Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1165
Статья научная
In recent days, the smart city project has been emerging concept all over the world. In this process, the proper communication between the sensors and the smart devices, and identification of optimal path between sensors and mutation sensors in large geographical area is very difficult. The main objective has been considered to overcome the drawbacks as mentioned above. The proposed algorithm is efficient to provide integrated communication of IoT-based ubiquitous networking (UBN) devices to improve in large geographically distributed area. The data storage capacity and accuracy of sensors and smart devices are enhanced using the proposed algorithm. The communication latency and data pre-processing of IoT-based UBN nodes deployed in smart cities are reduced. The proposed algorithm also analyses the performance of IoT-based UBN nodes by considering geographical testbeds that represent a smart city scenario. The analysis and comparison are carried out by considering the heuristic parameters. The proposed algorithm will also optimize the communication latency and data pre-processing time by analyzing various sensitivity levels by considering the heuristic parameters in different probability of nodes in smart cities. The proposed IoT-based UBN computing devices improve the objective function due to proper integrated communication between the sensors using a machine learning based regression algorithm. The proposed algorithm also identifies the probability sensitivity of optimal path between smart devices in a smart city thereby enhancing the connectivity of mutated sensor nodes. The proposed algorithm also enhances the probability of smart device connectivity to improve the accuracy, flexibility and large geographical coverage using machine learning based regression algorithm.
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An Efficient String Matching Technique for Desktop Search to Detect Duplicate Files
Статья научная
Information retrieval is used to identify the relevant documents in a document collection, which is matching a user's query. It also refers to the automatic retrieval of documents from the large document corpus. The most important application of information retrieval system is search engine like Google, which identify those documents on the World Wide Web that are relevant to user queries. In most situations, users may download the files that are already downloaded and stored in their computer. Then, there is a chance of multiple copies of the files that are already stored in different drives and folders on the system, which in turn reduces the performance of the system and these files occupy a lot of memory space. Analyzing the contents of the file and finding their similarity is one of the major problems in text mining and information retrieval. The main objective of this research work is to analyze the file contents and deletes the duplicate files in the system. In order to perform this task, this research work proposes a new tool named Duplicate File Detector Tool i.e. DFDT. DFDT helps the user to search and delete duplicate files in the system at a minimum time. It also helps to delete the duplicate files not only with the same file category, but also with different file categories. Boyer Moore Horspool and Knuth Morris Pratt string searching algorithms are existing algorithms and these algorithms are used to compare the file contents for finding their similarity. This work also proposes a new string matching algorithm named as W2COM (Word to Word COMparison). From the experimental results it is observed that the newly proposed W2COM string matching algorithm performance is better than Boyer Moore Horspool and Knuth Morris Pratt algorithms.
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An Empirical Study on Testing of SOA based Services
Статья научная
Service-Oriented Architecture (SOA) removed the gap between software and business. Today, there is a business transformation among enterprises and they adopt a service based information technology (IT) model. So, testing is necessary for SOA based applications. This paper investigates different type of approaches and techniques that address the testing problems of SOA based services. Here we also investigate the differences between SOA and web services and traditional testing and SOA testing. Various testing levels are also discussed in detail. This paper also expresses various testing perspectives, challenges of SOA testing and review the many testing approaches and identify the problems that improve the testability of SOA based services.
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An Energy Effective WBAN Architecture for Athletes Efforts Analysis under a Common Activity
Статья научная
Wireless Body Area Network (WBAN) is one of the most critical real time networks. In order to increase network lifetime, several protocols have been proposed in the Literature. Majority of these protocols are based on either coverage or residual energy of the network. However, none of the protocols is based on node criticality, communication count, coverage and residual energy. In this paper, we have taken into consideration all the above stated parameters to determine the optimal path for transmission of message. In particular, we are implementing this scenario for Athlete Activity Monitoring to identify the athlete with maximum physical stamina. As per best of our knowledge, only intra-BAN routing protocols are proposed till date. However, in this paper we have simulated Athlete Activity Monitoring scenario for intra-WBAN and inter-WBAN architecture. It is clear from the simulation results that our scheme performs much better in terms of energy consumption than earlier routing schemes.
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Статья научная
Consolidation of storage into IP SANs (Internet protocol storage area network) has led to a combination of multiple workloads of varying demands and importance. To ensure that users get their Service level objective (SLO) a technique for isolating workloads is required. Solutions that exist include cache partitioning and throttling of workloads. However, all these techniques require workloads to be classified in order to be isolated. Previous works on performance isolation overlooked the classification process as a source of overhead in implementing performance isolation. However, it’s known that linear search based classifiers search linearly for rules that match packets in order to classify flows which results in delays among other problems especially when rules are many. This paper looks at the various limitation of list based classifiers. In addition, the paper proposes a technique that includes rule sorting, rule partitioning and building a tree rule firewall to reduce the cost of matching packets to rules during classification. Experiments were used to evaluate the proposed solution against the existing solutions and proved that the linear search based classification process could result in performance degradation if not optimized. The results of the experiments showed that the proposed solution when implemented would considerably reduce the time required for matching packets to their classes during classification as evident in the throughput and latency experienced.
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An Experimental Analysis on Performance and Energy Saving in Mobile Cloud Computing
Статья научная
Mobile Cloud Computing is a combination of mobile, cloud and wireless network where data storage and processing happens outside the mobile device. The storage capacity, processing power and battery life can be improved by moving resource intensive applications onto the cloud. In this paper, the performance of the mobile device is measured by using four different sorting techniques. Two different strategies were used for execution. In the first strategy, the input, execution and the output happens on the mobile device. In the other strategy, the input and output happens on the mobile device while the execution of the sorting techniques is offloaded to the server. The parameters considered for measurement are the execution time and mobile device’s energy consumption. The results show that offloading the task to the server reduces the execution time and energy consumption on the mobile device.
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An Exploratory Analysis between the Feature Selection Algorithms IGMBD and IGChiMerge
Статья научная
Most of the data mining and machine learning algorithms will work better with discrete data rather than continuous. But the real time data need not be always discrete and thus it is necessary to discretize the continuous features. There are several discretization methods available in the literature. This paper compares the two methods Median Based Discretization and ChiMerge discretization. The discretized values obtained using both methods are used to find the feature relevance using Information Gain. Using the feature relevance, the original features are ranked by both methods and the top ranked attributes are selected as the more relevant ones. The selected attributes are then fed into the Naive Bayesian Classifier to determine the predictive accuracy. The experimental results clearly show that the performance of the Naive Bayesian Classifier has improved significantly for the features selected using Information Gain with Median Based Discretization than Information Gain with ChiMerge discretization.
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Статья научная
A mathematical model and a solving strategy for the Pickup and Delivery Problem with three-dimensional loading constraints regarding a combinatorial configuration instead of a traditional approach that utilizes Boolean variables is proposed. A traditional one-to-one Pickup and Delivery Problem in a combination with a problem of packing transported items into vehicles by means of the proposed combinatorial generation algorithm is solved.
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An ICT based solution for virtual garment fitting for online market place
Статья научная
In this paper, we describe various technologies that are being used in virtual garment fitting and simulation. There, we have focused on the usage of anthropometry in the clothing industry and avatar generation of virtual garment fitting. Most commonly used technologies for avatar generation in virtual environment have been discussed in this paper such as generic body model method and laser scanning technologies. Moreover, this paper includes the usage of real-time tracking technologies used in virtual garment fitting like markers and depth cameras. Apart from these, virtual clothing methods such as geometrical, physical and hybrid-based models were also discussed in this paper. As ease allowance has a major impact on virtual cloth fitting, it is also considered in this paper relating to similar research studies. As the final stage, our proposed design has been explained including the steps of the experiment that has been conducted to generate a two-dimensional model of the garment item. Within this paper, all the above-mentioned areas were described thoroughly while stating the existing gap of the virtual garment fitting in online marketplaces and our proposed solution to bridge that gap.
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An Image Encryption Scheme Based on Bit Circular Shift and Bi-directional Diffusion
Статья научная
A novel image encryption scheme based on chaotic system is proposed. The proposed encryption scheme utilizes one tent map to generate a pseudo-random sequence and then shift the bits of the expanding 0-1 image circularly so as to shuffle the image gray values. To make the encryption scheme resist differential attack efficiently, generalized Arnold maps and Bernoulli shift maps are applied to produce two pseudo-random gray value sequences and then diffuse the gray values bi-directionally. The bit circular shift process and diffusion processes greatly confuse the statistical nature between plain-images and cipher-images. Security analyses including key sensitivity analysis, key space analysis, statistical analysis, differential attack analysis and information entropy analysis are performed. All the experimental results demonstrate that the proposed image encryption scheme possesses large key space to frustrate brute-force attack efficiently and can resist statistical attack, differential attack, etc.
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An Improved Active Shape Model Application onFacial Feature Localization
Статья научная
An improved active shape model is proposed in this paper. The proposed algorithm includes the following four aspects. Firstly, this paper adopts a semi-automatic facial feature points marking tool. Secondly, this paper proposes to extract 2D gradient feature on the highest level and the higher level of multi-resolution pyramid images, and use Gabor wavelet transformation to extract the lowest level’s 2D texture feature. Thirdly, this paper adopts a new method of decomposition of multi-resolution pyramid. Pyramid images are got by wavelet transformation. Finally, this paper uses an improved searching scheme of multiresolution pyramid. The length of 2D profile searching rectangle is changed according to different pyramid levels. Experimental results demonstrate that the proposed algorithm exhibits better performance than the original ASM.
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An Improved Classification Model for Fake News Detection in Social Media
Статья научная
Fake news dissemination is a critical issue in today’s fast-changing network environment. Existing classification models for fake news detection have not completely stopped the spread because of their inability to accurately classify news, thus leading to a high false alarm rate. This study proposed a model that can accurately identify and classify deceptive news articles content infused on social media by malicious users. The news content, social-context features and the respective classification of reported news was extracted from the PHEME dataset using entropy-based feature selection. The selected features were normalized using Min-Max Normalization techniques. A predictive fake news detection model was formulated as a stacked ensemble of three algorithms. The model was simulated and its performance was evaluated by benchmarking with an existing model using detection accuracy, sensitivity, and precision as metrics. The result of the evaluation showed a higher 17.25% detection accuracy, 15.78% sensitivity, but lesser 0.2% precision than the existing model. Thus, the proposed model detects more fake news instances accurately based on news content and social content perspectives. This indicates that the proposed classification model has a better detection rate, reduces the false alarm rate of news instances and thus detects fake news more accurately.
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An Improved Kerberos Scheme Based on Dynamic Password
Статья научная
By studying the Kerberos authentication scheme, an improved authentication scheme is raised, which is based on Dynamic Password Method. In the improved scheme, user’s password can be effectively protected, and the authentication is double between users and severs. Also, the scheme can resist jacking connection attack. The improved scheme is more secure and more practical than the original one.
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An Improved Wavelet Filtering Algorithm and Its FPGA Implementation
Статья научная
The de-noising of sensor data has become an important to research. Since the traditional de-noising method can’t achieve successful de-noising effect and the software-only method never meets a high real time capability. In this paper, we illustrate a novel threshold function based on the wavelet hard and soft threshold function. It is unlike ordinary function, which has overcome the defect such as the discontinuity of hard threshold function and an invariable dispersion between the estimated wavelet coefficients and the decomposed coefficients of soft threshold function. Moreover, we consider the hardware implementation of wavelet threshold filter on FPGA which adopt the pleated sheet structure of multiplier and fit to frame data. A detailed description of the simulation and implementation is given. Finally, the experiment result on-board is shown that our hardware implementation can meet the requirement of real-time signal processing.
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An Improvement over AODV Routing Protocol by Limiting Visited Hop Count
Статья научная
The AODV protocol is based on the minimum delay path as its route selection criteria, regardless of the paths load. This issue leads to unbalanced load dissemination in the network and the energy of the nodes on the shortest path deplete earlier than others. We proposed an improved AODV protocol with limited TTL (Time to Live) of RREP packet in which the route reply (RREP) packet of AODV is modified to limite TTL information of nodes. Experiments have been carried out using network simulator software (NS2). Simulation results show that our proposed routing protocol outperforms regular AODV in terms of packet delivery rate, good put, throughput, and jitter.
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Статья научная
The advancements of Information Technology have led to many developments that make life easier and faster with high reachability and efficiency. There is a drastic improvement in the area of distributed applications with the advent of mobile agent technology over the usual client/server framework. Applying mobile agent technology in the area of distributed applications improves the performance and quality of service. Mobile agent technologies in particular have taken a prominent place in handling effective road and vehicle traffic (VANET). This paper highlights on the integration of previously defined MATLB, PCM and MSA Agent and proposes an approach for reducing the size of the mobile agent that helps in data collection of vehicles in the VANET for effective and efficient traffic control. This paper is an advancement of agent load shedding algorithm and an attempt to optimize the size reduction process. This reduction in size of mobile agents will enhance the performance of VANET making the agents more acceptable by the hosts and correspondingly building an effective co-operative vehicular network.
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An Integrated Approach to Drive Ontological Structure from Folksonomie
Статья научная
Web 2.0 is an evolution toward a more social, interactive and collaborative web, where user is at the center of service in terms of publications and reactions. This transforms the user from his old status as a consumer to a new one as a producer. Folksonomies are one of the technologies of Web 2.0 that permit users to annotate resources on the Web. This is done by allowing users to use any keyword or tag that they find relevant. Although folksonomies require a context-independent and inter-subjective definition of meaning, many researchers have proven the existence of an implicit semantics in these unstructured data. In this paper, we propose an improvement of our previous approach to extract ontological structures from folksonomies. The major contributions of this paper are a Normalized Co-occurrences in Distinct Users (NCDU) similarity measure, and a new algorithm to define context of tags and detect ambiguous ones. We compared our similarity measure to a widely used method for identifying similar tags based on the cosine measure. We also compared the new algorithm with the Fuzzy Clustering Algorithm (FCM) used in our original approach. The evaluation shows promising results and emphasizes the advantage of our approach.
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An Integrated CEA Approach for Color Light Source Estimation
Статья научная
Color constancy is an element of human vision framework which guarantees that the apparent color of items under fluctuating light conditions generally remains constant. It is fundamentally used to eliminate the color cast in the picture. Color Cat is a quick and precise learning-based methodology for accomplishing computational color constancy. However, despite everything it confronts a few limitations like poor brightness due to normalization used. Furthermore it doesn't promise edge preservation. So to overcome these issues a CEA strategy has been proposed which is a hybrid model based on Color Cat, Edge preservation filter and Adaptive histogram Equalization. As Adaptive histogram Equalization is exceptionally valuable for contrast improvement and edges are protected by edge preservation filter. Experimental results show that the proposed CEA approach outperforms over existing techniques.
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An Integrated Knowledge Base System Architecture for Histopathological Diagnosis of Breast Diseases
Статья научная
The histopathological diagnosis of breast diseases requires highly trained and experienced experts, and often strains pathologists’ cognitive capabilities. Accurate and timely diagnosis of breast diseases is essential for the appropriate management of the patients. The paper presents a knowledge base system that uses a combination of rule-based and case-based techniques to achieve the diagnosis. Rule-based systems handle problems with well-defined knowledge bases this limits the flexibility of such system. Case-based reasoning has been adopted to overcome this inherent weakness of rule-based systems by incorporating previous cases in the generation of new cases to improve the performance of the system. The result of this research shows that the system is capable of assisting pathologists in making accurate, consistent and timely diagnoses. The system also aid in eliminating errors of omission that have been viewed as a prominent cause of medical errors. In conclusion this paper investigated the histological features used in the diagnosis of breast diseases and proposed an integrated knowledge base system based on the features.
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An Integrated Knowledge Management Capabilities Framework for Assessing Organizational Performance
Статья научная
In the present aggressive world of competition, knowledge management strategies are becoming the major vehicle for the organizations to achieve their goals; to compete and to perform well. Linking knowledge management to business performance could make a strong business case in convincing senior management of any organization about the need to adopt a knowledge management strategy. Organizational performance is, therefore, a key issue and performance measurement models provide a basis for developing a structured approach to knowledge management. In this respect, organizations need to assess their knowledge management capabilities and find ways to improve their performance. This paper takes these issues into account when study the role of knowledge management in enhancing the organizational performance and consequently, developed an integrated knowledge management capabilities framework for assessing organizational performance. The results show that there is positive correlation between knowledge management capabilities and organizational performance. The results also show that the proposed framework can be used to assess organizational performance and also can be used as decision tool to decide which knowledge management capability should be improved.
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