- Все статьи 584
Статьи журнала - International Journal of Information Engineering and Electronic Business
Все статьи: 584
Aesthetic QR: Approaches for Beautified, Fast Decoding, and Secured QR Codes
Статья научная
A QR code is a two-dimensional code that encodes data but it is unattractive and not ideal. QR codes have been applied in item identifications, publicity campaigns, advertisements, product promotions, etc. so they need to be visually good in appearance. Visually good and decorated QR codes degrade the decoding rate as compared to the standard QR code decoding rate. As they are used for mobile payments and logins some security must be there. For this many researchers have contributed using various approaches to beautify QR codes with high decoding accuracy and to make them secure. This paper aims towards the study of works carried out in the direction of beautification of QR codes using blended type techniques and artificial intelligence based techniques by different authors. The present state of prior strategies, methods, and major features used are described in this survey.
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Amplification-based Attack Models for Discontinuance of Conventional Network Transmissions
Статья научная
Amplification attacks take advantage of insecurity of different OSI layers. By targeting broadcast address of the victim networks and sending a few packets by the attackers, they force the legitimate user in the victim networks to response to these packets and attack their own trusted networks unknowingly. Despite importance of amplification attacks, there is not any work to implement these attacks to identify their procedure and quantify and compare their impacts on the networks. In this work, we use NS2 to achieve these goals. A variety range of scenarios are designed to implement DDoS amplification attacks and collect the results in terms of different network performance measures. The quantitative results prove devastating impact of the attacks which are easily capable of rendering the target wireless networks disable for their legitimate users.
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Статья научная
Feature size reduction in microelectronic circuits has been an important contributing factor to the dramatic increase in the processing power of computer arithmetic circuits. However, it is generally accepted that MOS based circuits cannot be reduced further in feature size due to fundamental physical restrictions. Therefore, several emerging technologies are currently being investigated. Nano devices offer greater scaling potential than MOS as well as ultra low power consumption. Nano devices display a switching behaviour that differs from traditional MOS devices. This provides new possibilities and challenges for implementing digital circuits using different techniques like CNTFET,SET, FinFET etc. In this work the design of Inverter and Nand gate using CNT, SET and FinFET has been analyzed elaborately with its own advantageous of the mentioned techniques.
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An Analysis of Fuzzy and Spatial Methods for Edge Detection
Статья научная
An image segmentation is an area in which image is subdivided into sub-regions for extracting characteristics of images which will help to analysis in various applications. For getting accuracy sharp changes of intensity is an important issue which is known as edge detection. In this paper various spatial edge detection methods and fuzzy based edge detection method has described and spatial edge detection methods and fuzzy if-then-else are compared to know which method will be more suitable to find edges for the enhancement of images.
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An Analysis of RDF Storage Models and Query Optimization Techniques
Статья научная
The Web provides access to substantial amount of information. Metadata that means data about data enables the discovery of such information. When the metadata is effectively used, it increases the usefulness of the original data/resource and facilitates the resource discovery. Resource Description Framework (RDF) is a basis for handling these metadata and is a graph-based, self-describing data format that represents information about web-based resources. It is necessary to store the data persistently for many Semantic Web applications that were developed on RDF to perform effective queries. Because of the difficulty of storing and querying RDF data, several storage techniques have been proposed for these tasks. In this paper, we present the motivations for using the RDF data model. Several storage techniques are discussed along with the methods for optimizing the queries for RDF datasets. We present the differences between the Relational Database and the XML technology. Additionally, we specify some of the use cases for RDF. Our findings will shed light on the current achievements in RDF research by comparing the different methodologies for storage and optimization proposed so far, thus identifying further research areas.
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An Android Based Automated Tool for Performance Evaluation of a Course Teacher (CTE)
Статья научная
For the betterment of teaching methodology student’s evaluation is an integral part of any educational organization. To achieve this process the authority needs to know how the teachers are teaching and therefore the interaction between the learners and therefore educators. This paper develops an android based automated tool for performance evaluation of a course teacher (CTE) which is able to create an educator’s performance report from the student’s evaluation based on some predefined questionnaire by using an android mobile device with internet connectivity from anywhere and anytime. The performance report is auto generated together with a graph and it also creates a file to send the teacher if the authority wants to inform the educator. With the assistance of this technique, course teachers can easily understand their current situation of their corresponding courses where they should focus on.
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Статья научная
The present day users navigate more using electronic gadgets, interacting with social networking sites and retrieving the images of interest from the information groups or similar groups. Most of the retrievals techniques are not much effective due to the semantic gap. Many models have been discussed for effective retrievals of the images based on feature extraction, label based and semantic rules. However effective retrievals of images are still a challenging task, model based techniques together with semantic attributes provide alternatives for efficient retrievals. This article is developed with the concepts of Generalized Gaussian Mixture Models and Semantic attributes. Flicker dataset is considered to experiment the model and efficiency is measured using Precision and Recall.
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An Approach for Similarity Matching and Comparison in Content based Image Retrieval System
Статья научная
Today, in the age of images and digitization relevant retrieval is quite a topic of research. In past era, the database was having only text or database was low dimensional type. But with every new day thousands of pictures are getting added into the database making it a high dimensional data set. Therefore, from a high dimensional dataset to get a set of relevant images is quite a cumbersome task. Number of approaches for getting relevant retrieval is defined, some includes retrievals only on the basis of color, while some include more than one primitive feature to retrieve the relevant image such as color, shape and texture. In this paper experiment has been performed on the trademark images. Trademark is a very important asset for any organization and increasing trademark images have developed a quick need to organize these images. This paper includes the implementation of HSV model for fast retrieval. Which use color and texture so as to extract feature vector. Experiment takes query image and retrieve twelve most relevant images to the user. Further for performance evaluation parameter used is Precision and Recall.
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Статья научная
The enormous growth of smartphones was impelled by the idea to make a mobile phone offer more than just cellular telephony. One of the prime factors that initiated the age of smartphones (e.g. iOS, Android, RIM, etc.) was inarguably the capability of wireless sharing of images, music etc. among the users; which was possible due to Bluetooth Technology (IEEE 802.15). Today customers of the cheapest phone in world demand to have an inbuilt Bluetooth stack. Apart from sharing files, especially media, Bluetooth provides us with a lot more functionality, like streaming audio to a home entertainment system, allowing to share an Internet connection over DUN profile, a remote car locking and security system, a few to mention. Though the IEEE 802.15 stack has its own security mechanism, sometimes a system might require an additional security architecture running collaboratively with the in-built security to authorize an inbound pairing request. A simple example of the authorization paradox is that the standard security mechanism cannot help a Bluetooth system that was paired to multiple devices, to decide which of the paired devices to authorize to execute a certain task. For example, a device may be required to allow a smartphone Bluetooth stack to stream audio but restrict it from transferring files. Here need of a profile specific authorization is felt but it is beyond the scope of IEEE 802.15. To understand it better, let us assume that a home theater system has a Bluetooth link which allows smart phones to stream audio to it over A2D Audio sharing profile. Such a home theater system (e.g. HT-DZ350 by Sony) can be connected to any smartphone and play the streamed music. Each time a device disconnects, the Bluetooth stack resets itself and identity of the Bluetooth stack on the smartphone is lost. Since Bluetooth radio waves can penetrate walls and windows, it may be possible that a neighbor of mine connected her smartphone to the Home theater system and played an unwanted music. Sometimes this can be fatal in some remote controlled instruments unless proper security mechanisms are installed. Proposed in this thesis is a novel, generic and extensible framework to prevent unauthorized access over Bluetooth serial port profile; which is independent of any Cryptographic algorithm or approach. The thesis also suggests different architectures for differently equipped hardware systems, because the performance of the system under an augmented security stack will be different for different devices with varying hardware resources.
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An E-mail Spam Detection using Stacking and Voting Classification Methodologies
Статья научная
Nowadays, we use emails almost in every field; there is not a single day, hour, or minute when emails are not used by people worldwide. Emails can be categorized into two types: ham and spam. Hams are useful emails, while spam is junk or unwanted emails. Spam emails may carry some unwanted, harmful information or viruses with them, which might harm user privacy. Spam mails are used to harm people by wasting their time and energy and stealing valuable information. Due to increasing in spam emails rapidly, spam detection and filtering are the prominent problems that need to be solved. This paper discusses various machine learning models like Naïve Bayes, Support Vector Machine, Decision Tree, Extra Decision Tree, Linear regression., and surveys about these machine learning techniques for email spam detection in terms of their accuracy and precision. In this paper, a comprehensive comparison of these techniques and stacking of different algorithms is also made based on their speed, accuracy, and precision performance.
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An Efficient Architecture and Algorithm for Resource Provisioning in Fog Computing
Статья научная
Cloud computing is a model of sharing computing resources over any communication network by using virtualization. Virtualization allows a server to be sliced in virtual machines. Each virtual machine has its own operating system/applications that rapidly adjust resource allocation. Cloud computing offers many benefits, one of them is elastic resource allocation. To fulfill the requirements of clients, cloud environment should be flexible in nature and can be achieve by efficient resource allocation. Resource allocation is the process of assigning available resources to clients over the internet and plays vital role in Infrastructure-as-a-Service (IaaS) model of cloud computing. Elastic resource allocation is required to optimize the allocation of resources, minimizing the response time and maximizing the throughput to improve the performance of cloud computing. Sufficient solutions have been proposed for cloud computing to improve the performance but for fog computing still efficient solution have to be found. Fog computing is the virtualized intermediate layer between clients and cloud. It is a highly virtualized technology which is similar to cloud and provide data, computation, storage, and networking services between end users and cloud servers. This paper presents an efficient architecture and algorithm for resources provisioning in fog computing environment by using virtualization technique.
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Статья научная
Feature selection is an indispensable pre-processing technique for selecting more relevant features and eradicating the redundant attributes. Finding the more relevant features for the target is an essential activity to improve the predictive accuracy of the learning algorithms because more irrelevant features in the original feature space will cause more classification errors and consume more time for learning. Many methods have been proposed for feature relevance analysis but no work has been done using Bayes Theorem and Self Information. Thus this paper has been initiated to introduce a novel integrated approach for feature weighting using the measures viz., Bayes Theorem and Self Information and picks the high weighted attributes as the more relevant features using Sequential Forward Selection. The main objective of introducing this approach is to enhance the predictive accuracy of the Naive Bayesian Classifier.
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An Efficient Smote-based Model for Dyslexia Prediction
Статья научная
Dyslexia is a learning disability which causes difficulty in an individual to read, write and spell and do simple mathematical calculations. It affects almost 10% of the global population and detecting it early is paramount for its effective handling. There are many different methods to detect the risk of Dyslexia. Some of these methods are using assessment tools, handwriting recognition, expert psychological help and also using the eye movement data recorded while reading. One of the other convenient and easy ways of detecting risk of dyslexia is to make an individual participate in a simple game related to phonological awareness, syllabic awareness, auditory discrimination, lexical awareness, visual working memory, and many more and recording the observations. The proposed research work presents an effective way of predicing the risk of dyslexia with high accuracy and reliability. It uses a dataset made available from the kaggle repository to predict the risk of dyslexia using various machine learning algorithms. Also it is observed that the dataset has an unequal distribution of positive and negative cases and so the classification accuracy is compromised if used directly. The proposed research work uses three resampling techniques to reduce the imbalance in the dataset. The resampling techniques used are undersampling using near-miss algorithm, oversampling using SMOTE and ADASYN. After applying the undersampling near-miss algorithm, best accuracy was given by SVC classifier with the value of 81.63%. All the other classifiers used in the experiment produced accuracy in the range of 64% to 79.08%. After using the oversampling algorithm SMOTE, the classifiers produced very good results in the evaluation metrics of accuracy,CV score, F1 Score and recall. The maximum accuracy was given by RandomForest with a value of 96.37% and closely followed by XGBBoosting and GradientBoosting with an accuracy of 95.14%. Decision tree, SVC and ADABoost got an accuracy of 91.26%, 93.36% and 93.48% respectively. Even the values of CV score, F1 and recall were considerably high for all these classifiers. After applying the oversampling technique of ADASYN, RandomForest algorithm generated maximum accuracy of 96.25%. Between the two oversampling techniques, SMOTE algorithm performed slightly better in producing better evaluation metrics than ADASYN. The proposed system has very high reliability and so can be effectively used for detecting the risk of dyslexia.
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Статья научная
In this article, we propose a system with RFID and sensor networks to guarantee the keeping quality of low-temperature logistics. 3G network and GPS transportations are incorporated to this system to create a full-time monitoring system and all the processing will be transparent to the customers so that it could be a keeping-quality guarantee to the customers as well as a good strategy for differentiated marketing. This system can be connected with the customers’ system so that all the processing, storage, and transportation temperatures can be sent to the customers through extranet and WEB server of suppliers. The customers’ system can judge whether temperature data is normal or not before the foods receive. Owing to the fact that all the RFID tags and readers can be reused and not expensive at all, this system is very practical to be applied in the cold-chain logistics. A format of data exchange needs to be standardized in the future for broad applications.
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Статья научная
Remarkable growth in the electronics and communication field provides ubiquitous services. It also permits to save huge amount of documents on web. As a result, it is very difficult to search a specific and desired information over the Internet. Classical search engines were unable to investigate the content on web intelligently. The tradition searching results has a lot of immaterial information along with desired one as per user query. To overcome from stated problem many modifications are done in traditional search engines to make them intelligent. These search engines are able to analyze the stored data and reflects only appropriate contents as per users query. Semantic Web is an emerging and efficient approach to handle the searching queries. It gathers appropriate information from web pool based on logical reasoning. It also incorporates rule-based system. Semantic web reasonably scrutinizes webs contents using ontology. The learning process of ontology not only intelligently analyze the contents on web but also improves scrutinizing process of search engine. The paper suggests a new keyword-based semantic retrieval scheme for google search engines. The schemes accelerates the performance of searching process considerably with the help of domain-specific knowledge extraction process along with inference and rules. For this, in ontology the prefix keywords and its sematic association are pre-stored. The proposed framework accelerates the efficiency of content searching of google search engine without any additional burden of end users.
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An Experimental Study of K* Algorithm
Статья научная
Machine Learning techniques are taking place in all areas of our lives, to help us to make decisions. There is a large number of algorithms available for multiple purposes and appropriate for specific data types. That is why it is required to pay special attention to decide which is the recommended technique, to use in each case. K Star is an instance-based learner that tries to improve its performance for dealing with missing values, smoothness problems and both real and symbolic valued attributes; but it is not known much information about how the way it faces attribute and class noisy, and with mixed values of the attributes in the datasets. In this paper we made six experiments with Weka, to compare K Star and other important algorithms: Naïve Bayes, C4.5, Support Vector Machines and k-Nearest Neighbors, taking into account its performance classifying datasets with those features. As a result, K Star demonstrated to be the best of them in dealing with noisy attributes and with imbalanced attributes.
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An Image Steganography-based Novel Approach to develop 8-Share Integrated Security Toolkit (ISTI-8)
Статья научная
Encryption is a process or algorithm to make information hidden or secret and considered as a subset of cryptography. Using encryption data are being transformed into some another form that appears to be meaningless and incomprehensible. Here we have embedded encrypted data bits about the entire image to some suitable nonlinear pixel positions using key. After that we have formed several shares of image and key using R, G and B components and character or digits respectively. At the decryption end through appropriate arrangement of shares of image and key, make possible to retrieve hidden data bits from stego-image and reform into its original content.
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An Improved Framework of Healthcare Supports System for the Treatment of Dementia Cases
Статья научная
This research proposes an improved framework that would support the healthcare services and attention given to dementia patients. The paper shows the design and implementation of a web-based application that demonstrates the proposed framework. This study was necessitated by the observed flaws and weaknesses in the current manual technique of handling dementia cases in care homes which are plagued with loss of records, time wastage in retrieving records, data insecurity, user entry and data management errors, among others. The system design was realized using the unified modeling language (UML) on EdrawMax. The frontend implementation was done using HTML5, CSS3, and JavaScript, while the business logic was achieved using PHP, and the Database was designed with MySQL and managed through PHPMyAdmin. The system was tested by medical practitioners and dementia patients in a select care home. Other tests on browsers’ compatibility and platform interoperability were successful. The result of the study advances technical knowledge in developing medical expert systems using web 2.0 technologies, and promotes academic inquiry in the domain. The demonstration of the framework shows an improvement on the existing techniques which use quasi-automated approach. The proposed model is suitable for supporting efficient management of data of dementia patients.
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An Improved Information Retrieval Approach to Short Text Classification
Статья научная
Twitter act as a most important medium of communication and information sharing. As tweets do not provide sufficient word occurrences i.e. of 140 characters limits, classification methods that use traditional approaches like “Bag-Of-Words” have limitations. The proposed system used an intuitive approach to determine the class labels with the set of features. The System can able to classify incoming tweets mainly into three generic categories: News, Movies and Sports. Since these categories are diverse and cover most of the topics that people usually tweet about .Experimental results using the proposed technique outperform the existing models in terms of accuracy.
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An Improved Particle Swarm Optimization for Protein Folding Prediction
Статья научная
In this paper, we combine particle swarm optimization (PSO) and levy flight to solve the problem of protein folding prediction, which is based on 3D AB off-lattice model. PSO has slow convergence speed and low precision in its late period, so we introduce levy flight into it to improve the precision and enhance the capability of jumping out of the local optima through particle mutation mechanism. Experiments show that the proposed method outperforms other algorithms on the accuracy of calculating the protein sequence energy value, which is turned to be an effective way to analyze protein structure.
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