IAES journal

    Higuchi Fractal Dimension Analysis of EEG Signal Before and After OM Chanting to Observe Overall Effect on Brain

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    The OM chanting is one type of the meditation. In the present paper, the author tried to observe its effect on the brain. To obtain insight of the brain, the author recorded EEG signal before and after OM chanting for 10 subjects. Author used a technique of the complexity measure based on fractal analysis to compare the EEG signal before and after OM chanting. Time domain fractal dimension was calculated using Higuchi algorithm. (HFD).Paper present the results based on average HFD all over the electrodes for each subject before and after OM chanting.DOI:http://dx.doi.org/10.11591/ijece.v4i4.580

    Graph-Based Concept Clustering for Web Search Results

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    A search engine usually returns a long list of web search results corresponding to a query from the user. Users must spend a lot of time for browsing and navigating the search results for the relevant results. Many research works applied the text clustering techniques, called web search results clustering, to handle the problem. Unfortunately, search result document returned from search engine is a very short text. It is difficult to cluster related documents into the same group because a short document has low informative content. In this paper, we proposed a method to cluster the web search results with high clustering quality using graph-based clustering with concept which extract from the external knowledge source. The main idea is to expand the original search results with some related concept terms. We applied the Wikipedia as the external knowledge source for concept extraction. We compared the clustering results of our proposed method with two well-known search results clustering techniques, Suffix Tree Clustering and Lingo. The experimental results showed that our proposed method significantly outperforms over the well-known clustering techniques

    Gender Classification Using Hybrid of Gabor Filters and Binary Features of an Image

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    Face is one of the most important biometric of human and contains lots of useful information such as gender, age, race and identity. Gender classification is very easy for human but it considers a challenge for computers. Gender classification through face images has recently been considered so much. Gender recognition can be useful in interaction between human and computer like identifying individual’s identity. It is also applicable in TV networks in order to study the rate of viewers. Various algorithms have been designed for this issue and each of them has unraveled that to some extent. The last obtained rate to identify gender was through article written by Dr. Mozaffari who obtained mean rate of 83% for identification. It is the proposed method of the present study which has brought identification rate to 92.5. in this method we draw out face features based on Gabor filters and local binary patterns. These features are resistant against noise and they select proper features against bottleneck of images. In order to obtain a proper classification, we use self-organized map (SOM) (type of artificial neural network). This neural network finds the proper weights for each gender with very little error. Obtained results are compared with existing datasets and therefore, superiority of the proposed method would be evident.DOI:http://dx.doi.org/10.11591/ijece.v4i4.592

    Design and Optimization of a High Gain Multiband Patch Antenna for Millimeter Wave Applications

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    This paper presents an enhanced Quadri-band microstrip patch antenna, using defective slots in the ground plane, designed to operate in the millimeter wave band, formulated using cavity model and simulated by an EM-simulator, based on finite element method: HFSSv15 (High Frequency Structure Simulator). The proposed antenna incorporates two symmetric patterns of “U” shaped slots with an “I” shaped slot engraved in the middle of the ground plane. The resulting antenna has four frequency bands; the first resonant frequency is located in the Ka band, at about 27Ghz, the second at nearly 35Ghz, the third at 41Ghz and the last one at 51GHz. Those resonant frequencies could be shifted by tuning the slots dimensions introduced if the ground plane of the proposed antenna.

    Query Dependent Ranking for Information Retrieval Based on Query Clustering

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    Ranking is the central problem for information retrieval (IR), and employing machine learning techniques to learn the ranking function is viewed as a promising approach to IR. In information retrieval, the users’ queries often vary a lot from one to another. In this paper we take into account the diversity of query type by clustering the queries. Instead of deriving a single function, this system attempt to develop several ranking functions based on the resulting query clusters in the sense that different queries of the same cluster should have similar characteristics in terms of ranking. Before the queries are clustered, query features are generated based on the average scores of its associated retrieved documents.  So, for each query cluster, there will be its associated ranking model. To rank the documents for a new query, the system first find the most suitable cluster for that query and produce the scoring results depend on that cluster. The effectiveness of the system will be tested on LETOR, publicly available benchmark dataset.DOI: http://dx.doi.org/10.11591/ij-ict.v2i1.150

    Resource Sharing in Libraries on Cloud Landscape: Potentials and Paradoxes

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    The initial hype of any development can eclipse its practical applications. The present paper attempts to separate the hype of cloud computing and explores the possibilities of resource sharing in libraries through Cloud Computing. The present study is based on the usage pattern of cloud computing in libraries and explores a model for resource sharing. It also proposes a cloud based resource sharing framework for moving from ground to the cloud. Although a lot of work has been done on cloud computing yet none of the works has been dedicated to the cause of libraries. With the development of electronic resources and shift in user’s preference of online services there is an urgent need of change in library practices

    Node-based Sampling P2P Bot Detection

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    The concept of using node-based sampling for the treatment of packet capture mechanism based on Libpcap of network-based detecting Peer-to-Peer botnet process was tested, and its effect on the time window of feature extracting and sampling time interval was explored. Node-based sampling treatment resulted in significant increase in the detection performance due to node profile of the novel behaviors to the detected computer in Peer-to-Peer bot detection, and the degradation of false positive. At relatively right time window (e.g., about 180s), precision was completely maximized, while the false positive decreased by 10% to 15%. The detection rate can be significantly increased due to the false positive degradation. A new performance index called Comprehensive Evaluation Index is proposed for more clearly represent the effectiveness. Sampling can reduce morn than 60% input raw packet traces and achieve a high detection rate (about 99%) and a low false positive rates (0-2%). DOI: http://dx.doi.org/10.11591/telkomnika.v10i5.127

    Data in Transit Validation for Cloud Computing Using Cloud-Based Algorithm Detection of Injected Objects

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    The recent paradigm shift in the IT sector leading to cloud computing however innovative had brought along numerous data security concerns. One major such security laps is that referred to as the Man in the Middle (MITM) attack where external data are injected to either hijack a data in transit or to manipulate the files and object by posing as a floating cloud base. Fresh algorithms’ for cloud data protection do exist however, they are still prone to attack especially in real-time data transmissions due to employed mechanism. Hence, a validation protocol algorithm based on hash function labelling provides a one-time security header for transferable files that protects data in transit against any unauthorized injection. The labelling header technique allows for a two-way data binding; DOM based communication between local and cloud computing that triggers automated acknowledgment immediately after file modification. A two layer encryption functions in PHP was designed for detecting injected object; bcrypt methods in Laravel and MD5 that generate 32 random keys. A sum total of 1600 different file types were used during training then evaluation of the proposed algorithm, where 87% of the injected objects were correctly detected

    An Interactive Mobile Augmented Reality for Tourism Objects at Purbalingga District

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    This paper presents the development of an interactive mobile Augmented Reality (AR) for tourism promotion with eXtreme programming (XP) at Purbalingga district, Central Java that has many places of tourist attractions such as Owabong, Purbasari Pancuran Mas, Sanggaluri Park and Buper Munjuluhur. By applying the AR concept it is expected the tourism objects could be enhanced by augmenting the virtual brochures which could be viewed over a mobile device. In this study, mobile device Android platform is used to display interactive brochures of tourism promotion containing 3D models, animations, and sounds. The brochure will provide information in of real attractions of the tourism objects in the Purbalingga district

    Learning Based Route Management in Mobile Ad-Hoc Networks

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    Ad hoc networks are mobile wireless networks where each node is acting as a router. The existing routing protocols such as Destination sequences distance vector, Optimized list state routing protocols, Ad hoc on demand routing protocol, dynamic source routing are optimized versions of distance vector or link state routing protocols.  Reinforcement Learning is new method evolved recently which is learning from interaction with an environment. Q Learning which is based on Reinforcement learning that learns from the delayed reinforcements and becomes more popular in areas of networking. Q Learning is applied  to the routing algorithms where the routing tables in the distance vector algorithms are replaced by the estimation tables called as Q values. These Q values are based on the link delay. In this paper, various optimization techniques over Q routing are described in detail with their algorithms
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