Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1165
A System Call Randomization Based Method for Countering Code-Injection Attacks
Статья научная
Code-injection attacks pose serious threat to today’s Internet. The existing code-injection attack defense methods have some deficiencies on performance overhead and effectiveness. To this end, we propose a method that uses system called randomization to counter code injection attacks based on instruction set randomization idea. System calls must be used when an injected code would perform its actions. By creating randomized system calls of the target process, an attacker who does not know the key to the randomization algorithm will inject code that isn’t randomized like as the target process and is invalid for the corresponding de-randomized module. The injected code would fail to execute without calling system calls correctly. Moreover, with extended complier, our method creates source code randomization during its compiling and implements binary executable files randomization by feature matching. Our experiments on built prototype show that our method can effectively counter variety code injection attacks with low-overhead.
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A Systematic Literature Review of Studies Comparing Process Mining Tools
Статья научная
Process Mining (PM) and PM tool abilities play a significant role in meeting the needs of organizations in terms of getting benefits from their processes and event data, especially in this digital era. The success of PM initiatives in producing effective and efficient outputs and outcomes that organizations desire is largely dependent on the capabilities of the PM tools. This importance of the tools makes the selection of them for a specific context critical. In the selection process of appropriate tools, a comparison of them can lead organizations to an effective result. In order to meet this need and to give insight to both practitioners and researchers, in our study, we systematically reviewed the literature and elicited the papers that compare PM tools, yielding comprehensive results through a comparison of available PM tools. It specifically delivers tools’ comparison frequency, methods and criteria used to compare them, strengths and weaknesses of the compared tools for the selection of appropriate PM tools, and findings related to the identified papers' trends and demographics. Although some articles conduct a comparison for the PM tools, there is a lack of literature reviews on the studies that compare PM tools in the market. As far as we know, this paper presents the first example of a review in literature in this regard.
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A Systematic Literature Review on SMS Spam Detection Techniques
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Spam SMSes are unsolicited messages to users, which are disturbing and sometimes harmful. There are a lot of survey papers available on email spam detection techniques. But, SMS spam detection is comparatively a new area and systematic literature review on this area is insufficient. In this paper, we perform a systematic literature review on SMS spam detection techniques. For that purpose, we consider the available published research works from 2006 to 2016. We choose 17 papers for our study and reviewed their used techniques, approaches and algorithms, their advantages and disadvantages, evaluation measures, discussion on datasets and finally result comparison of the studies. Although, the SMS spam detection techniques are more challenging than email spam detection techniques because of the regional contents, use of abbreviated words, unfortunately none of the existing research addresses these challenges. There is a huge scope of future research in this area and this survey can act as a reference point for the future direction of research.
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A Systematic Review of Natural Language Processing in Healthcare
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The healthcare system is a knowledge driven industry which consists of vast and growing volumes of narrative information obtained from discharge summaries/reports, physicians case notes, pathologists as well as radiologists reports. This information is usually stored in unstructured and non-standardized formats in electronic healthcare systems which make it difficult for the systems to understand the information contents of the narrative information. Thus, the access to valuable and meaningful healthcare information for decision making is a challenge. Nevertheless, Natural Language Processing (NLP) techniques have been used to structure narrative information in healthcare. Thus, NLP techniques have the capability to capture unstructured healthcare information, analyze its grammatical structure, determine the meaning of the information and translate the information so that it can be easily understood by the electronic healthcare systems. Consequently, NLP techniques reduce cost as well as improve the quality of healthcare. It is therefore against this background that this paper reviews the NLP techniques used in healthcare, their applications as well as their limitations.
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A Taxonomy of Data Management Models in Distributed and Grid Environments
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The distributed environments vary largely in their architectures, from tightly coupled cluster environment to loosely coupled Grid environment and completely uncoupled peer-to-peer environment, and thus differ in their working environments as well as performance. To meet the specific needs of these environments for data organization, replication, transfer, scheduling etc. the data management systems implement different data management models. In this paper, major data management tasks in distributed environments are identified and a taxonomy of the data management models in these environments is presented. The taxonomy is used to highlight the specific data management requirements of each environment and highlight the strengths and weakness of the implemented data management models. The taxonomy is followed by a survey of different distributed and Grid environments and the data management models they implement. The taxonomy and the survey results are used to identify the issues and challenges of data management for future exploration.
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A Temporal Reasoning System for Diagnosis and Therapy Planning
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The research is based on the designing of Clinical Temporal Mediator for medical domain. The Clinical Temporal Mediator incorporates the concept of artificial intelligence for performing temporal reasoning tasks. The designing of reasoning system involves the implementation of various mathematical models of insulin-glucose metabolism. The reasoning system consists of three subsystems: Nuti-Diet subsystem, Insulin-Glucose subsystem and Therapy Planner and Diagnosis subsystem. The paper discusses about the designing of TPD subsystems. The temporal mediator perform diagnosis on patient's time oriented database and also suggest therapy planning for diabetes mellitus patient.
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Статья научная
Predicting human behavior based on the usage of text on social networking sites can be a challenging area of interest to a particular community. Text mining being a major interest in Data Mining has vast applications in various fields. Clients can assess an individual’s behavior using the proposed framework that is based on person’s textual interaction with other people. In this paper, a framework is proposed for predicting human behavior in three phases- Text Extraction, Text cleaning and Text Analysis. For cleaning text, all the stop words have been removed and then the text is utilized for further processing. Then, the terms from the text are clustered based on semantic similarity and then gets associated with respective physiological parameters that identify a human behavior. This application is best suited for the fields of Criminal Sciences, Medical Sciences, Human Resource Department and Political Science and even for Matrimonial purposes. The proposed framework is applied on some famous world known celebrities and the results are quite encouraging.
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A Tool for Diabetes Prediction and Monitoring Using Data Mining Technique
Статья научная
Data mining is the process of analyzing different aspects of data and aggregating it into useful information. Classification is a data mining task generally used in medical data mining. The goal here is to discover new and useful patterns to provide meaningful and useful information for the users about the diabetes. Here a diabetes prediction and monitoring system is designed and implemented using ID3 classification algorithm. The symptoms causing diabetes are identified and are applied to the prediction model based on which the prediction is done. The monitoring module analyzes the laboratory test reports of the blood sugar levels of the patient and provides proper awareness messages to the patient through mail and bar chart.
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A Trust Management System for the Nigerian Cyber-health Community
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Trust is a basic requirement for the acceptance and adoption of new services related to health care, and therefore, vital in ensuring that the integrity of shared patient information among multi-care providers is preserved and that no one has tampered with it. The cyber-health community in Nigeria is in its infant stage with health care systems and services being mostly fragmented, disjointed, and heterogeneous with strong local autonomy and distributed among several healthcare givers platforms. There is the need for a trust management structure for guaranteed privacy and confidentiality to mitigate vulnerabilities to privacy thefts. In this paper, we developed an efficient Trust Management System that hybridized Real-Time Integrity Check (RTIC) and Dynamic Trust Negotiation (DTN) premised on the Confidentiality, Integrity, and Availability (CIA) model of information security. This was achieved through the design and implementation of an indigenous and generic architectural framework and model for a secured Trust Management System with the use of the advanced encryption standard (AES-256) algorithm for securing health records during transmission. The developed system achieved Reliabity score, Accuracy and Availability of 0.97, 91.30% and 96.52% respectively.
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A Turkish Wikipedia Text Summarization System for Mobile Devices
Статья научная
Today Wikipedia provides a very large and reliable domain-independent encyclopedic repository. With this study a mobile system which summarizes Turkish Wikipedia text is presented. The presented system selects the sentences due to structural features of Turkish language and semantic features of the sentences. The performance evaluation is made based on judgments of human experts. The results are tested due to precision and recall values of a ranked sentence list and it is concluded that, the summarization results are promising.
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A Web-Based Skin Disease Diagnosis Using Convolutional Neural Networks
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Skin diseases are reported to be the most common disease in humans among all age groups and a significant root of infection in sub-Saharan Africa. The diagnosis of skin diseases using conventional approaches involves several tests. Due to this, the diagnosis process is seen to be intensely laborious, time-consuming and requires an extensive understanding of the domain. The enhancement of computer vision through artificial intelligence has led to a more straightforward and quicker way of detecting patterns in images, which can be harnessed to equip diagnosis process. Despite the breakthrough in technology, the dermatological process in Ghana is yet to be automated, making the diagnosis process complicated and time-consuming. Hence, this study sought to propose a web-based skin disease detection system named medilab-plus using a convolutional neural network classifier built upon the Tensorflow framework for detecting (atopic dermatitis, acne vulgaris, and scabies) skin diseases. Experimental results of the proposed system exhibited classification accuracy of 88% for atopic dermatitis, 85% for acne vulgaris, and 84.7% for scabies. Again, the computational time (0.0001 seconds) of the proposed system implies that any dermatologist, who decides to implement this study, can attend to not less than 1,440 patients a day compared to the manual diagnosis process. It is estimated that the proposed system will enhance accuracy and offer fasting diagnosis results than the traditional method, which makes this system a trustworthy and resourceful for dermatological disease detection. Additionally, the system can serve as a realtime learning platform for students studying dermatology in medical schools in Ghana.
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A Web-based Portal for Ornamental Plants and Flowers in Arusha City, Tanzania
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There is a wide collection of invaluable varie-ties of ornamental plants and flowers available for sale by vendors in Arusha city contributing to local employment, and food security. Horticulture in Tanzania is dominated by small scale farmers in Arusha that contribute to about 70% of the produces in the sector. However, there are challenges that need to be addressed including; inadequate information for the development of the sector and livelihoods of the vendors. The information on the varied species of ornamental plants and flowers are mainly undocumented and not digitized. This limits access to the scientific community and the general public bringing on these varieties in Arusha a growing conservation concern. The other challenge is that the small-scale vendors have limited visibility to regional markets and international market places which hinders their business growth. On this study, a Web portal was developed for inventory, mapping and digitization of the various species of ornamental plants and flowers as a solution to above challenges. Data collection was conducted using various data collection techniques such as; Interviews, observations, Questionnaire (Open Data Kit) and re-viewing numerous research papers in seven wards of Arusha city where the vendors grow and sell a number of species of ornamental plants and flowers. Both qualitative and quantitative methods mentioned above were deployed to provide insights on the ornamental plants and flower business operations. For the survey, 70 varieties of ornamental plants and flowers were gathered and arranged categorically in terms of taxonomy and usage and uploaded on the portal. The portal developed indicates the potential to help stakeholders find plants’ and flowers’ varieties infor-mation, images, and sales location online, the vendors will be able to advertise their products on the portal and conduct business with customers online. In addition, it will also help Arusha City Representatives with baseline information on the sector to make informed plans and decisions.
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Статья научная
It is a tremendous challenge to transmit real-time video streams over wireless sensor network because of the poor wireless communication conditions and the high requirements of video transmission. The opportunistic routing protocol can take advantage of the broadcast nature of wireless communication and can improve transfer throughput significantly. But the bigger size of transmission unit also increases the end-to-end delay at the same time. In order to overcome this problem and improve the real-time video transmission quality in wireless video sensor network, we propose a source adaptive frame discard algorithm for MAC-independent Opportunistic Routing & Encoding (MORE) Protocol in this paper. In our approach, the historical transmission delay is recorded to estimate current network transmission rate. Based on the video deadline, frames predicted to be delayed are discarded adaptively in the source node to get better overall video quality. In some practice application scenarios, there are usually need to deliver multiple video streams over multi-hop wireless network. It can’t work effectively with the originally MORE protocol in such scenarios. Furthermore, we modify the MORE protocol and design an adaptive scheme to support multiple video streams over multi-hop wireless video sensor network in this paper. The simulation results show that our algorithm can reduce frame loss rate and improve video quality significantly.
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A comparative analysis and proposing ‘ANN Fuzzy AHP model’ for requirements prioritization
Статья научная
Requirements prioritization is an essential component of software release planning and requirement engineering. In requirement engineering the requirements are arranged as per their priority using prioritization techniques to develop high-quality software’s. It also helps to the decision makers for making good decisions about, which set of requirements should be executed first. In any software development industry a ‘software project’ may have a larger number of requirements and then it is very difficult to prioritize such type of larger number of requirements as per their priority when stakeholder’s priorities are in the form of linguistic variables. This paper presents a comparative analysis of existing seven techniques based on various aspects like: scale of prioritization, scalability, time complexity, easy to use, accuracy, and decision making, etc. It was found from literature survey none of the techniques can be considered as the best one. These techniques undergo from a number of drawbacks like: time complexity, lack of scalability, Negative degree of membership function, inconsistency ratio, rank updates during requirement development, and conflicts among stakeholders. This paper proposed a model called ‘ANN Fuzzy AHP model’ for requirements prioritization that will overcome these limitations and drawbacks. In the investigation of this proposed model, a case study is implemented. Ozcan et al [31] using a FAHP (Fuzzy AHP) with ANN based technique to choose the best supplier based on the multiple criteria. The examination on ANN with FAHP is performed on MATLAB software and outcome evaluated by fuzzy pair-wise comparison matrix with three supplier selection criteria states that the requirements prioritization outcome is better from existing techniques.with higher priority.
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A comparison between syllable, di-phone, and phoneme-based Myanmar speech synthesis
Статья научная
Among the speech synthesis approach, concatenative method is one of the most popular method which can produce more natural sounding speech output. The most important challenge in this method is choosing an appropriate unit for creating a database. The present used speech units are word, syllable, di-phone, tri-phone and phoneme. The speech quality may be trade-off between the selected speech units. This paper presents the three speech synthesis system of Myanmar language, respectively based on syllable, di-phone and phoneme speech units by using concatenation method. Then, we compare the speech quality of the three systems, using the subjective tests.
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A context-aware reference architecture for ambient assisted living information systems
Статья научная
AAL (Ambient Assisted Living) existing architectures lack the sense of abstraction. The overall existing designs propose a set of elements combined with specific technologies. These visions of AAL systems narrows the possibilities and the choices ahead of the engineers and strict the range of using new technologies, which are likely to be easier and affordable. In this paper, we propose a context-aware RA (Reference Architecture) suitable for the design of distributed AAL systems. Our design is standardized and technology independent. Our aim is to provide a common background for developers and deployers to achieve a common understanding while designing the systems. The major gain is to reduce the efforts made while integrating several systems into one complete and stable environment. Ignoring all the specifications, the details and the objectives of the systems, we introduce the standard qualifications, practices and experiences that assimilate the core of every AAL oriented system. Our perception is global, unified and standard. In addition, it presents an infrastructure that would survive the evolution of technologies. It is adjustable and adaptable to the different possibilities of AAL applications.
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A data analysis of the academic use of social media
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The use of Facebook, in higher education, has become common place presumably due to a general belief that the platform can promote information flows between students and with staff as well as increasing a sense of community engagement. This study sets out to examine the academic use of Facebook groups using data analysis in order to determine if there are educational benefits and if Facebook group based learning strategies can be evaluated quickly and relatively easily. The data analysis involved utilising Social Network Analysis (SNA) in examining two Facebook groups; one under-graduate ‘course’ based group with 135 members and one under-graduate first year ‘module’ based group with 123 members. The SNA metrics included degree centrality, betweeness centrality, clustering coefficient and eigenvector centrality. The study also involved conducting a survey and interviews drawn from users of the Facebook groups to validate the utility of the SNA metrics. Results from the validation phase of the data analysis suggested that degree centrality is a useful guide to positive attitudes towards information flows, whilst betweenness centrality is useful for detecting a sense of academic community. The validation outcomes also suggest that high clustering coefficient scores were associated with a lower perception of academic community. The analysis of the data sets also found that the ‘course’ based group had higher scores for degree centrality and betweenness. This suggests that the ‘course’ based group provided a better experience of information access and a sense of academic community. Follow up interviews with respondents suggested that the ‘course’ based Facebook group may have had higher scores because it included more real world acquaintances than the ‘module’ based group.
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A domain specific key phrase extraction framework for email corpuses
Статья научная
With the growth in the communication over Internet via short messages, messaging services and chat, still emails are the most preferred communication method. Thousands of emails are been communicated everyday over different service providers. The emails being the most effective communication methods can also attract a lot of spam or irrelevant information. The spam emails are annoying and consumes a lot of time for filtering. Regardless to mention, the spam emails also consumes the main allocated inbox space and at the same time causes huge network traffic. The filtration methods are miles away from perfection as most of these filters depends on the standard rules, thus making the valid emails marked as spam. The first step of any email filtration should be extracting the key phrases from the emails and based on the key phrases or mostly used phrases the filters should be activated. A number of parallel researches have demonstrated the key phrase extraction policies. Nonetheless, the methods are truly focused on domain specific corpuses and have not addressed the email corpuses. Thus this work demonstrates the key phrases extraction process specifically for the email corpuses. The extracted key phrases demonstrate the frequency of the words used in that email. This analysis can make the further analysis easier in terms of sentiment analysis or spam detection. Also, this analysis can cater to the need for text summarization. The proposed component based framework demonstrates a nearly 95% accuracy.
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A failure detector for crash recovery systems in cloud
Статья научная
Cloud computing has offered remarkable scalability and elasticity to distributed computing paradigm. It provides implicit fault tolerance through virtual machine (VM) migration. However, VM migration needs heavy replication and incurs storage overhead as well as loss of computation. In early cloud infrastructure, these factors were ignorable due to light load conditions; however, nowadays due to exploding task population, they trigger considerable performance degradation in cloud. Therefore, fault detection and recovery is gaining attention in cloud research community. The Failure Detectors (FDs) are modules employed at the nodes to perform fault detection. The paper proposes a failure detector to handle crash recoverable nodes and the system recovery is performed by a designated checkpoint in the event of failure. We use Machine Repairman model to estimate the recovery latency. The simulation experiments have been carried out using CloudSim plus.
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A hybrid dimensionality reduction model for classification of microarray dataset
Статья научная
In this paper, a combination of dimensionality reduction technique, to address the problems of highly correlated data and selection of significant variables out of set of features, by assessing important and significant dimensionality reduction techniques contributing to efficient classification of genes is proposed. One-Way-ANOVA is employed for feature selection to obtain an optimal number of genes, Principal Component Analysis (PCA) as well as Partial Least Squares (PLS) are employed as feature extraction methods separately, to reduce the selected features from microarray dataset. An experimental result on colon cancer dataset uses Support Vector Machine (SVM) as a classification method. Combining feature selection and feature extraction into a generalized model, a robust and efficient dimensional space is obtained. In this approach, redundant and irrelevant features are removed at each step; classification presents an efficient performance of accuracy of about 98% over the state of art.
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