Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1195
Статья научная
Ever since the introduction of rough sets by Pawlak as a model to capture uncertainty, it has drawn much attention from both theoretical and application point of view. Classifications of universes play very important roles in several fields of study. The study of rough definability of classifications was initiated by Busse. The properties of approximations of classifications were established in the form of four theorems and were used to define the types of classifications. These results were generalised to develop two theorems of necessary and sufficient type were established by Tripathy et al , from which several results including the four theorems of Busse could be derived as corollaries. Recently, rough sets based on Multigranulation were introduced and studied by Qian et al. Also, it has been extended to include incomplete information systems. Many of these results are extended to the multigranular cases. In this paper, we extend the properties of types of classifications to the multigranular context. Also, we introduce some parameters like the accuracy of approximation and the quality of approximation of classifications with respect to Multigranulations. We have obtained interesting criteria under which both types of Multigranulations reduce to single granulation. Also, some algebraic properties of Multigranulations are derived.
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Some Observations on Dependency Analysis of SOA Based Systems
Статья научная
This paper presents some observations on Dependency Analysis of Service Oriented Architecture (SOA) based systems. In general, dependency analysis is based on the internal properties of artifacts (objects/components/services) and inter-relationship between the artifacts in the system. In order to make a dependency analysis for SOA based systems, one have to consider the special features of SOA that make them different with other approaches. This paper surveys the previous works taken on dependency analysis of service oriented systems. The present work provides insights about definitions related to service dependency, the modeling and analysis techniques of service dependency analysis, failure results to service dependence and some research challenges of the topic.The contribution of this paper is for novice researchers working on this topic as they can get an overview of dependency analysis of SOA based systems for their further research.
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Spatial co-location patterns on weather and forest fire data
Статья научная
One of problems that can increase the risk of forest fire occurrences in Indonesia is drought which is affected by weather conditions. Therefore, weather conditions and forest fire are strongly related. Spatial co-location pattern can be applied to identify the weather conditions that are vulnerable to fires based on the distance between weather observation points and hotspot occurrences. The purpose of this study is to apply the co-location miner algorithm on the weather and hotspot data in Rokan Hilir Riau Indonesia and to analyze the generated co-location patterns. Experimental results show that precipitation which co-located with hotspot occurrences are 0.08–6.69 mm/day. In addition, the temperature which co-located with hotspot occurrences are 22°C–29.17°C. Inside the intervals, hotspots will occur in the radius of 9.724 km from the precipitation and temperature observation points. In 2008, many hotspots were found on the three areas in the study area with the average of precipitation around 3.65–3.71 mm/day and temperature around 24.44°C–25.23°C.
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Speech Quality Assessment of VoIP: G.711 VS G.722 Based on Interview Tests with Thai Users
Статья научная
This paper presents the comparison between two codecs, G.711 and G.722 at 64 kbps, referring to speech quality perception using a subjective method called interview tests. These subjective tests have been conducted with 201 subjects, who are Thai native speakers that use Thai which is a tonal language, for accuracy and reliability of results. The results from testing with both codecs are almost the same; the scores are 4.17 for G.722 and 4.14 for G.711. After analyzing the results, it has been confirmed that G.722 does not provide better speech quality than G.711 to the Thai subjects significantly, which is consistent with previous information. However, these results could be used as the benchmark of G.711 and G.722 for speech quality assessment within Thai environments.
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Статья научная
Both fuzzy logic and sliding mode can compensate the steady-state error of proportional-derivative (PD) control. This paper presents parallel sliding mode compensations for fuzzy PD controllers. The asymptotic stability of fuzzy PD control with first-order sliding mode compensation in the parallel structure is proven. For the parallel structure, the finite time convergence with a super-twisting second-order sliding-mode is guaranteed.
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Статья научная
Most local communities in Tanzania depend on herbal remedies as the primary source of health care and such knowledge have been stored in the minds of the elderly who pass it on orally to young generations. However, the method is not reliable, as there is a likelihood of gradual loss of such knowledge as the elderly become older and incapacitated. It is at the backdrop of such a scenario that this study investigated the stakeholder’s attitude towards the use of information and communication technology tools in preserving traditional medical knowledge in Tanzania. The study also investigated the existing approaches for managing both traditional medical practitioners, herbaria activities and the difficulties. Both quantitative and qualitative data were employed and the study covered Arusha, Kagera and Dar es Salaam regions where 60 ethnobotanical researchers and 156 traditional medical practitioners were involved. The collected data was analyzed using R and Tableau software. The study indicated that 75% of traditional medical practitioners use story-telling for preserving traditional medical knowledge; 86.53% of practitioners indicated that much of the knowledge has disappeared over generations. More than half (69.87%) of practitioners were aware of the existence of technological devices for accessing the internet and 80.5% of researchers and practitioners believed that Information and Communication Technology tools have benefits in the practice of traditional medicine. From the findings, the study came up with the ICT model solution that can help in documenting, preserving and disseminating traditional medical knowledge and integrate the management of stakeholders in Tanzania.
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Star Coloring Problem: The DNA Solution
Статья научная
In this paper, a DNA based computing model for solving the star coloring problem is proposed. This model shows how to use DNA strands to construct solution space of molecules for the star coloring problem and how to apply the DNA algorithm to solve the star coloring problem using biological operations. The algorithm is highly parallel and has satisfactory fidelity. The time complexity of the algorithm is O (n2), where n is the number of vertices of the graph.
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Статья научная
In this paper, new statistical features based approach (SFBA) for hourly energy consumption prediction using Multi-Layer Perceptron is presented. The model consists of four stages: data retrieval, data pre-processing, feature extraction and prediction. In the data retrieval stage, historical hourly consumed energy data has been retrieved from the database. During data pre-processing, filters have been applied to make the data more suitable for further processing. In the feature extraction stage, mean, variance, skewness, and kurtosis are extracted. Finally, Multi-Layer Perceptron has been used for prediction. For experimentation with Multi-Layer Perceptron with different training algorithms, a final model of the network was designed in which the scaled conjugate gradient (trainscg) was used as a network training function, tangent sigmoid (Tansig) as a hidden layer transfer function and linear function as an output layer transfer function. For hourly energy consumption prediction, a total of six weeks data of ten residential buildings has been used. To evaluate the performance of the proposed approach, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), evaluation measurements were applied.
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Статья научная
Gross Domestic Product (GDP) per capita is a critical degree of a nation's monetary growth that records for its number of people. A balanced participation ratio of both males and females in the industry by ensuring skilled and technical education for all provides a stable economic development in a country. Population and Gender impact on GDP prices in Bangladesh were investigated in this study. To address the effect of gender factors in GDP prices, we considered the following parameters: year, combined population, male population, and female population. Based on these parameters, the global domestic product-current prices of Bangladesh were analyzed. For the predictive analysis, we have used various machine learning algorithms to make prediction and visualization of the predicted output. A quantitative analysis was also performed to examine the correlation among different gender factors with the growth of GDP. Based on analysis and study results, we can say that the machine learning approach could be applied efficiently in numerous applications of GDP forecasting.
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Статья научная
The negative impact of out-of-school students' problems at the basic and high-school levels is always very weighty on the affected individuals, parents, and society at large. Owing to the weighty negative consequences, policymakers, different government agencies, educators and researchers have long been looking for how to effectively study and forecast the trends as a means of offering a concrete solution to the problem. This paper develops a better hybrid machine learning method, which combines the least square and support vector machine (LS-SVM) model for robust prediction improvement of out-of-school children trend patterns. Particularly, while other previous works only engaged some regional and few samples of out-of-school datasets, this paper focused on long-ranged global out-of-school datasets, collated by UNESCO between 1975- 2020. The proposed hybrid method exhibits the optimal precision accuracies with the LS-SVM model in comparison with ones made using the ordinary SVM model. The precision performance of both LS-SVM and SVM was quantified and a lower NRMSE value is preferred. From the results, the LS-SVM attained lower error values of 0.0164, 0.0221, 0.0268, 0.0209, 0.0158, 0.0201, 0.0147 and 0.0095 0.0188, compared to the SVM model that attained higher NRMSE values of 0.041, ,0.0628, 0.0381, 0.0490, 0.0501, 0.0493, 0.0514, 0.0617 and 0.0646, respectively. By engaging the MAPE indicator, which expresses the mean disconnection between the sourced and predicted values of the out-of-school data. By means of the MAPE, LS-SVM attained lower error values of 0.51, 1.88, 0.82, 2.38, 0.62, 2.55, 0.60, 0.60, 1.63 while SVM attained 1.83, 7.39, 1.79 7.01, 2.43, 8.79, 2.58, 4.13, 6.18. This implies that the LS-SVM model has better precision performance than the SVM model. The results attained in this work can serve as an excellent guide on how to explore hybrid machine-learning techniques to effectively study and predict out-of-school students among researchers and educators.
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Story scrambler - automatic text generation using word level RNN-LSTM
Статья научная
With the advent of artificial intelligence, the way technology can assist humans is completely revived. Ranging from finance and medicine to music, gaming, and various other domains, it has slowly become an intricate part of our lives. A neural network, a computer system modeled on the human brain, is one of the methods of implementing artificial intelligence. In this paper, we have implemented a recurrent neural network methodology based text generation system called Story Scrambler. Our system aims to generate a new story based on a series of inputted stories. For new story generation, we have considered two possibilities with respect to nature of inputted stories. Firstly, we have considered the stories with different storyline and characters. Secondly, we have worked with different volumes of the same stories where the storyline is in context with each other and characters are also similar. Results generated by the system are analyzed based on parameters like grammar correctness, linkage of events, interest level and uniqueness.
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Strategies for Searching Targets Using Mobile Sensors in Defense Scenarios
Статья научная
Target searching is one of the challenging research areas in defense. Different types of sensor networks are deployed for searching targets in critical zones. The selection of optimal strategies for the sensor nodes under certain constraints is the key issue in target searching problem. This paper addresses a number of target searching problems related to various defense scenarios and introduces new strategic approaches to facilitate the search operation for the mobile sensors in a two-dimensional bounded space. The paper classifies the target searching problems into two categories: preference-based and traversal distance based. In the preference based problems, the strategies for the mobile sensors are determined by Stable Marriage Problem, College Admission Problem, and voting system; they are analyzed with suitable examples. Alternatively, traversal distance based problems are solved by our proposed graph searching approaches and analyzed with randomly chosen examples. Results obtained from the examples signify that our proposed models can be applied in defense-related target searching problems.
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Статья научная
The rapid growth of data in various industries has led to the emergence of big data analytics as a vital component for extracting valuable insights and making informed decisions. However, analyzing such massive volumes of data poses significant challenges in terms of storage, processing, and analysis. In this context, the Hadoop ecosystem has gained substantial attention due to its ability to handle large-scale data processing and storage. Additionally, integrating machine learning models within this ecosystem allows for advanced analytics and predictive modeling. This article explores the potential of leveraging the Hadoop ecosystem to enhance big data analytics through the construction of machine learning models and the implementation of efficient data warehousing techniques. The proposed approach of optimizing stock price by constructing machine learning models and data warehousing empowers organizations to derive meaningful insights, optimize data processing, and make data-driven decisions efficiently.
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Статья научная
In this paper ‘DWT-SVD’ based Color Image Watermarking technique in YUV color space using Arnold Transform is proposed. The RGB color image is converted into YUV color space. Image is decomposed by 3 level DWT and then SVD is applied. The security is increased with watermark scrambling using Arnold Transform. The watermark is embedded in all Y,U and V color spaces in HL3 region. The decomposition is done with ‘Haar’ which is simple, symmetric and orthogonal wavelet and the direct weighting factor is used in watermark embedding and extraction process is used. PSNR and Normalized Correlations (NC) values are tested for 10 different values of flexing factor. We got maximum PSNR up to 52.3337 for Y channel and average value of NC equal to 0.99 indicating best recovery of watermark. The proposed scheme is non blind and strongly robust to different attacks like compression, scaling, rotation, cropping and Noise addition which is tested with standard database image of size 512x512 and watermark of size 64X64.
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Structural Conditions on Observability of Nonlinear Systems
Статья научная
In this paper parameter space and Lebesgue measurement are introduced into analysis of nonlinear systems. Structural observability rank condition is defined and together with the distinguishabililty the structural observability criterions of nonlinear systems are obtained. It proves that when the parameters are not identifiable the solutions with the same time but different parameters are also indistinguishable. Differential geometry and algebraic methods are used to investigate the observability problem, and it is proved that there are some relations between these two methods. Finally, examples are used to illustrate applications of the structural observability criterions.
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Study and Performance Evaluation on Recent DDoS Trends of Attack & Defense
Статья научная
Different types and techniques of DDoS attacks & defense are studied in this paper with some recent information on attacks dominated in year 2012 (1st Quarter). We further provide simulation based analysis of an FTP server’s performance in a typical enterprise network under distributed denial of service attack. Simulations in OPNET show noticeable variations in connection capacity, task processing and delay parameters of the attacked server as compared to the performance without attack. DDoS detection and mitigation mechanisms discussed in this paper mainly focus on some recently investigated techniques. Finally, conclusions are drawn on the basis of survey based study as well as simulation results.
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Study of Context Modelling Criteria in Information Retrieval
Статья научная
Whereas the majority of works and research about context-awareness in ubiquitous computing provide context models that make use of context features in a particular application, one of the main challenges these last years has been to come out with prospective standardization of context models. As for Information Retrieval, the lack of consensual Context Models represents the biggest issue. In this paper, we investigate the importance of good context modelling to overcome some of the issues surrounding a search task. Thus, after identifying those issues and listing and categorizing the modelling requirements, the objective of our research is to find correlations between the appreciations of context quality criteria taking into account the user dimension. Likewise, the results of a previous survey about search habits have been used such that many socio-demographic categories were considered and the Kendall's W evaluation performed together with the Friedman test provided very interesting results that encourage the feasibility of building large scale context models.
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Study of Covering Based Multi Granular Rough Sets and Their Topological Properties
Статья научная
The notions of basic rough sets introduced by Pawlak as a model of uncertainty, which depends upon a single equivalence relation has been extended in many directions. Over the years, several extensions to this rough set model have been proposed to improve its modeling capabilities. From the granular computing point of view these models are single granulations only. This single granulation model has been extended to multi-granulation set up by taking more than one equivalence relations simultaneously. This led to the notions of optimistic and pessimistic multi-granulation. One direction of extension of the basic rough set model is dependent upon covers of universes instead of partitions and has better modeling power as in many real life scenario objects cannot be grouped into partitions but into covers, which are general notions of partitions. So, multigranulations basing on covers called covering based multi-granulation rough sets (CBMGRS) were introduced. In the literature four types of CBMGRSs have been introduced. The first two types of CBMGRS are based on minimal descriptor and the other two are based on maximal descriptor. In this paper all these four types of CBMGRS are studied from their topological characterizations point of view. It is well known that there are four kinds of basic rough sets from the topological characterisation point of view. We introduce similar characterisation for CBMGRSs and obtained the kinds of the complement, union, and intersection of such sets. These results along with the accuracy measures of CBMGRSs are supposed to be applicable in real life situations. We provide proofs and counter examples as per the necessity of the situations to establish our claims.
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Study of Parametric Performance Evaluation of Machine Learning and Statistical Classifiers
Статья научная
Most of the researchers/ scientists are facing data explosion problem presently. Large amount of data is available in the world i.e. data from science, industry, business, survey and many other areas. The main task is how to prune the data and extract valuable information from these data which can be used for decision making. The answer of this question is data mining. Data Mining is popular topic among researchers. There is lot of work that cannot be explored in the field of data mining till now. A large number of data mining tools/software’s are available which are used for mining the valuable information from the datasets and draw new conclusion based on the mined information. These tools used different type of classifiers to classify the data. Many researchers have used different type of tools with different classifiers to obtained desired results. In this paper three classifiers i.e. Bayes, Neural Network and Tree are used with two datasets to obtain desired results. The performance of these classifiers is analyzed with the help of Mean Absolute Error, Root Mean-Squared Error, Time Taken, Correctly Classified Instance, Incorrectly Classified instance and Kappa Statistic parameter.
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Статья научная
This paper explores the possibility of applying techniques for segmenting the regions of medical image. For this we need to investigate the use of different techniques which helps for detection and classification of image regions. We also discuss some segmentation methods classified by researchers. Region classification is an essential process in the visualization of brain tissues of MRI. Brain image is basically classified into three regions; WM, GM and CSF. The forth region can be called as the tumor region, if the image is not normal. In the paper; Segmentation and characterization of Brain MR image regions using SOM and neuro fuzzy techniques, we integrate Self Organizing Map(SOM) and Neuro Fuzzy scheme to automatically extract WM, GM, CSF and tumor region of brain MRI image tested on three normal and three abnormal brain MRI images. Now in this paper this scheme is further tested on axial view images to classify the regions of brain MRI and compare the results from the Keith‘s database. Using some statistical tests like accuracy, precision, sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, false negative rate, likelihood ratio positive, likelihood ratio negative and prevalence of disease we calculate the effectiveness of the scheme.
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