Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1165
An Analysis of Key Factors to Mobile Health Adoption using Fuzzy AHP
Статья научная
In the present era, ICT has brought significant facilities for the growth and innovation of organizations. Thus, with the advent of information technology in the field of healthcare, significant advances have been made in terms of the high level of care in preventing a variety of diseases and treatments as well. Mobile health, which is a part of smart health concept, helps people, at any time and place, use smart devices such as smartphones, smart watches, and the like to monitor their health status like pulse, blood pressure and so on. Therefore, this article aims to examine the effective factors on the adoption of mobile health technology. According to the field of research and the number of people considered, this study examined some of the factors affecting the adoption of mobile health technology among 19 expert experts who have mainly researched in this field. This research uses the Fuzzy AHP method. The main factors for admitting mobile health technology were divided into five main categories, including system quality, information quality, individual factors, service quality, and organizational quality. The results indicated that system quality, quality of information and individual factors have more impact on the acceptance of mobile health technology than service quality and organizational factors. In addition, according to the results obtained in this study, mobile health can be used as the most reliable and safest tools to control and monitor diseases. Ultimately, experts emphasized the need to use mobile health technology continuously.
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An Analytic Approach for Calculating Frame Erasue Rate in Cellular GSM Networks
Статья научная
The Quality of Service (QoS) of a GSM system is quantified in terms of Bit Error Rate (BER) and Frame Erasure Rate (FER) observed by the user. The problem of obtaining analytical expressions for BER and FER in a fading channel with multiple cochannel interferers (CCI) is an extremely complex mathematical problem. The reason for this complexity is that the involvement of several GSM physical layer modules is required to obtain an expression for the probability of bit error. Besides, one needs to obtain the statistical properties of faded cochannel interferers in order to obtain the raw BER of GMSK modulation. Thus, error rate metrics are usually obtained by simulating the GSM physical layer rather than treating the problem analytically. A reliable interface between system and link level models can be obtained by evaluating the BER and FER in terms of the Signal-to-Interference Ratio (SIR) analytically, instead of the pre-defined statistical mapping data usually used in literature. In this work, bounds on the uplink BER and FER are obtained for the GSM physical layer assuming a CCI limited system where both the desired and interference signals are subjected to Rayleigh fading. The analysis considers GMSK modulation, convolutional coding and Frequency Hopping.
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An Analytical Review of Stereovision Techniques to Reconstruct 3D Coordinates
Статья научная
Stereovision based on 3D environment reconstruction provides a true picture of real world situations for detection of objects’ locations. This approach has specific use in the scenarios like identifying traffic jams on the roads, locating curves and bends on the roads, finding obstacles in the construction sites, etc. This paper describes different methods used in stereovision to detect images like use of trinocular stereovision, calculating correlation between left and right contours for achieving accuracy, use of prior information with intrinsic and extrinsic parameters, detection of side lane and 3D points of guardrails and fences, use of dense stereovision information, especially in urban environment. The paper also discusses Forward Collision Detection method that uses Elevation Map with Dense Stereovision, tracking of multiple objects using two-level approach and building an enhanced grid that involves obstacle cells. Hybrid dense stereo engine, which is used in urban detection scenarios is also discussed in the paper along with a solution of lane estimation in different situations using particle filtering method. Pattern matching using 3D image for pedestrian detection and lane estimation based on the particle filtering with greyscale images are also explored. The use of the rectangular digital elevation map for transforming stereo based information and the methodology used to enhance the sub pixel accuracy are also part of the paper.
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An Analytical Study of Power Line Effect on UTP Cable using Lumped Circuit Components
Статья научная
The paper defines the term electrical noise with its types. Electromagnetic Interference (EMI), which is one type of electrical noise, is also defined and general techniques used for controlling EMI are described. Networking cables are affected by the EMI effect caused by a nearby power cable and data transmission through Unshielded Twisted Pair (UTP) cable, which is the mostly effected cable by EMI, may be degraded for it. Today, UTP cable is the most popular networking cable supporting 10G Ethernet. The most common effective methods for reduction of EMI effect on UTP cable, physical separation and use of shielding are described. EMI is caused by coupling mechanisms between source of interference and receptor. The two types of couplings are capacitive coupling and inductive coupling. The paper analyses and models the two couplings using lumped circuit components and electric circuit analysis considering power cable as the source of interference and networking cable as the receptor circuit of EMI.
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An Approach for Indexing Web Data Sources
Статья научная
Web information sources such as forums, blogs, and news articles are becoming increasingly large and diverse. Even if advances in technology are helping to improve techniques for dealing with the large amounts of the generated data, such data sources are heterogeneous in structure (semi structured or unstructured sources) and nature (texts or images). Implementation of software solutions is then necessary to prepare data and access these sources in a homogenous way. In this paper we present an approach for indexing heterogeneous data sources. Our objective is to offer techniques for efficient indexing of web sources by storing only the necessary information. We propose automatic indexing for semi structured or unstructured sources (e.g., xml files, html files) and annotation for other sources (e.g., images, videos that exist within a page). We present our algorithms of indexing and propose the use of MapReduce model to build a scalable inverted index. Experiments on a real-world corpus show that our approach achieves a good performance.
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An Approach for Test Case Prioritization Based on Three Factors
Статья научная
The main aim of regression testing is to test the modified software during maintenance level. It is an expensive activity, and it assures that modifications performed in software are correct. An easiest strategy to regression testing is to re-test all test cases in a test suite, but due to limitation of resource and time, it is inefficient to implement. Therefore, it is necessary to discover the techniques with the goal of increasing the regression testing’s effectiveness, by arranging test cases of test suites according to some objective criteria. Test case prioritization intends to arrange test cases in such a manner that higher priority test cases execute earlier than test cases of lower priority according to some performance criteria. This paper presents an approach to prioritize regression test cases based on three factors which are rate of fault detection [6], percentage of fault detected and risk detection ability. The proposed approach is compared with different prioritization techniques such as no prioritization, reverse prioritization, random prioritization, and also with previous work of kavitha et al [6], using APFD (average percentage of fault detected) metric. The results represent that proposed approach outperformed all approaches mentioned above.
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An Approach of Degree Constraint MST Algorithm
Статья научная
This paper is approaching a new technique of creating Minimal Spanning Trees based on degree constraints of a simple symmetric and connected graph G. Here we recommend a new algorithm based on the average degree sequence factor of the nodes in the graph. The time complexity of the problem is less than O(N log|E|) compared to the other existing time complexity algorithms is O(|E| log|E|)+C of Kruskal, which is optimum. The goal is to design an algorithm that is simple, graceful, resourceful, easy to understand, and applicable in various fields starting from constraint based network design, mobile computing to other field of science and engineering.
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An Architecture for Recommendation of Courses in E-learning
Статья научная
Over the last few years, the face of traditional learning has changed significantly, primarily due to the emergence of the worldwide web. So in order to take advantage of the web various kinds of learning systems have emerged such as computer-based learning, web-based learning and other forms of electronic learning which have been very successful in meeting different kinds of the educational need of the learners and educators thus they are adopted by a large number of universities and institutions worldwide. E-learning systems let educators distribute information, create content material, prepare assignments, engage in discussions, and manage distance classes among others. They accumulate a huge amount of data as a result of learner's interaction with the site, which has the potential to provide useful knowledge to the students, teachers, e-learning system administrators and university management for decision making. However the tools that existed in the past for data mining were inadequate to provide useful insight into the huge data generated consequently, data mining techniques began to facilitate the process of knowledge discovery. In this paper, we propose an architecture for the recommendation of the best combination of courses to the students and apply the apriori algorithm at the preliminary stage.
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An Assessment of Software Testability using Fuzzy Logic Technique for Aspect-Oriented Software
Статья научная
Testability is a property of software which introduces with the purpose of forecasting efforts need to test the programs. Software quality is the most important factor in the development of software, which can be depend on many quality attributes. The absence of testability is responsible for higher maintenance and testing effort. In this paper Fuzzy Logic is used to ascertain the relationship between the factors that affects the software testability. This paper presents the application of fuzzy logic the assessment of software testability. A new model is proposed using fuzzy inference system for tuning the performance of software testability. Aspect-oriented metrics are taken i.e. Separation of Concern (SoC), cohesion, size and coupling. These metrics are closely related to the factors i.e. Controllability, Observability, Built in Test Capability, Understandability and Complexity. These factors are independent to each other and used for accessing software testability. A Triangular Membership Function (TriMF) is applied on these factors which defined in Mamdani Fuzzy Inference System in MATLAB. In this paper, we have defined and evaluated factors combination which is used for the assessment of software testability for as well as aspect oriented software.
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An E-Services Success Measurement Framework
Статья научная
The introduction of e-service solutions within the public sector has primarily been concerned with moving away from traditional information monopolies and hierarchies. E-service aims at increasing the convenience and accessibility of government services and information to citizens. Providing services to the public through the Web may lead to faster and more convenient access to government services with fewer errors. It also means that governmental units may realize increased efficiencies, cost reductions, and potentially better customer service. The main objectives of this work are to study and identify the success criteria of e-service delivery and to propose a comprehensive, multidimensional framework of e-services success. To examine the validity of the proposed framework, a sample of 200 e-service users were asked to assess their perspectives towards e-service delivery in some Egyptian organizations. The results showed that the proposed framework is applicable and implementable in the e-services evaluation; it also shows that the proposed framework may assist decision makers and e-service system designers to consider different criteria and measures before committing to a particular choice of e-service or to evaluate any existing e-service system.
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Статья научная
Terrorist groups in the Arab world are using social networking sites like Twitter and Facebook to rapidly spread terror for the past few years. Detection and suspension of such accounts is a way to control the menace to some extent. This research is aimed at building an effective text classifier, using machine learning to identify the polarity of the tweets automatically. Five classifiers were chosen, which are AdB_SAMME, AdB_SAMME.R, Linear SVM, NB, and LR. These classifiers were applied on three features namely S1 (one word, unigram), S2 (word pair, bigram), and S3 (word triplet, trigram). All five classifiers evaluated samples S1, S2, and S3 in 346 preprocessed tweets. Feature extraction process utilized one of the most widely applied weighing schemes tf-idf (term frequency-inverse document frequency).The results were validated by four experts in Arabic language (three teachers and an educational supervisor in Saudi Arabia) through a questionnaire. The study found that the Linear SVM classifier yielded the best results of 99.7 % classification accuracy on S3 among all the other classifiers used. When both classification accuracy and time were considered, the NB classifier demonstrated the performance on S1 with 99.4% accuracy, which was comparable with Linear SVM. The Arab world has faced massive terrorist attacks in the past, and therefore, the research is highly significant and relevant due to its specific focus on detecting terrorism messages in Arabic. The state-of-the-art methods developed so far for tweets classification are mostly focused on analyzing English text, and hence, there was a dire need for devising machine learning algorithms for detecting Arabic terrorism messages. The innovative aspect of the model presented in the current study is that the five best classifiers were selected and applied on three language models S1, S2, and S3. The comparative analysis based on classification accuracy and time constraints proposed the best classifiers for sentiment analysis in the Arabic language.
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An Efficient Algorithm for Density Based Subspace Clustering with Dynamic Parameter Setting
Статья научная
Density based Subspace Clustering algorithms have gained their importance owing to their ability to identify arbitrary shaped subspace clusters. Density-connected SUBspace CLUstering(SUBCLU) uses two input parameters namely epsilon and minpts whose values are same in all subspaces which leads to a significant loss to cluster quality. There are two important issues to be handled. Firstly, cluster densities vary in subspaces which refers to the phenomenon of density divergence. Secondly, the density of clusters within a subspace may vary due to the data characteristics which refers to the phenomenon of multi-density behavior. To handle these two issues of density divergence and multi-density behavior, the authors propose an efficient algorithm for generating subspace clusters by appropriately fixing the input parameter epsilon. The version1 of the proposed algorithm computes epsilon dynamically for each subspace based on the maximum spread of the data. To handle data that exhibits multi-density behavior, the algorithm is further refined and presented in version2. The initial value of epsilon is set to half of the value resulted in the version1 for a subspace and a small step value 'delta' is used for finalizing the epsilon separately for each cluster through step-wise refinement to form multiple higher dimensional subspace clusters. The proposed algorithm is implemented and tested on various bench-mark and synthetic datasets. It outperforms SUBCLU in terms of cluster quality and execution time.
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An Efficient Approach to Genetic Algorithm for Task Scheduling in Cloud Computing Environment
Статья научная
Cloud computing is recently a booming area and has been emerging as a commercial reality in the information technology domain. Cloud computing represents supplement, consumption and delivery model for IT services that are based on internet on pay as per usage basis. The scheduling of the cloud services to the consumers by service providers influences the cost benefit of this computing paradigm. In such a scenario, Tasks should be scheduled efficiently such that the execution cost and time can be reduced. In this paper, we proposed a meta-heuristic based scheduling, which minimizes execution time and execution cost as well. An improved genetic algorithm is developed by merging two existing scheduling algorithms for scheduling tasks taking into consideration their computational complexity and computing capacity of processing elements. Experimental results show that, under the heavy loads, the proposed algorithm exhibits a good performance.
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An Efficient Diffusion Load Balancing Algorithm in Distributed System
Статья научная
In distributed computing system some nodes are very fast and some are slow and during the computation many fast nodes become idle or under loaded while the slow nodes become over loaded due to the uneven distribution of load in the system. In distributed system, the most common important factor is the information collection about loads on different nodes. The success of load balancing algorithm depends on how quickly the information about the load in the system is collected by a node willing to transfer or accept load. In this paper we have shown that the number of communication overheads depends on the number of overloaded nodes present in the domain of an under loaded nodes and vice-versa. We have also shown that communication overhead for load balancing is always fairly less than KN but in worst case our algorithm’s complexity becomes equal to KN.
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An Efficient Dimension Reduction Quantization Scheme for Speech Vocal Parameters
Статья научная
To achieve good reconstruction speech quality in a very low bit rate speech codecs, an efficient dimension reduction quantization scheme for the linear spectrum pair (LSP) parameters is proposed based on compressed sensing (CS). In the encoder, the LSP parameters extracted from consecutive speech frames are shaped into a high dimensional vector, and then the dimension of the vector is reduced by CS to produce a low dimensional measurement vector, the measurements are quantized using the split vector quantizer. In the decoder, according to the quantized measurements, the original LSP vector is reconstructed by the orthogonal matching pursuit method. Experimental results show that the scheme is more efficient than that of conventional matrix quantization scheme, the average spectral distortion reduction of up to 0.23dB is achieved in the DFT transform domain. Moreover, in the approximate KLT transform domain, this scheme can obtain transparent quality at 5 bits/frame with drastic bits reduction compared to other methods.
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An Efficient Distributed Power Control in Cognitive Radio Networks
Статья научная
In cognitive radio networks, when primary users (PUs) do not use a shared frequency band, secondary users (SUs) are allowed to use that frequency band. However, when SU using frequency band and reclaiming PU also wants to use this band, there are two solutions: SU jumps to another existing spectrum band or stays in the same frequency band and changes its power so that interference in the reclaiming PUs does not exceed a threshold. Since the first solution interrupts the SUs' work, the second solution is more appropriate. Therefore, power control in cognitive networks is absolutely significant which should receive more attention. In the previous work (TPC-CBS) [2], TPC algorithm has been used for power control. The drawback of this algorithm is fixed target SIRs. In other words, while using this algorithm, all the users having enough sources or extra sources reach the same target SIR, which is not desirable. Therefore, in this paper, we are going to solve it by means of the proposed algorithm called "An Efficient Distributed Power Control Algorithm for Cognitive Networks (EDPC)". By the proposed algorithm, when a user has a bad channel, its SIR sets to the minimum target SIR and, when it has a good channel, its SIR will be more than the minimum value. Moreover, by means of fuzzy logic systems (FLS), the value of interference on reclaiming PU caused by SUs is checked in each iteration and power level of SUs is decreased if this value exceeds a threshold. Simulation results show that using our proposed algorithm not only allows SUs and PUs use frequency band simultaneously, but also enhances throughput significantly in comparison with the previous approaches (TPC-CBS).
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An Efficient Framework for Creating Twitter Mart on a Hybrid Cloud
Статья научная
The contemporary era of technological quest is buzzing with two words - Big Data and Cloud Computing. Digital data is growing rapidly from Gigabytes (GBs), terabytes (TBs) to Petabytes (PBs), and thereby burgeoning data management challenges. Social networking sites like Twitter, Facebook, Google+ etc generate huge data chunks on daily basis. Among them, twitter masks as the largest source of publicly available mammoth data chunks intended for various objectives of research and development. In order to further research in this fast emerging area of managing Big Data, we propose a novel framework for doing analysis on Big Data and show its implementation by creating a ‘Twitter Mart’ which is a compilation of subject specific tweets that address some of the challenges for industries engaged in analyzing subject specific data. In this paper, we adduce algorithms and an holistic model that aids in effective stockpiling and retrieving data in an efficient manner.
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Статья научная
This paper focuses on an empirical hand gesture recognition system in the domain of image processing and machine learning. The hand gesture is probably the most intuitive and frequently used mode of nonverbal communication in human society. The paper analyzes the efficiency of the Histogram of Oriented Gradients (HOG) as the feature descriptor and Support Vector Machine (SVM) as the classification model in case of gesture recognition. There are three stages of the recognition procedure namely image binarization, feature extraction, and classification. The findings of the paper show that the model classifies hand gestures for the given dataset with satisfactory efficiency. The outcome of this work can be further utilized in practical fields of real-world applications dealing with non-verbal communication.
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An Efficient Graph-Coloring Algorithm for Processor Allocation
Статья научная
This paper develops an efficient exact graph-coloring algorithm based on Maximum Independent Set (MIS) for allocating processors in distributed systems. This technique represents the allocated processors in specific time in a fully connected graph and prevents each processor in multiprocessor system to be assigned to more than one process at a time. This research uses a sequential technique to distribute processes among processors. Moreover, the proposed method has been constructed by modifying the FMIS algorithm. The proposed algorithm has been programmed in Visual C++ and implemented on an Intel core i7. The experiments show that the proposed algorithm gets better performance in terms of CPU utilization, and minimum time for of graph coloring, comparing with the latest FMIS algorithm. The proposed algorithm can be developed to detect defected processor in the system.
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An Efficient IBE Scheme using IFP and DDLP
Статья научная
In 1984, Shamir introduced the concept of an identity-based encryption. In this system, each user needs to visit a private key generation (PKG) and identify him- self before joining a communication network. Once a user is accepted, the PKG will provide him with a secret key. In this way, if a user wants to communicate with others, he only needs to know the “identity” of his communication partner and the public key of the PKG. There is no public file required in this system. However, Shamir did not succeed in constructing an identity based encryption, but only in constructing an identity-based signature (IBS) scheme. In this paper, we propose an identity based encryption (IBE) based on the factorization problem (IFP) and double discrete logarithm problem (DDLP) and we consider the security against a conspiracy of some entities in the proposed system and show the possibility of establishing a more secure system.
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