Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1195
Biometric Verification, Security Concerns and Related Issues - A Comprehensive Study
Статья научная
There has been many attempts to make authentication processes more robust. Biometric techniques are one among them. Biometrics is unique to an individual and hence their usage can overcome most of the issues in conventional authentication process. This paper makes a scrutinizing study of the existing biometric techniques, their usage and limitations pertaining to their deployment in real time cases. It also deals with the motivation behind adapting biometrics in present day scenarios. The paper also makes an attempt to throw light on the technical and security related issues pertaining to biometric systems.
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Body Gestures Recognition System to Control a Service Robot
Статья научная
Personal service robots will be in the short future part of our world by assisting humans in their daily chores. A highly efficient way of communication with people is through basic gestures. In this work, we present an efficient body gestures’ interface that gives the user practical communication to control a personal service robot. The robot can interpret two body gestures of the subject and performs actions related to those gestures. The service robot’s setup consists of a Pioneer P3-DX research robot, a Kinect sensor and a portable workstation. The gesture recognition system developed is based on tracking the skeleton of the user to get the body parts relative 3D positions. In addition, the system takes depth images from the sensor and extracts their Haar features, which will train the Adaboost algorithm to classify the gesture. The system was developed using the ROS framework, showing good performance during experimental evaluation with users. Our body gesture-based interface may serve as a baseline to develop practical and natural interfaces to communicate with service robots in the near future.
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Bug Severity Prediction using Keywords in Imbalanced Learning Environment
Статья научная
Reported bugs of software systems are classified into different severity levels before fixing them. The number of bug reports may not be equally distributed according to the severity levels of bugs. However, most of the severity prediction models developed in the literature assumed that the underlying data distribution is evenly distributed, which may not correct at all instances and hence, the aim of this study is to develop bug classification models from unevenly distributed datasets and tested them accordingly. To that end first, the topics or keywords of developer descriptions of bug reports are extracted using Rapid Keyword Extraction (RAKE) algorithm and then transferred them into numerical attributes, which combined with severity levels constructs datasets. These datasets are used to build classification models; Naïve Bayes, Logistic Regression, and Decision Tree Learner algorithms. The models’ prediction quality is measured using Area Under Recursive Operative Characteristics Curves (AUC) as the models learnt from more skewed environments. According to the results, the prediction quality of the Logistics Regression model is 0.65 AUC whereas the other two models recorded maximum 0.60 AUC. Though the datasets contain comparatively less number of instances from the high severity classes; Blocking and High, the Logistic Regression models predict the two classes with a decent AUC value of 0.65 AUC. Hence, this projects shows that the models can be trained from highly skewed datasets so that the models prediction quality is equally well over all the classes regardless of number of instances representing the class. Further, this project emphasizes that the models should be evaluated using the appropriate metrics when the models are trained from imbalance learning environments. Also, this work uncovers that the Logistic Regression model is also capable of classifying documents as Naïve Bayes, which is well known for this task.
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Building Ontologies for Cross-domain Recommendation on Facial Skin Problem and Related Cosmetics
Статья научная
Nowadays, recommendation has become an everyday activity in the World Wide Web. An increasing amount of work has been published in various areas related to the recommender system. Cross-domain recommendation is an emerging research topic. This type of recommendations has barely been investigated because it is difficult to obtain public datasets with user preferences crossing different domains. To solve dataset problem, one of the solution is to create different domains. Ontology is playing increasingly important roles in many research areas such as semantics interoperability and knowledge base and creating domain. Ontology defines a common vocabulary and a shared understanding and is applied for real world applications. Ontology is a formal representation of a set of concepts within a domain and the relationships between those concepts. This paper presents an approach for building ontologies using Taxonomic conversational case-based reasoning (Taxonomic CCBR) to apply cross-domain recommendation based on facial skin problems and related cosmetics. For linking cross-domain recommendation, Ford-Fulkerson algorithm is used to build the bridge of the concepts between two domain ontologies (Problems domain as the source domain and Cosmetics domain as the target domain).
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Building a template for intuitive virtual e-commerce shopping site in India
Статья научная
With the aim to take forward the digital India mission, it is essential to building a template for intuitive e-commerce shopping site so that users can shop easily, without taking any special training. We have achieved this using several steps. First, we have documented mental model and behavioral patterns of end users while they were interacting with the shopping site. We have mapped existing shopping sites with the mental model, behavioral pattern and as a result, problem themes are identified. Effective procedures are identified to make GUI for the e-commerce shopping sites more intuitive. Based on these procedures, the prototype is designed and validated. Finally, the template for intuitive e-commerce shopping site is formed.
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Building and Annotating a Codeswitched Hate Speech Corpora
Статья научная
Presidential campaign periods are a major trigger event for hate speech on social media in almost every country. A systematic review of previous studies indicates inadequate publicly available annotated datasets and hardly any evidence of theoretical underpinning for the annotation schemes used for hate speech identification. This situation stifles the development of empirically useful data for research, especially in supervised machine learning. This paper describes the methodology that was used to develop a multidimensional hate speech framework based on the duplex theory of hate [1] components that include distance, passion, commitment to hate, and hate as a story. Subsequently, an annotation scheme based on the framework was used to annotate a random sample of ~51k tweets from ~400k tweets that were collected during the August and October 2017 presidential campaign period in Kenya. This resulted in a gold-standard codeswitched dataset that could be used for comparative and empirical studies in supervised machine learning. The resulting classifiers trained on this dataset could be used to provide real-time monitoring of hate speech spikes on social media and inform data-driven decision-making by relevant security agencies in government.
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Building secure web-applications using threat model
Статья научная
Ensuring security in web based applications is one of the key issues nowadays. The processes of designing and building a web site have changed. As the online transactions are increasing, increase in type and number of attacks have been observed regarding security of online payment systems. Generally used web development methodologies do not assure security as an umbrella activity. Moreover appropriate threat modeling is also not being conducted against web security objectives. Need of the hour is to have a comprehensive and simple to use web development methodology which caters security throughout the WDLC for web based solutions.
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Статья научная
The document illustrates the methods and algorithms for calculation of overvoltage at the power transformer excerpts during the activation of inductive consumer to secondary transformer. Characteristic stages are activation of the primary phase and followed by two other phases. The new specific condition occurs during the activation of each phase which is presented by alternative electric circuit and simplified equivalent scheme that is used to calculate the values and evaluate overvoltage. For selected parameters of the transformer and inductive loads, the simulation is performed with chosen MATLAB software package.
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Capacity Enhancement by Using a Multi-User Detector on Uplink Synchronous Mode
Статья научная
The Uplink Synchronous Transmission Scheme [1], is a technique used by operators, that exploit the uplink orthogonality, to reduce multiple access interferences in uplink direction and consequently to increase uplink capacity. The USTS gives better performances when we have an ideal case presented by no channelization code restrictions per scrambling code. In reality, channelization codes are limited. To resolve this problem, several scrambling codes are used to admit more users in the cell. However, the use of different scrambling codes increases the multiple access interference and consequently decreases uplink capacity gain, since signals transmitted by users under different scrambling codes are non-orthogonal. To obtain more performances and therefore to increase the uplink capacity gain, we will study the introduction of a multi-user detector for interferences cancellation, in uplink synchronous mode. For that, two values of interference cancellation efficiency of the multi-user detector are considered. In this study, only the multiple access interference is reduced. To show the effect of other-cell interferences on uplink synchronous mode capacity, two scenarios are considered: an isolated cell and a multiple cell network.
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Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning
Статья научная
A sizeable number of women face difficulties during pregnancy, which eventually can lead the fetus towards serious health problems. However, early detection of these risks can save both the invaluable life of infants and mothers. Cardiotocography (CTG) data provides sophisticated information by monitoring the heart rate signal of the fetus, is used to predict the potential risks of fetal wellbeing and for making clinical conclusions. This paper proposed to analyze the antepartum CTG data (available on UCI Machine Learning Repository) and develop an efficient tree-based ensemble learning (EL) classifier model to predict fetal health status. In this study, EL considers the Stacking approach, and a concise overview of this approach is discussed and developed accordingly. The study also endeavors to apply distinct machine learning algorithmic techniques on the CTG dataset and determine their performances. The Stacking EL technique, in this paper, involves four tree-based machine learning algorithms, namely, Random Forest classifier, Decision Tree classifier, Extra Trees classifier, and Deep Forest classifier as base learners. The CTG dataset contains 21 features, but only 10 most important features are selected from the dataset with the Chi-square method for this experiment, and then the features are normalized with Min-Max scaling. Following that, Grid Search is applied for tuning the hyperparameters of the base algorithms. Subsequently, 10-folds cross validation is performed to select the meta learner of the EL classifier model. However, a comparative model assessment is made between the individual base learning algorithms and the EL classifier model; and the finding depicts EL classifiers’ superiority in fetal health risks prediction with securing the accuracy of about 96.05%. Eventually, this study concludes that the Stacking EL approach can be a substantial paradigm in machine learning studies to improve models’ accuracy and reduce the error rate.
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Career Guidance through Multilevel Expert System Using Data Mining Technique
Статья научная
In this paper, the author provides a framework for Multilevel Expert System to advice scholars for their future career. The proposed framework aims at providing information to decide the career paths for the academics. The emerging fields of Expert System, Education, and Data Mining are speedily providing new possibilities for collecting, analyzing and guiding the scholars in their careers. Many scholars suffer from taking a right career decision, only a few scholars took the right decision about their careers. A poor career decision of scholars may push his whole life in the dark. Nowadays selecting a right career becomes very difficult for the scholars. Among the works reported in this field, we concentrate only Experts Systems that deal with scholar's career selection problem through Data Mining technique.
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Статья научная
Correlation between gene expression profiles to disease or different developmental stages of a cell through microarray data and its analysis has been a great deal in molecular biology. As the microarray data have thousands of genes and very few sample, thus efficient feature extraction and computational method development is necessary for the analysis. In this paper we have proposed an effective feature extraction method based on factor analysis (FA) with discrete wavelet transform (DWT) to detect informative genes. Radial basis function neural network (RBFNN) classifier is used to efficiently predict the sample class which has a low complexity than other classifier. The potential of the proposed approach is evaluated through an exhaustive study by many benchmark datasets. The experimental results show that the proposed method can be a useful approach for cancer classification.
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Case-Based Reasoning Framework for Malaria Diagnosis
Статья научная
Malaria is life threatening disease in Ethiopia specifically in Tigray region. Having common symptoms with other diseases makes it complex and challenging to diagnose effectively. In this paper case based reasoning framework for malaria diagnosis has been designed to diminish the challenges faced by inexperienced practitioners during malaria diagnosis and to solve the problem on shortage of health professionals. The required knowledge for this study was collected through interview and document analysis from domain experts, malaria patient history cards and other related relevant documents. In the case acquisition process the manual format of cases makes the process too challenging. Decision tree is used to model the acquired knowledge. The case structure was then constructed using the selected most determinant attributes. Machine learning approach is applied to select the most relevant features. Feature-vector case representation technique is applied to represent the collected malaria cases. Jcolibri programming tool integrated with Eclipse and Nearest Neighbor retrieval algorithm are used to design the framework. To the end based on the results we can say that the machine learning approach can be used to select most relevant attributes in diseases having several common symptoms and designing case-based diagnosis frameworks could overcome the main problems observed in health centers of Tigray. As an artifact the framework is evaluated by statistical analysis, comparative evaluation, user evaluation and other evaluation techniques. Averagely 79 % precision, 89 % recall, 91.4% accuracy and 78.8% domain expert’s evaluation was the results scored.
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Статья научная
This paper presents the latency and potential of central nervous system based system intelligent computer engineering system for detecting shelf life of soft mouth melting milk cakes stored at 10o C. Soft mouth melting milk cakes are exquisite sweetmeat cuisine made out of heat and acid thickened solidified sweetened milk. In today’s highly competitive market consumers look for good quality food products. Shelf life is a good and accurate indicator to the food quality and safety. To achieve good quality of food products, detection of shelf life is important. Central nervous system based intelligent computing model was developed which detected 19.82 days shelf life, as against 21 days experimental shelf life.
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Challenges of Airline Reservation System and Possible Solutions (A Case Study of Overland Airways)
Статья научная
An Airline Reservation system is very important because it has the strong ability to reduce errors that might have occurred when using a manual system of reservation and helps speed up the boarding process. Overland Airways has an existing Airline Reservation System, but this paper analyzed the problems of the existing system. The problems are: inability of passengers to select their preferred seat(s) from the reservation system, No option of passengers printing their boarding pass from the existing system, non-notification of passengers of flight cancellation or delays and passengers don't have access to aircraft maintenance report to ease the fears associated with air travel and its disasters. In this paper, an Improved Airline Reservation System that is convenient for passengers to solve the aforementioned problems was designed. The Improved Airline Reservation system is designed and implemented using data obtained from interviewing airline personnel, passengers, and materials on Airline Reservation Systems. In this regard, the Improved Airline Reservation System will assist Overland Airways in variety of airline administration tasks and service needs from time of initial reservation through completion of the task. The following programming languages were used: PHP, JavaScript, HTML and CSS for designing the interface of the system, and SQL for the database. The designed airline system was tested with 50 passengers.
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Chaotic Dynamics of Complex Logistic Map in I-Superior Orbit
Статья научная
Recently, the logistic map is studied to analyse the impact on the chaotic dynamics of various iterated logistic maps using Picard, Mann, and many more. The purpose of this paper is to explore the behavior of a multi-scale population model, i.e. modified logistic map (Mod-LM) and chosen population proportion model, i.e. extended logistic map (Ex-LM) in an I-superior orbit using a bifurcation diagram. The additional parameters of Mod-LM and Ex-LM with the three-step iteration system, increase the degree of freedom which invariably enhances the stability of both the functions. A detailed study of possible scenarios has been conducted to discover the effect of each parameter to the fixed point and its location, periodic cycle, and stability condition by examining the corresponding bifurcation diagram. The experimental result is discussed in terms of convergence point and chaotic range of the given dynamical systems.
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Chaotic Firefly Algorithm for Solving Definite Integral
Статья научная
In this paper, an Improved Firefly Algorithm with Chaos (IFCH) is presented for solving definite integral. The IFCH satisfies the question of parallel calculating numerical integration in engineering and those segmentation points are adaptive. Several numerical simulation results show that the algorithm offers an efficient way to calculate the numerical value of definite integrals, and has a high convergence rate, high accuracy and robustness.
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Статья научная
Meditation is a practice of concentrated focus upon the breath in order to still the mind. In this paper we have investigated an algorithm to classify rest and meditation, by processing of electroencephalogram (EEG) signals through the Wavelet and nonlinear methods. For this purpose, two types of EEG time series (before, and during meditation) of 25 healthy women are collected in the meditation clinic in Mashhad. Correlation dimension and Wavelet coefficients at the forth decomposition level of EEG signals in Fz, Cz and Pz are extracted and used as an input of different classifiers. In order to evaluate performance of the classifiers, the classification accuracies and mean square error (MSE) of the classifiers were examined. The results show that the Fisher discriminant and Parzen classifier trained on both composite features obtain higher accuracy than that of the others. The total classification accuracy of the Fisher discriminant and Parzen classifier applying Wavelet coefficients was 85.02% and 84.75%, respectively which is raised to 92.37% in both classifiers using Correlation dimensions.
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Classification of Leaf Disease Using Global and Local Features
Статья научная
Leaf disease of plants causes great loss in productivity of crops. So proper take care of plants is mandatory. Plants can be affected by various diseases. So Early diagnosis of leaf disease is a good practice. Computer vision-based classification of leaf disease can be a great way in diagnosing diseases early. Early detection of diseases can lead to better treatment. Vision based technology can identify disease quickly. Though deep learning is trending and using vastly for recognition task, but it needs very large dataset and also consumes much time. This paper introduced a method to classify leaf diseases using Gist and LBP (Local Binary Pattern) feature. These manual feature extraction process need less time. Combination of gist and LBP features shows significant result in classification of leaf diseases. Gist is used as global feature and LBP as local feature. Gist can describe an image very well as a scene. LBP is robust to illumination changes and occlusions and computationally simple. Various diseases of different plants are considered in this study. Gist and LBP features from images are extracted separately. Images are pre-processed before feature extraction. Then both feature matrix is combined using concatenation method. Training and testing is done on different plants separately. Different machine learning model is applied on the feature vector. Result from different machine learning algorithms is also compared. SVM performs better in classifying plant's leaf dataset.
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Classification of SAR Images Based on Entropy
Статья научная
SAR image classification is the progression of separating or grouping an image into different parts. The good feat of recognition algorithms based on the quality of classified image. The good recital of recognition algorithms depend on the quality of classified image. The proposed classification method is hierarchical: classes which are difficult to distinguish are grouped.An important problem in SAR image application is accurate classification. In this paper, we developed a new methodology of SAR image Classification by Entropy. The severance between different groups or classes is based on logistic and multi-nominal regression, which finds the best combination of features to make the separation and at the same time perform a feature selection depending on Grouped –Entropy value.
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