• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1886-1898
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• 2015

In today’s era of advanced technological developments, the threats to the authenticity and integrity of digital images, in a nutshell, the threats to the Image Forensics Research communities have also increased proportionately. This happened as even for the ‘non-expert’ forgers, the availability of image processing tools has become a cakewalk. This image forgery poses a great problem for judicial authorities in any context of trade and commerce. Block matching based image cloning detection system is widely researched over the last 2-3 decades but this was discouraged by higher computational complexity and more time requirement at the algorithm level. Thus, for reducing time need, various dimension reduction techniques have been employed. Since a single technique cannot cope up with all the transformations like addition of noise, blurring, intensity variation, etc. we employ multiple techniques to a single image. In this paper, we have used Fuzzy logic approach for decision making and getting a global response of all the techniques, since their individual outputs depend on various parameters. Experimental results have given enthusiastic elicitations as regards various transformations to the digital image. Hence this paper proposes Fuzzy based cloning detection and classification system. Experimental results have shown that our detection system achieves classification accuracy of 94.12%. Detection accuracy (DAR) while in case of 81×81 sized copied portion the maximum accuracy achieved is 99.17% as regards subjection to transformations like Blurring, Intensity Variation and Gaussian Noise Addition.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.199-202
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• 1996

Although numerous studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective to solve a specific problem. Alternatively, it has been suggested that a better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the prediction performance. This paper proposes the post-model integration method, which means integration is performed after individual techniques produce their own outputs, by finding the best combination of the results of each method. To get the optimal or near optimal combination of different prediction techniques. Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an objective function subject to numerous hard and soft constraints. This study applied three individual classification techniques (Discriminant analysis, Logit and Neural Networks) as base models to the corporate failure prediction context. Results of composite prediction were compared to the individual models. Preliminary results suggests that the use of integrated methods will offer improved performance in business classification problems.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.470-480
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• 2006

The longer the input sentences, the worse the parsing results. To improve the parsing performance, many methods about long sentence segmentation have been reserarched. As an isolating language, Chinese sentence has fewer cues for sentence segmentation. However, the average frequency of comma usage in Chinese is higher than that of other languages. The syntactic information that the comma conveys can play an important role in long sentence segmentation of Chinese languages. This paper proposes a method for classifying commas in Chinese sentences according to the context where the comma occurs. Then, sentences are segmented using the classification result. The experimental results show that the accuracy of the comma classification reaches 87.1%, and with our segmentation model, the dependency parsing accuracy of our parser is improved by 5.6%.

• Atmosphere
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• v.16 no.3
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• pp.235-246
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• 2006

Remote sensing techniques using satellites or the scanning weather radars depend mostly on the presence of clouds or precipitation, and leave the extensive regions of clear air unobserved. But wind profilers provide the most direct measurements of mesoscale vertical air motion in the troposphere, even in the context of heavy precipitation. In this paper, the precipitation events during the Changma period was classified into 4 precipitation types - stratiform, mixed stratiform/ convective, deep convective, and shallow convective. The parameters for the classification of analysis are the vertical structure of reflectivity, Doppler velocity, and spectral width measured with the wind profiler at Haenam for a three-year period (2003-2005). In addition, the synoptic fields and total amount of precipitation were analyzed using the Global Final Analyses (FNL) data and the Global Precipitation Climatology Project (GPCP) data. During the Changma period, the results show that the stratiform type was dominant under the moist-neutral atmosphere in 2003, whereas the deep convective type was under the moist unstable condition in 2004. The stratiform type was no less popular than the deep convective type among four seasons because the moist neutral layer was formed by the convergence between the upper-level jet and the low-level jet, and by the moisture transport along the western rim of the North Pacific subtropical anticyclone.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.35-42
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• 2021

In the current context of digitalization of education, the use of modern methods and techniques of data analysis and processing in order to improve students' school results has a very important role. In our paper, we aimed to perform a comparative study of the classification performances of AdaBoost, SVM, Naive Bayes, Neural Network and kNN algorithms to classify the results obtained at the Baccalaureate by students from a college in Suceava, during 2012-2019. To evaluate the results we used the metrics: AUC, CA, F₁, Precision and Recall. The AdaBoost algorithm achieves incredible performance for classifying the results into two categories: promoted / rejected. Next in terms of performance is Naive Bayes with a score of 0.999 for the AUC metric. The Neural Network and kNN algorithms obtain scores of 0.998 and 0.996 for AUC, respectively. SVM shows poorer performance with the score 0.987 for AUC. With the help of the HeatMap and DataTable visualization tools we identified possible correlations between classification results and some characteristics of data.

• Journal of Veterinary Science
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• v.21 no.1
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• pp.18.1-18.17
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• 2020

Cat vocalizes to communicate with another and express their internal states. The vocal repertoire of the cat is wide and up to 21 different vocalizations have been described in the literatures. But it is more than probable that the repertoire contains more types of vocalizations. An ethogram was created in this paper describing the actual known vocalisations of the domestic cat based on an auditory classification. However, the audiogram allows also a visual classification which can increase the accuracy of vocalization differentiation. The classification can be risky as it is sometimes unclear if different types of vocalizations are produced in different environments or if a unique type of vocalization is used with variation in the acoustic parameters. As an example, isolation calls produced by kittens differ depending on the context. The environment has an important impact on the vocal behaviour and thus feral cats and pet cats vocalize differently. Pet cats are thus able to create an efficient communication with humans thanks to the flexibility of vocalisation behaviours. This review allowed us to create a simple model of the cat vocal repertory.

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.96-101
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• 2022

As the elderly population gradually increases, the risk of fatal fall accidents among the elderly is increasing. One way to cope with a fall accident is to determine the fall direction before impact using a wearable inertial measurement unit (IMU). In this context, a previous study proposed a method of classifying fall directions using a support vector machine with sensor velocity, acceleration, and tilt angle as input parameters. However, in this method, the IMU signals are processed through several processes, including a Kalman filter and the integration of acceleration, which involves a large amount of computation and error factors. Therefore, this paper proposes a machine learning-based method that classifies the fall direction before impact using IMU raw signals rather than processed data. In this study, we investigated the effects of the following two factors on the classification performance: (1) the usage of processed/raw signals and (2) the selection of machine learning techniques. First, as a result of comparing the processed/raw signals, the difference in sensitivities between the two methods was within 5%, indicating an equivalent level of classification performance. Second, as a result of comparing six machine learning techniques, K-nearest neighbor and naive Bayes exhibited excellent performance with a sensitivity of 86.0% and 84.1%, respectively.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.80-88
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• 2017

Although hazard identification is one of the most important steps of safety management process, numerous hazards remain unidentified in the construction workplace due to the dynamic environment of the construction site and the lack of available resource for visual inspection. To this end, our previous study proposed the collective sensing approach for safety hazard identification and showed the feasibility of identifying hazards by capturing collective abnormalities in workers' walking patterns. However, workers generally performed different activities during the construction task in the workplace. Thereby, an additional process that can identify the worker's walking activity is necessary to utilize the proposed hazard identification approach in real world settings. In this context, this study investigated the feasibility of identifying walking activities during construction task using Wearable Inertial Measurement Units (WIMU) attached to the worker's ankle. This study simulated the indoor masonry work for data collection and investigated the classification performance with three different machine learning algorithms (i.e., Decision Tree, Neural Network, and Support Vector Machine). The analysis results showed the feasibility of identifying worker's activities including walking activity using an ankle-attached WIMU. Moreover, the finding of this study will help to enhance the performance of activity recognition and hazard identification in construction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.11-18
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• 2020

This study aims to complement the poor performance of existing context-aware authentication techniques in the mobile environment. The data used are GPS, Call Detail Record(CDR) and app usage. locational classification according to GPS density was implemented in order to distinguish other people in populated areas in the processing of GPS. It also handles missing values that may occur in data collection. The authentication model consists of two long-short term memory(LSTM) and one Artificial Neural Network(ANN) that aggregates the results, which produces authentication scores. In this paper, we compare the accuracy of this technique with that of other studies. Then compare the number of authentication attempts required to detect someone else's authentication. As a result, we achieved an average 11.6% improvement in accuracy and faster detection of approximately 60% of the experimental data.

• Journal of Digital Convergence
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• v.10 no.4
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• pp.341-346
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• 2012

Social network service is service that enables the human network to be built up on web. It is important to record users' information simply and establish the network with people based on the information to provide with the social network service effectively. But it is very troublesome work for the user to input his or her own information on the mobile environment. In this paper we suggested a system which classifies users' behavior using context and creates blogging sentences automatically after inferring the destination. For this, users' behavior is classified and the destination is inferred with the sequence matching method using Naive Bayes classification. Then sentences which are suitable for situation is created by arranging the processed context using the structure of 5W1H. The system was evaluated satisfaction degree by comparing the created sentences based on actually collected data with users' intension and got accuracy rate of 88.73%.