the best performances versus uni-modal ones, and the fusion at feature level was better than fusion at score level in recognition rate (100%, 95.54% respectively) but using more amounts of data for identification. The proposed hybrid fusion strategy has overcome this limit and clearly preserved the best performance (100% as recognition rate) and in the same time it has reduced the proportion of essential data necessary for identification.
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Статья научная
The rise of the Internet accelerates the creation of various large-scale online social networks, which can be described the relationships and activities between human beings. The online social networks relationships in real world are too big to present with useful information to identify the criminal or cyber-attacks. This research proposed new information security analytic model for online social networks, which called Security Visualization Analytics (SVA) Model. SVA Model used the set of algorithms (1) Graph-based Structure algorithm to analyze the key factors of influencing nodes about density, centrality and the cohesive subgroup to identify the influencing nodes of anomaly and attack patterns (2) Supervised Learning with oneR classification algorithm was used to predict new links from such influencing nodes in online social networks on discovering surprising links in the existing ones of influencing nodes, which nodes in online social networks will be linked next from the attacked influencing nodes to monitor the risk. The results showed 42 influencing nodes of anomaly and attack patterns and can be predict 31 new links from such nodes were achieved by SVA Model with the accuracy of confidence level 95.0%. The new proposed model and results illustrated SVA Model was significance analysis. Such understanding can lead to efficient implementation of tools to links prediction in online social networks. They could be applied as a guide to further investigate of social networks behavior to improve the security model and notify the risk, computer viruses or cyber-attacks for online social networks in advance.
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