International Journal of Information Technology and Computer Science @ijitcs
Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1235
Plagiarism detection system for the kurdish language
Статья научная
One of the serious issues is plagiarism, especially in the education field. Detecting the plagiarism became a challenging task, particularly in natural language texts. In the past years, some plagiarism detection tools have been developed for diverse natural languages, mainly English. Language-independent tools exist as well but are considered as too restrictive as they usually do not consider specific language features. The problem is there is no plagiarism Detection system for the Kurdish language. In this paper, we introduce a new system for plagiarism detection for Kurdish Language, based on n-gram algorithm, our system can detect the word, phrases, and paragraphs. Moreover, our system effectiveness for detect plagiarist texts in localhost and online especially in Google search engine. This system is more useful for the academic organizations such as schools, institutes, and universities for finding copied texts from another document.
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Plant disease detection system using bag of visual words
Статья научная
Plants are important to human life since plants provide the food, shelter, rain, building material, medicine, fuel such as coal, wood, etc. Therefore, planting, growing, and protecting the plants is essential for sustainable development of any nation. The plant disease can affect the growth of the plats that is caused by pathogens, living microorganisms, bacteria, fungi, nematodes, viruses, and living agents. Hence, identifying the plant disease is very essential to protect the plants in the early stage. Moreover, the plant diseases are identified from the symptoms that appear in stem, fruit, leaf, flower, root, etc. The common symptom of the plant disease can be predicted from the appearance of leaf since the appearance of leaves highly depends on the healthiness of the plant. Therefore, this paper presents a system to identify the lesion leaf from the plants in order to detect the disease occurred in the plant. This system is developed using the bag of visual words model. Moreover, the real time images are collected for various plants and tested with this system and the system produces better results for the given set of images.
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Platform-Independent Courseware Sharing
Статья научная
Courseware distribution between different platforms is the major issue of current e-Learning. SCORM (Sharable Content Object Reference Model) is one of the solutions for courseware sharing. However, to make SCORM-conformable courseware, some knowledge about HTML and JavaScript is required. This paper presents a SWF (Sharable Web Fragment)-based e-Learning system, where courseware is created with sharable Web fragments such as Web pages, images and other resources, and the courseware can be distributed to another platform by export and import facilities. It also demonstrates how to export a subject that contains assignments and problems and how to import the whole subject, only the assignments, or only the problems. The exported meta-information is architecture-independent and provides a model of courseware distribution.
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Polynomial Differential-Based Information-Theoretically Secure Verifiable Secret Sharing
Статья научная
In Pedersen’s VSS scheme the secret is embedded in commitments. And the polynomial used is of degree at most (t-1). In strong – (t, n) VSS which based on Pedersen’s scheme that polynomial in verification purpose is public polynomial. The public polynomial in their scheme which acts in verification purpose is not secure. And the secret is secure if the dealer cannot solve the discrete logarithm problem. In our propose scheme we will satisfy the security requirements in strong t-consistency and consider the security on verification polynomial used. We will show in shares verification algorithm the participants can verify that their shares are consistent and the dealer is honest (i.e. the dealer cannot success in distributing incorrect shares even the dealer can solve the discrete logarithm problem.) before start secret reconstruction algorithm. The security strength of the proposed scheme lies in the fact that the shares and all the broadcasted information convey no information about the secret.
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Статья научная
The recommendation is playing an essential part in our lives. Precise recommendations facilitate users to swiftly locate desirable items without being inundated by irrelevant information. In the last few years, the amount of customers, products and online information has raised speedily and results out into the huge data analysis problem for recommender systems. While handling and evaluating such large-scale data, usual service recommender systems regularly undergo scalability and inefficiency problems. Nowadays, in multimedia platform such as movie, music, games, the use of Recommender System is increased. Collaborative Filtering is a dominant filtering technique used by many RSs. CF utilizes the rating history of the user to find out "like minded" users and this set of like-minded user is then used to recommend the movies which are liked by these like-minded users but did not watch by the active user. Thus, in CF, to find out the "neighborhood" the rating history of a user is used, but the reason behind the rating is not considered at all. This will lead to inaccuracy in finding a neighborhood set and subsequently in recommendation also. To cope with these scalability and accuracy challenges, this paper proposes an innovative solution, Clustering and Review based Approach for Collaborative Filtering based Recommendation. This innovative approach is enacted with the two stages; in the first stage the clustering of the available movies for recommendation is clustered into the subclasses for further computation. In the succeeding stage, the methodology based on reviews is utilized for finding neighborhood set in User Based Collaborative Filtering.
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Predicting the Occurrence of Cerebrovascular Accident in Patients using Machine Learning Technique
Статья научная
Cerebrovascular disease commonly known as stroke is the third leading cause of disability and mortality in the world. In recent years, technological advancements have transformed the way information is acquired and how problems are solved in diverse fields of human endeavors, including the medical and healthcare sectors. Machine Learning (ML) and data driven techniques have gain prominence in problem solving and have been deployed in the prediction of the occurrences of stroke. This work explores the application of supervised machine learning algorithms for the prediction of stroke, emphasizing the critical need for early prediction to enhance preventive measures. A comprehensive comparison of classification (Support Vector Machine and Random Forest) and regression (Logistic Regression) algorithms was conducted, with concerns on binary stroke outcome (likelihood of stroke and no stroke) data utilizing dataset from the International Stroke Trial database. The Synthetic Minority Oversampling Technique (SMOTE) and K-fold cross validation were used to balance and address the class imbalance in the datasets. The subsequent model comparison demonstrated distinct strengths and weaknesses among the three models. Random Forest (RF) exhibited high accuracy score of 89%, Support Vector Machine (SVM) and Logistic Regression (LR) showed 86% accuracy. LR demonstrated the most balanced predictive performance, achieving high precision for stroke cases and reasonable recall for both classes.
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Prediction Model of the Stock Market Index Using Twitter Sentiment Analysis
Статья научная
Stock market prediction has been an interesting research topic for many years. Finding an efficient and effective means of predicting the stock market found its way in different social networking platforms such as Twitter. Studies have shown that public moods and sentiments can affect one's opinion. This study explored the tweets of the Filipino public and its possible effects on the movement of the closing Index of the Philippine Stock Exchange. Sentiment Analysis was used in processing individual tweets and determining its polarity - either positive or negative. Tweets were given a positive and negative probability scores depending on the features that matched the trained classifier. Granger causality testing identified whether or not the past values of the Twitter time series were useful in predicting the future price of the PSE Index. Two prediction models were created based on the p-values and regression algorithms. The results suggested that the tweets collected using geo location and local news sources proved to be causative of the future values of the Philippine Stock Exchange closing Index.
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Prediction Models for Diabetes Mellitus Incidence
Статья научная
Diabetes mellitus is an incurable disease with global prevalence and exponentially increasing incidence. It is one of the greatest health hazards of the twenty-first century which poses a great economic threat on many nations. The premise behind effective disease management in healthcare system is to ensure coordinated intervention targeted towards reducing the incidence of such disease. This paper presents an approach to reducing the incidence of diabetes by predicting the risk of diabetes in patients. Diabetes mellitus risk prediction model was developed using supervised machine learning algorithms of Naïve Bayes, Support Vector Machine and J48 Decision Tree. The decision tree was able to give a prediction accuracy of 95.09% using rules of prediction that give acceptable results, that is, the model was approximately 95% accurate. The easy-to-understand rules of prediction got from J48 decision tree make it excellent in developing predictive models.
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Prediction and monitoring agents using weblogs for improved disaster recovery in cloud
Статья научная
Disaster recovery is a continuous dilemma in cloud platform. Though sudden scaling up and scaling down of user’s resource requests is available, the problem of servers down still persists getting users locked at vendor’s end. This requires such a monitoring agent which will reduce the chances of disaster occurrence and server downtime. To come up with an efficient approach, previous researchers’ techniques are analyzed and compared regarding prediction and monitoring of outages in cloud computing. A dual functionality Prediction and Monitoring Agent is proposed to intelligently monitor users’ resources requests and to predict coming surges in web traffic using Linear Regression algorithm. This solution will help to predict the user’s future requests’ behavior, to monitor current progress of resources’ usage, server virtualization and to improve overall disaster recovery process in Cloud Computing.
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Статья научная
Pharmacy handles all the medicine needed in the hospital that consists of vast amount of records. These produce large scale of datasets that are complex to manage and thereby need tools and technique to easily process, interpret, forecast and predict future consumption. Due to this, the method of predicting and forecasting stock consumption using Data Mining technique in hospital pharmacy is not be a surprising issue. Thus, this research investigated the potential applicability of data mining technology to predict the Anti-Retroviral drugs consumption for pharmacy based up on patient's history datasets of Jugal hospital, Harar, Ethiopia. The methodology used for this research is based on Knowledge Discovery in Database which had mostly relied on using the decision tree algorithms specifically M5P model tree. WEKA software, a data-mining tool were used for interpreting, evaluating and predicting from large datasets. Result with the data set suggests that tree based modeling approach can effectively be used in predicting the consumption of ARV drugs.
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Prediction of Defect Prone Software Modules using MLP based Ensemble Techniques
Статья научная
Prediction of defect prone software modules is now considered as an important activity of software quality assurance. This approach uses the software metrics to predict whether the developed module is defective or not. This research presents MLP based ensemble classification framework to predict the defect prone software modules. The framework predicts the defective modules by using three dimensions: 1) Tuned MLP, 2) Tuned MLP with Bagging 3) Tuned MLP with Boosting. In first dimension only the MLP is used for the classification after optimization. In second dimension, the optimized MLP is integrated with bagging technique. In third dimension, the optimized MLP is integrated with boosting technique. Four publically available cleaned NASA MDP datasets are used for the implementation of proposed framework and the performance is evaluated by using F-measure, Accuracy, Roc Area and MCC. The performance of the proposed framework is compared with ten widely used supervised classification techniques by using Scott-Knott ESD test and the results reflects the high performance of the proposed framework.
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Prediction of Missing Values for Decision Attribute
Статья научная
The process of determining missing values in information system is an important issue for decision making especially when the missing values are in the decision attribute. The main goal for this paper is to introduce algorithm for finding missing values of decision attribute. Our approach is depending on distance function between existing values. These values can be calculated by distance function between the conditions attributes values for the complete information system and incomplete information system. This method can deal with the repeated small distance by eliminating a condition attribute which has the smallest effect on the complete information system. This algorithm will be discussed in detail with an example of a case study.
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Preference-Based Web Service Composition: Case-Based Planning Approach
Статья научная
Web service selection is an indispensable process for web service composition. However it became a difficult task as many web services are increased on the web and mostly they offer similar functionalities, which service will be the best. User preferences are the key to retain only the best services for the composition. In this paper, we have proposed a web service composition model based on user preferences. To improve the process of web service composition we propose a case-based planning approach with user preferences which uses successful experiences in past to solve similar problems. In this paper we integrate user preferences in the phase of selection, adaptation and planning. Our main contributions are a new method of case retrieval, an extended algorithm of adaptation and planning with user preferences. Results obtained offer more than a solution to the user and taking both functional and non-functional requirements.
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Primary-Backup Access Control Scheme for Securing P2P File-Sharing Systems
Статья научная
Peer-to-peer (P2P) file-sharing systems have gained large interests among the internet users. However, wide-scale applications of P2P file-sharing technologies are constrained by the limitations associated with the sophisticated control mechanisms. Moreover, the decentralized and anonymous characteristics of P2P environments make it more difficult to control accesses on the shared resources, especially for using traditional access control methods. To overcome these limitations, we propose a role-based access control architecture for P2P file-sharing systems that supports autonomous decisions and centralized controls. The architecture integrates policies of credential, identity and role-based access control models to provide scalable, efficient and fault-tolerant access control services. Furthermore, we employ the primary-backup (PB) scheme to preserve P2P decentralized structure and peers’ autonomy property while enabling collaboration between peers. In particular, we propose a method for setting up interoperating relationships between domains by role mappings and resolve two kinds of interoperability conflicts while mapping roles from foreign domain to local domain without centralized authority. We believe that the proposed architecture is realistic, efficient and can provide controlled communications between peers.
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Статья научная
The study is devoted to assessing the risks of cyber threats in the future based on expert sampling patterns. One of the key problems of modern cybersecurity is the dynamic nature of threats that change under the influence of technological progress and socio-economic factors. In this context, the authors consider a methodological approach that involves the use of a multi-level analysis of expert opinions. The main emphasis is placed on taking into account the different points of view, experience and professional activities of experts from the public, private and academic sectors. An important stage of the study is the procedure of data cleaning to form a representative sample that takes into account only logically consistent responses of experts. The paper focuses on the integration of the expert sample patterns‘ features. The key differences in threat assessments between different groups of experts depending on their professional role and experience are identified. This made it possible to formulate comprehensive recommendations for strategic cyber risk management focused on both short-term and long-term priorities. The study makes a significant contribution to understanding the peculiarities of cyber risk assessment through the use of multivariate analysis of expert opinions. The proposed methodology allows not only to improve the quality of forecasts of future cyber threats, but also contributes to the creation of adaptive cybersecurity strategies that take into account the specifics of each sector. The findings of the study emphasize the importance of a multidimensional approach to analyzing cyber threats, taking into account the specifics of each expert group. Integration of assessments and consideration of local peculiarities are key to the development of adaptive and effective cyber defense strategies focused on global and local challenges.
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Prioritization of Barriers to Digitization for Circular Systems using Analytical Hierarchy Process
Статья научная
The increasing urgency for sustainable practices has motivated this research to explore the barriers hindering the adoption of digital technologies in circular systems. As industries seek to leverage IoT for enhanced efficiency and sustainability, understanding these barriers is crucial for effective implementation. This study employs a comprehensive, multi–dimensional approach, integrating insights from a literature review and expert interviews with industry professionals. Key findings reveal that technological complexity and high initial costs are the most significant barriers, highlighting the need for targeted strategies to address these challenges. Additional barriers include regulatory compliance issues and unclear return on investment, which further complicate the adoption process. The study's conclusion emphasizes that overcoming these barriers is essential for facilitating the successful integration of digital technologies in circular economies. Furthermore, the research identifies the necessity for future investigations into the interactions between these barriers and the effectiveness of various interventions. The novelty of this study lies in its holistic examination of the multifaceted barriers, combining qualitative insights with a structured analytical framework. This approach not only contributes to the existing literature on Digitization but also offers practical implications for stakeholders aiming to enhance sustainability and efficiency in their operations. By addressing the identified challenges, organizations can pave the way for a more circular and resilient future, ultimately driving innovation and growth in the rapidly evolving digital landscape.
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Priority Based New Approach for Correlation Clustering
Статья научная
Emerging source of Information like social network, bibliographic data and interaction network of proteins have complex relation among data objects and need to be processed in different manner than traditional data analysis. Correlation clustering is one such new style of viewing data and analyzing it to detect patterns and clusters. Being a new field, it has lot of scope for research. This paper discusses a method to solve problem of chromatic correlation clustering where data objects as nodes of a graph are connected through color-labeled edges representing relations among objects. Purposed heuristic performs better than the previous works.
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Priority Based Uplink Scheduling Scheme for WiMAX Service Classes
Статья научная
IEEE 802.16 standard supports five different service classes in order to support different needs of the mobile users with different QoS criteria. But, this standard doesn’t specify admission control and scheduling mechanisms and so far many solutions are proposed in the science world. In this paper we propose priority based uplink scheduling scheme for IEEE 802.16 standard that improves the QoS performances of the five WiMAX service classes, especially of the ertPS service class. Simulation experiments and analysis are done choosing the most adequate WiMAX simulator and the ns-2 simulation tool. Traffic load of the ertPS connections is changed from low to high in order to perform detailed performance analysis of the results. Results given in average delay, average jitter and average throughput are evaluated and compared with so far known uplink scheduling mechanism. The results show that our proposed uplink scheduling scheme based on the priority of the service classes improves the QoS performances especially in high loaded scenarios.
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Processing of Satellite Digital Images for Mapping Atmospheric Transmissivity in Bangladesh
Статья научная
This study investigates the potential of determining atmospheric transmissivity (τ) from NOAA-AVHRR satellite images using a simple methodology. Using this method, hourly transmissivity values over the land surface area of Bangladesh has been determined. The spatio-temporal distribution of τ has been studied by constructing monthly average maps for the whole country for one complete year (February 2005 to January 2006). Yearly average map has been prepared by integrating monthly average maps. Geographical distribution of τ exhibits patterns and trends. It is observed that the value of τ varies from 0.3 to 0.65 with the average maximum value in the month of April and minimum value in the month of November. It is also observed that for western parts of the country, which is the drought prone area, transmissivity values are little bit higher than that at the eastern parts. Relatively lower values of τ in the dry months (November to January) may be due to the effect of particulate or chemical pollution in the atmosphere.
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Статья научная
Software process improvement (SPI) is an important requirement in a software company. The search for better approach brought different kinds of models with multiple sets of principles for SPI to be founded. The framework is proposed to mainly address an alternative way of achieving a better process capability. The approach focuses on the implementation of SPI which can seamlessly align with the organization nature, day to day business activities, and financial capability. The paper provides the detailed implementation guideline and application of the framework through case study results. The case study is performed in a software development unit placed under academic institution. The unit is founded specifically for application development for internal and external customers. The case study is designed to be implemented in two software development projects in the development unit. From the ongoing case study, the results from the first project which is completed in six iterations are presented in this paper. Considering SPI implementation, the development team followed the framework and associated procedures throughout the development process. The results obtained in terms of aligning SPI to the daily development task and CMMI KPAs capability improvement achieved showed promising results.
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