Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1195
Classification of the fire station requirement with using machine learning algorithms
Статья научная
In crowded cities, selection of the suitable location for fire stations within the town is a vital issue in terms of rapid response to fires and minimizing loss of life and property. For the selection of the suitable fire station location, at first it is necessary to divide the whole city into certain zones and the need for a fire station service should be questioned for each zone. In this study, based on existing fire stations service area, classification of fire station requirement by zones was carried out using machine learning classification algorithms. In order to estimate fire station requirement according to the zones, a classification study was conducted by using some data such as the travel time of the fire engines to zone from closed fire stations, population density of the zone, the mean number of main and assistant vehicles travelling to the zone from closed fire stations, and the fire station existence data in the zone. The purpose of this study was to determine the most successful classification algorithm for the classification of the fire station requirement of 808 zones determined by Izmir Metropolitan Municipality. As a result of the analysis of fire records between 2015 and 2017, it was found that for the classification of the zones, the most successful algorithm was Random Forest algorithm with 93.84% accuracy rate. Experimental evaluation of the study; according to the 5-minute access distance of the existing fire stations, the fire station requirements of the regions and the fire station needs of the regions covered by the machine learning algorithm classification results were found to be 85.43% similar.
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Cloud Computing – A market Perspective and Research Directions
Статья научная
Computational paradigm has been revolving round cloud computing and its offshoots for some time and till we see a breakout resulting in a breakthrough technology driven by advances in microelectronics and material technology. Till we experience a radically efficient technology for computation it is worth juxtaposing the virtues of cloud computing and market’s longing for offering cost and quality arbitrage to the marketplace. Integration of cloud computing in enterprises has the potential to influence the way business gets carried out by them in the market place. Different reports show that demand for cloud computing products and processes is in an upward growth trajectory. This paper identified the characteristics, drivers and constraints of cloud computing which influence its adaptation and integration in enterprises. We are also examining India specific opportunities and threats of cloud computing tools and cloud driven practices in the context of fierce competition among enterprises to remain competitive in the marketplace by reducing software licensing fees, cost of capital to acquire digital systems and cost of maintenances. New directions in cloud computing are analyzed by using Gartner strategic technologies and trend in research publications. Paper focuses on exploring the research issues which are categorized into technical and business in nature for understanding the evolving fortunes of cloud computing. Number of papers published in IEEE is an indication of the popularity and relevance of the continued research initiatives happening in the area. It is also noticed that that very few researchers are attempting to understand the possibility of remodeling business processes leveraging the new found computational paradigm.
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Cloud Computing: A review of the Concepts and Deployment Models
Статья научная
This paper presents a selected short review on Cloud Computing by explaining its evolution, history, and definition of cloud computing. Cloud computing is not a brand-new technology, but today it is one of the most emerging technology due to its powerful and important force of change the manner data and services are managed. This paper does not only contain the evolution, history, and definition of cloud computing, but it also presents the characteristics, the service models, deployment models and roots of the cloud.
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Clustered Webbase: A Repository of Web Pages Based on Top Level Domain
Статья научная
The World Wide Web is a huge source of hyperlinked information; it is growing every moment in context of web documents. So it has become an enormous challenge to manage the local repository (storage module of search engine) for to handling the web documents efficiently that leads to less access time of web documents and proper utilization of available resources. This research paper proposes an architecture of search engine with the clustered repository, organized in a better manner to make task easy for user to retrieving the web pages in reasonable amount of time. The research focuses on coordinator module which not only indexes the documents but also uses compression technique to increase the storage capacity of repository.
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Clustering Based on Node Density in Heterogeneous Under-Water Sensor Network
Статья научная
An underwater sensor network comprise of sensors and vehicles to perform numerous tasks. In underwater ad-hoc sensor network acoustic signals are transmitted through multi-hop sequence so as to save sensors’ energy and to achieve longer life time. Re-charging batteries of deep water deployed sensors is practically not feasible. Clustering is the best strategy to achieve efficient multi-hopping, where cluster head is made responsible to collect local data and forward it to the sink. Cluster-head selection is the challenging job in a cluster, as it loses its energy in transmitting its own data and aggregated data, as compared to other sensors. In this paper we have proposed an Under Water Density Based Clustered Sensor Network (UWDBCSN) scheme using heterogeneous sensors. The scheme utilizes two types of sensors: one having high energy capacity, working as cluster head, having small quantity and other are ordinary sensors in huge quantity. Further cluster-head selection is based on node degree i.e. the density of the sensors in a region. The proposed scheme is found to be more energy efficient helps in extending the life time of underwater sensor networks.
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Clustering Structure and Deployment of Node in Wireless Sensor Network
Статья научная
Generally, grouping sensor nodes into clusters has been widely adopted by the research community to satisfy the above scalability objective and generally achieve high energy efficiency and prolong network lifetime in large scale WSN environments. The corresponding hierarchical routing and data gathering protocols imply cluster based organization of the sensor nodes in order that data fusion and aggregation are possible, thus leading to significant energy savings. We propose a clustering approach which organizes the whole network into a connected hierarchy and discuss the design rationale of the different clustering approaches and design principles. Further, we propose several key issues that affect the practical deployment of clustering techniques in wireless sensor network applications.
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Clustering Undergraduate Computer Science Student Final Project Based on Frequent Itemset
Статья научная
Abstract is a part of document has an important role in explaining the whole document. Words that frequently appear can be used as a reference in grouping the final project document into categories. Text mining method can be used to group the abstracts. The purpose of this study is to apply the method of association rule mining namely ECLAT algorithm to find most common terms combination and to group a collection of abstracts. The data used in this study is documents of final project abstract in English of undergraduate computer science student of IPB from 2012 to 2014. This research used stopwords about common computer science terminology, applied association rule mining with support of 0.1, 0.15, 0.2, 0.25, 0.3, and 0.35, and used k-Means clustering with number of cluster (k) of 10 because it gives the lowest SSE. This research compared the value of support, SSE, the number of cluster members, and purity value in each cluster. The best clustering result is data with additional stopwords and without applying association rule mining, and with k is 10. The SSE result is 23 485.03, and with purity of 0.512
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CodeUP: A Web Application for Collaborative Question-answering System
Статья научная
The majority of collaborative learning and knowledge sharing (CLKS) platforms are built with numerous communication mediums, team and task management in mind. However, with the CLKS, the Question-Answering (QAs), User profile evaluation based on the quality of answers provided, and feeding of subject or project relevant data are all available. QAs are required for online or offline cooperation between team members or users. To that purpose, this paper presents a web application called CodeUP with features like QA system, Question similarity testing, and user profile rating for boosting communication and cooperation efficiency in CLKS for academic groups and small development teams. CodeUP is intended to be quickly established and step for academic or development groups to collaborate. As the CodeUP application supports the CLKS, it is also an ideal tool for academia and development teams to perform computer supported QA system and knowledge sharing in the sphere of work or study.
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Статья научная
Due to the lack of particular algorithms for automatic detection and tracking of person face(s), we have developed a new algorithm to achieve detection and single/multiple face tracking in different background video sequence. To detect faces, skin sections are segmented from the frame by means of YCbCr color model; and facial features are used to agree whether these sections contain person face or not. This procedure is challenging, because face color is unique and some objects may have similar color. Further, color and Eigen features are extracted from detected faces. Based on the points detected in facial region, point tracker tracks the user specified number of faces throughout the video sequence. The developed algorithm was tested on challenging dataset videos; and measured for performance using standard metrics. Test results obtained ensure the efficiency of proposed algorithm at the end.
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ComPer: A Comprehensive Performance Evaluation Method for Recommender Systems
Статья научная
Recommender Systems are receiving substantial attention in several application areas (such as healthcare systems and e-commerce), where each area has different requirements. These systems are multifaceted by nature. So, many metrics, which are sometimes contradictious, are introduced to assess different aspects. The existence of several alternatives and dimensions to recommendation approaches complicate the evaluation of recommender systems. In such a situation, it is desirable to evaluate and compare recommenders in a united way that assesses the multifaceted aspects of these systems fairly and uniformly. Despite the abundance of evaluation dimensions, the literature still lacks an evaluation method that evaluates the multiple properties of these systems, all at once. As a potential solution, this paper proposes an evaluation methodology that provides a multidimensional assessment of recommender systems. The proposed method, which we call ComPer, combines the most common evaluation dimensions into a single, yet, general evaluation metric. ComPer is inspired by the idea that a recommender system mimics human beings; hence, it can be seen as a human and its outputs can be assessed as human’s outputs. Up to our knowledge, this is the first evaluation approach that deals with recommenders as humans. ComPer aims to be thorough (by combining multiple dimensions), simple (by presenting the final result as a single value), and independent (by providing setting-independent results). The applicability of the proposed methodology is evaluated empirically using three different datasets. The initial results are promising in the sense that ComPer is able to give comparable results regardless of the experimental settings.
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Combining Fuzzy Logic and k-Nearest Neighbor Algorithm for Recommendation Systems
Статья научная
Recommendation systems are a type of systems that are able to help users finding relevant and personalized content in a wide variety of possibilities. To help computers perform recommendations, there are several approaches used nowadays such as the Content-based approach, the Collaborative filtering approach and the Hybrid recommendation approach. However, these approaches are sometimes inappropriate for use cases where there is no prior large datasets of users’ feedbacks or ratings needed for training Machine Learning models. Thus, in this work, we proposed a novel approach based on the combination of Fuzzy Logic and the k-Nearest neighbor algorithm (KNN). The proposed approach can be applied without any prior collected feedbacks of users and performs good recommendations. Moreover, our proposal uses Fuzzy Logic to infer values based on inputs and a set of rules. Furthermore, the KNN uses the output values of the Fuzzy Logic system to do some retrieval tasks based on existing distance measures. In order to evaluate our approach, we considered an expert system of food recommendation for people suffering from the two deadliest diseases in Cameroon: HIV/AIDS and Malaria. The obtained results are closed to the recommendation made by nutritionists. These results demonstrate how effective our approach can be used to solve a real nutrition problem for people suffering from Malaria or HIV/AIDS. Furthermore, this approach can be extended to other fields and even be used to perform any recommendation task where there is no prior collected user’s feedback or ratings by using the proposed approach as a framework.
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Combining Naïve Bayes and Modified Maximum Entropy Classifiers for Text Classification
Статья научная
Text Classification is done mainly through classifiers proposed over the years, Naïve Bayes and Maximum Entropy being the most popular of all. However, the individual classifiers show limited applicability according to their respective domains and scopes. Recent research works evaluated that the combination of classifiers when used for classification showed better performance than the individual ones. This work introduces a modified Maximum Entropy-based classifier. Maximum Entropy classifiers provide a great deal of flexibility for parameter definitions and follow assumptions closer to real world scenario. This classifier is then combined with a Naïve Bayes classifier. Naïve Bayes Classification is a very simple and fast technique. The assumption model is opposite to that of Maximum Entropy. The combination of classifiers is done through operators that linearly combine the results of two classifiers to predict class of documents in query. Proper validation of the 7 proposed modifications (4 modifications of Maximum Entropy, 3 combined classifiers) are demonstrated through implementation and experimenting on real life datasets.
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Combining Time Reversal and Fast Marching Method in Wireless Indoor Positioning
Статья научная
Most of current wireless indoor positioning methods could not accurately obtain channel model, the mapping between spatial position and received signal features. The main factor for a precise channel model in an indoor environment is multipath effect. Time reversed (TR) wireless indoor positioning method has been validated to effectively reduce signals fading or time delay affected by multipath effect. However, these advantages are depended on a prior known channel model, without this condition, the accuracy of TR method will be seriously deteriorated. To solve the shortcoming of a general TR method in an unknown channel model application, we present a combining Time Reversal and Fast Marching Method (TR-FMM) positioning method. This method locates a target with two stages. In the stage one, the precise channel model of an indoor environment is estimated by FMM and simultaneous algebraic reconstruction technique (SART). In this stage, Time of Flight (TOF) information generated by some fixed spatial position anchors are used to fulfill the indoor channel model estimation, then the needed channel impulse response (CIR) for TR method will be obtained based on the estimated channel model. In the stage two, with the obtained CIR, any new joint mobile target will be accurately located by a general TR wireless indoor positioning method. Some numerical simulations have been presented to validate the proposed method. Simulative results depict the positioning deviation is less than 3cm for a newly joined mobile target with 1cm scale in a moderate complex indoor configure, and the accuracy of the positioning is improved 30 times comparing to a general TR method. The positioning time in the stage 2 is less than 3 minutes in a PC with 1.6 GHz dual CPUs and 2G Bytes memory. Obviously, the proposed method has great advantage in high accuracy and low complexity for wireless indoor positioning system.
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Comparative Analysis of Data Mining Techniques for Predicting the Yield of Agricultural Crops
Статья научная
Predicting crop yields is one of the more difficult tasks in the agriculture sector. A fascinating area of research to estimate agricultural productivity has emerged from recent advancements in information technology for agriculture. Crop yield prediction is a technique for estimating crop production based on a variety of factors, including weather conditions and parameters such as temperature, rainfall, fertilizer, and pesticide use. In the world of agriculture, Data mining techniques are extremely popular. In order to predict the crop production for the following year, data mining techniques are employed and evaluated in the agricultural sector. In this paper, we carried out the comparison between Naive Bayes, K-nearest neighbor, Decision Tree, Random Forest, and K-Means clustering algorithms to predict crop yield in order to determine which method is most effective at doing so. The results show which algorithm is better suitable for this particular purpose by comparing these data mining algorithms for agricultural crop production and determining which algorithm is more successful for this outcome.
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Comparative Analysis of Data Mining Techniques to Predict Cardiovascular Disease
Статья научная
Cardiovascular disease is the leading cause of death. In recent days, most people are living with cardiovascular disease because of their unhealthy lifestyle and the most alarming issue is the majority of them do not get any symptoms in the early stage. This is why this disease is becoming more deadly. However, medical science has a large amount of data regarding cardiovascular disease, so this data can be used to apply data mining techniques to predict cardiovascular disease at the early stage to reduce its deadly effect. Here, five data mining classification techniques, such as: Naïve Bayes, K-Nearest Neighbors, Support Vector Machine, Random Forest and Decision Tree were implemented in the WEKA tool to get the best accuracy rate and a dataset of 12 attributes with more than 300 instances was used to apply all the data mining techniques to get the best accuracy rate. After doing this research people who are at the early stage of cardiovascular disease or probably going to be a victim can be identified more accurately.
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Comparative Analysis of Three Improved Deep Learning Architectures for Music Genre Classification
Статья научная
Among the many music information retrieval (MIR) tasks, music genre classification is noteworthy. The categorization of music into different groups that came to existence through a complex interplay of cultures, musicians, and various market forces to characterize similarities between compositions and organize collections is known as a music genre. The past researchers extracted various hand-crafted features and developed classifiers based on them. But the major drawback of this approach was the requirement of field expertise. However, in recent times researchers, because of the remarkable classification accuracy of deep learning models, have used similar models for MIR tasks. Convolutional Neural Net- work (CNN), Recurrent Neural Network (RNN), and the hybrid model, Convolutional - Recurrent Neural Network (CRNN), are such prominently used deep learning models for music genre classification along with other MIR tasks and various architectures of these models have achieved state-of-the-art results. In this study, we review and discuss three such architectures of deep learning models, already used for music genre classification of music tracks of length of 29-30 seconds. In particular, we analyze improved CNN, RNN, and CRNN architectures named Bottom-up Broadcast Neural Network (BBNN) [1], Independent Recurrent Neural Network (IndRNN) [2] and CRNN in Time and Frequency dimensions (CRNN- TF) [3] respectively, almost all of the architectures achieved the highest classification accuracy among the variants of their base deep learning model. Hence, this study holds a comparative analysis of the three most impressive architectural variants of the main deep learning models that are prominently used to classify music genre and presents the three architecture, hence the models (CNN, RNN, and CRNN) in one study. We also propose two ways that can improve the performances of the RNN (IndRNN) and CRNN (CRNN-TF) architectures.
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Comparative Evaluation of Mobile Forensic Tools
Статья научная
Mobile technology, over the years, has improved tremendously in sophistication and functionality. Today, there are mobile phones, known as smartphones, that can perform virtually most functions associated with personal computers. This has translated to increase in the adoption of mobile technology. Consequently, there has been an increase in the number of attacks against and with the aid of this technology. Mobile phones will often contain data that are needed as evidence in a court of law. And, therefore, the need to be able to acquire and present this data in an admissible form cannot be overemphasized. This requires the right forensic tools. This is the focus of this study. We evaluated the ability of four forensic tools to extract data, with emphasis on deleted data, from Android phones. Our results show that AccessData FTK Imager and EnCase performed better than MOBILedit Forensic and Oxygen Forensic Suite at acquiring deleted data. The conclusion is that, finding a forensic tool or toolkit that is virtually applicable across all mobile device platforms and operating systems is currently infeasible.
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Статья научная
This research focuses on the basic concepts of continuum robot manipulator and control methodology. OCTARM Continuum robot manipulator is a 6 DOF serial robot manipulator. From the control point of view, robot manipulator divides into two main parts i.e. kinematics and dynamic parts. The dynamic parameters of this system are highly nonlinear. To control of this system nonlinear control methodology (computed torque controller and sliding mode controller) is introduced. Computed torque controller (CTC) is an influential nonlinear controller to certain systems which it is based on feedback linearization and computes the required arm torques using the nonlinear feedback control law. When all dynamic and physical parameters are known computed torque controller works superbly; practically a large amount of systems have uncertainties and sliding mode controller reduce this kind of limitation. Sliding mode controller (SMC) is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for robot manipulators, because this controller is a robust and stable. Comparative study between computed torque controller and sliding mode controller is introduced in this research.
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Comparative Study: Performance of MVC Frameworks on RDBMS
Статья научная
The regular utilization of web-based applications is crucial in our everyday life. The Model View Controller (MVC) architecture serves as a structured programming design that developers utilize to create user interfaces. This pattern is commonly applied by application software developers to construct web-based applications. The use of a MVC framework of PHP Scripting language is often essential for application software development. There is a significant argument regarding the most suitable PHP MVC such as Codeigniter & Laravel and Phalcon frameworks since not all frameworks cater to everyone's needs. It's a fact that not all MVC frameworks are created equal and different frameworks can be combined for specific scenarios. Selecting the appropriate MVC framework can pose a challenge at times. In this context, our paper focuses on conducting a comparative analysis of different PHP frameworks. The widely used PHP MVC frameworks are picked to compare the performance on basic Operation of Relational databases and different type of Application software to calculate execution time. In this experiment a large (Big Data) dataset was used. The Mean values of insert operation in MySQL database of Codeigniter, Laravel, Phalcon were 149.64, 149.99, 145.48 and PostgreSQL database`s 48.259, 49.39, 45.87 respectively. The Mean values of Update operation in MySQL database of Codeigniter, Laravel, Phalcon were 149.64, 158.39, 207.82 and PostgreSQL database`s 48.24, 49.39, 46.64 respectively. The Mean values of Select operation in MySQL database of Codeigniter, Laravel, Phalcon were 1.60, 3.23, 0.98 and PostgreSQL database`s 1.95, 4.57, 2.36 respectively. The Mean values of Delete operation in MySQL database of Codeigniter, Laravel, Phalcon were 150.27, 156.99, 149.63 and PostgreSQL database`s 42.95, 48.25, 42.07 respectively. The findings from our experiment can be advantageous for web application developers to choose proper MVC frameworks with their integrated development environment (IDE). This result will be helpful for small, medium & large-scale organization in choosing the appropriate PHP Framework.
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Comparative Weka Analysis of Clustering Algorithm's
Статья научная
Data mining is a procedure of mining or obtaining a pertinent volume of data or information making the data available for understanding and processing. Data analysis is a common method across various areas like computer science, biology, telecommunication industry and retail industry. Data mining encompass various algorithms viz. association rule mining, classification algorithm, clustering algorithms. This survey concentrates on clustering algorithms and their comparison using WEKA tool. Clustering is the splitting of a large dataset into clusters or groups following two criteria ie. High intra-class similarity and low inter-class similarity. Every cluster or group must contain one data item and every data item must be in one cluster. Clustering is an unsupervised technique that is fairly applicable on large datasets with a large number of attributes. It is a data modelling technique that gives a concise view of data. This survey tends to explain all the clustering algorithms and their variant analysis using WEKA tool on various datasets.
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